statistical analysis of market research

Statistical Analysis Methods for Market Research

Statistical analysis will take market research to the next level, in this article…, introduction.

• What is statistical analysis?
• Statistical analysis methods
• Benefits of Using Statistical Analysis

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Primary market research allows organizations to collect information from target markets by employing traditional quantitative and qualitative techniques.

However, acquiring data is not the only factor for conducting effective market research as data is more widely available thanks to the power of technology and the internet and online panels .

Modern advancements make it easier for businesses across all industries to monitor customer and market sentiments while collecting large quantities of data.

The power of data is not inherent as a data set is only as good as the analysis and insights. The right analysis methods can assist your business in extracting key information and trends from a pool of random data points that analysts can use to set short and long-term growth strategies.

What is Statistical Analysis?

Statistical analysis is a quantitative data analysis method that uses numbers to assign a measurability factor that is easy to compare and interpret. Under statistical analysis, the raw data is collected and analyzed to identify any patterns and trends which can be used for informed decision making.

The process of using statistics for market research involves:

• Defining the type of data to be extracted from the target population
• Exploring the relationship of the data with the population set
• Developing a model that summarizes insights and defines any visible links between the data set and the population
• Testing the model to establish the validity of the model
• Incorporating the results into your business strategy by anticipating future trends

Statistical Analysis Methods

There are two main statistical analysis methods commonly used for market research purposes: descriptive and inferential statistics. Both methods have different goals and applications, making them suitable for evaluating different data sets.

Descriptive Statistics

Descriptive statistics provide insight into the data collected, but they do not draw any conclusions about the larger population the data sample is extracted from. This method essentially describes a sample by summarizing and graphing data.

Conducting market research with descriptive statistics can help organizations understand the basic features of any set of quantifiable data by grouping data and identifying any patterns or trends.

This method is relatively simple as it involves basic mathematic calculations and data aggregation to yield important figures to evaluate historical business practices and their effectiveness. Some common descriptive statistical analysis methods include:

This involves mathematical functions, including counting, percentage calculation, and frequency occurrences.

Measures of frequency are used to primarily count the number of times a specific variable, event, or number appears in a data set.

It is used to establish how often a response occurs in the sample.

This describes the central positions of a distribution for a given data set.

It is used to display the average responses by analyzing the frequency of the sample data points and expressing it using the mean, median, and mode.

The central tendency measure identifies the most common trends or shared characteristics in the sample data.

Inferential Statistics

Inferential statistics use insights and measurements derived from the sample set and extrapolates the results to a larger set.

This method is primarily used to draw conclusions from an experiment sample and generalize the points to a relevant population.

An underlying assumption of this method is that the sample size is an accurate representation of the population which requires us to identify the population, include relevant sampling techniques to extract the sample set, and have some built-in safeguards to account for sampling errors.

While this method is more complicated than descriptive statistics, it provides richer numerical data for future business strategies. Some common inference statistical analysis methods include:

This is used to establish the underlying structure of a larger set of correlated variables.

The purpose is to condense information contained in multiple original valuables into a smaller set with composite dimensions while ensuring there is minimum loss of information.

Simply stated we are reducing data by making one variable, which is easy to manage by representing a set of observed variables (typically semantic differential scales ) in terms of common factors which can explain correlation that can be applied to a larger population.

This is used to distinguish how market research respondents make complicated purchasing decisions that include perceiving and evaluating different variables related to a product or service.

Conjoint analysis requires respondents to evaluate the tradeoffs involved with different factors such as prices, branding, etc., and identify their bearing on purchase consideration to evaluate the decision-making criteria for customers.

This technique is used to evaluate patterns, trends, relationships, and probabilities by grouping variables to understand correlations between different variables involved in the sample data.

This method identifies relationships that might not be readily apparent by placing the variables next to each other in a two-dimensional table which provides a unique perspective and outlook beneficial for gauging insights.

The Totally Unduplicated Reach and Frequency Analysis is used to rank and optimize product combinations and while fine-tuning our communication strategies by analyzing the reach of communication sources and frequency.

TURF Analysis allows you to evaluate estimates of media and market potential in devising optimal communication and placement strategies.

It identifies the number of users reached by each communication method and how often they are reached so you can have a stronger grip on market sentiments.

This method provides an in-depth study into the relationship between two or more variables from a data set and their application to the overall population pool.

This can help businesses make predictions about future behavior by establishing a causal or dependency relationship that can be positive or negative.

The intensity is measured by a higher numeric value on a scale ranging from -1 to +1.

This is a commonly used method for predicting the strength of a relationship between two or more variables.

To run a regression analysis, you need to have a dependent variable whose variation is dependent on another variable and independent variables which are controlled by the experimenter, and its variation is not dependent on any other variable.

In the analysis, the impact of independent variables on the dependent variable is evaluated to understand which variables have a greater impact.

Speaking hypothesis testing is another way to derive conclusions about a population by testing representative sample sets against experimenter-defined expectations or hypotheses.

The hypothesis can establish relationships between variables or provide insights about population properties such as mean and variation through T-Test, Chi-Square, and ANOVA tests.

This method makes it easy to draw suitable conclusions when it is impossible to test the entire population.

However, this method requires sophisticated sampling techniques to ensure the sample is representative of the population.

Benefits of Using Statistical Analysis in Market Research

Statistical analysis methods can provide worthwhile benefits to facilitate market research processes by:

• Producing theories backed by numerical evidence . The quantitative nature of statistical analysis provides a solid numeric framework to provide objective support for relationships between variables and hypotheses. These statistics make it easier for organizations to make well thought out decisions to serve customers better and provide relevant products and services to align with long-term goals and a positive impact on productivity.
• Yielding data that is easily calculated and analyzed . The precision offered by numbers and percentages can provide answers that can be analyzed by performing arithmetic functions. They do not need to be coded, unlike qualitative data, to improve understandability. This cuts down on data processing time while producing relevant results.
• Yielding a larger respondent pool . Before implementing any statistical analysis techniques, you need to have a data set. It is important to note that the data set for quantitative research is typically larger than that of qualitative research because it consists of close-ended questions, which are less time consuming, thereby encouraging more people to complete the survey or questionnaire. The larger data set allows you to have a more accurate sample representing the population which provides greater credibility to any results derived from the analysis

Jim Whaley  is a business leader, market research expert, and writer. He posts frequently on  The Standard Ovation  and other industry blogs.

OvationMR is a global provider of first-party data  for those seeking solutions that require information for informed business decisions.

OvationMR is a leader in delivering insights  and reliable results across a variety of industry sectors around the globe consistently for market research professionals and management consultants.

Visit: https://www.ovationmr.com .

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Statistical analysis: The Role of Statistical Analysis in Market Research

1. understanding the significance of statistical analysis in market research, 2. types of statistical analysis techniques in market research, 3. ensuring representativeness in statistical analysis, 4. summarizing and interpreting market data, 5. drawing conclusions and making predictions in market research, 6. assessing the significance of market research findings, 7. uncovering relationships and patterns in market data, 8. enhancing market research insights through graphs and charts, 9. emerging trends and technologies.

In today's competitive business landscape , market research plays a crucial role in understanding consumer behavior , identifying market trends, and making informed business decisions. Within the realm of market research , statistical analysis is an indispensable tool that allows researchers to interpret data, draw meaningful insights , and make accurate predictions. By employing statistical techniques, businesses can effectively assess market potential , evaluate the success of marketing campaigns , and gain a competitive edge . In this blog section, we will delve into the significance of statistical analysis in market research , exploring its key role, examples of its application, and tips for its effective use.

2. role of Statistical analysis in Market Research

Statistical analysis serves as the backbone of market research, providing a systematic approach to analyzing and interpreting data. It helps researchers identify patterns, relationships, and trends that may not be immediately apparent. By employing statistical techniques such as regression analysis, hypothesis testing, and correlation analysis, businesses can extract valuable insights from large datasets and make data-driven decisions .

For example, suppose a company wants to introduce a new product to the market. Through statistical analysis, they can conduct surveys, collect data on consumer preferences, and analyze the results to identify the target audience , determine pricing strategies, and forecast demand. Statistical analysis enables businesses to make informed decisions based on objective evidence rather than relying on intuition or guesswork.

3. Tips for Effective Statistical analysis in Market research

To ensure accurate and reliable results, it is essential to follow certain best practices when conducting statistical analysis in market research. Here are a few tips to keep in mind:

A) Define clear research objectives: Clearly define the research questions and objectives before collecting and analyzing data . This clarity will guide the selection of appropriate statistical techniques and ensure the analysis focuses on relevant insights.

B) ensure data quality : Garbage in, garbage out. It is crucial to collect high-quality data for statistical analysis. Ensure the data is accurate, complete, and relevant to the research objectives. Clean and validate the data to eliminate errors or outliers that may skew the results.

C) Choose the right statistical techniques: Familiarize yourself with a range of statistical techniques and select the most appropriate ones for your research objectives. Consider factors such as the type of data (categorical or continuous), the research question, and the level of precision required.

D) Interpret results in context: Statistical analysis provides numerical outputs, but it is important to interpret these results in the context of the research objectives. Avoid drawing conclusions solely based on statistical significance; consider the practical implications and real-world scenarios .

4. Case Studies: Real-World Applications of Statistical Analysis in Market Research

Let's explore a couple of case studies that highlight the practical applications of statistical analysis in market research:

A) Pricing optimization: A retail company wants to determine the optimal price for a new product . By conducting a conjoint analysis and employing regression analysis techniques , they can assess the impact of different pricing attributes (e.g., price, brand, features) on consumer preferences. Statistical analysis helps identify the price point that maximizes sales and profitability.

B) Customer segmentation: A telecommunications company wants to identify distinct customer segments based on their preferences and usage patterns. Through cluster analysis and other statistical techniques , they can group customers with similar characteristics together. This segmentation enables targeted marketing strategies , tailored product offerings, and improved customer satisfaction .

In conclusion, statistical analysis plays a critical role in market research , empowering businesses to make data-driven decisions, identify market trends , and gain a competitive edge. By understanding its significance, following best practices, and leveraging the appropriate statistical techniques, businesses can extract valuable insights from data and drive successful marketing strategies.

Understanding the Significance of Statistical Analysis in Market Research - Statistical analysis: The Role of Statistical Analysis in Market Research

In market research , statistical analysis plays a crucial role in extracting meaningful insights from data. By applying various statistical techniques, researchers can uncover patterns, relationships, and trends that can inform decision-making and strategy development. Here, we will explore some commonly used statistical analysis techniques in market research :

1. Descriptive Analysis: Descriptive analysis involves summarizing and presenting data in a meaningful way. It helps researchers understand the basic characteristics of a dataset, such as central tendency, variability, and distribution. For example, a market researcher may use descriptive analysis to determine the average age, income distribution, or buying behavior of a target market.

2. Inferential Analysis: Inferential analysis allows researchers to make inferences or draw conclusions about a population based on a sample. It involves hypothesis testing and estimation techniques to determine the statistical significance of relationships or differences. For instance, a market researcher might conduct inferential analysis to determine if there is a significant difference in customer satisfaction between two product variants.

3. regression analysis : Regression analysis is used to examine the relationship between a dependent variable and one or more independent variables. It helps researchers understand how changes in one variable affect another. For example, a market researcher might use regression analysis to determine the impact of advertising expenditure on sales revenue.

4. factor analysis : Factor analysis helps identify underlying factors or dimensions within a dataset. It is particularly useful in market research when dealing with a large number of variables. For instance, a market researcher might use factor analysis to group survey responses into distinct factors, such as price sensitivity, brand loyalty, and product quality.

5. cluster analysis : Cluster analysis is a technique used to group similar objects or individuals based on their characteristics. It helps researchers identify market segments or customer groups with similar preferences or behaviors. For instance, a market researcher might use cluster analysis to identify different customer segments based on their demographics, psychographics, or purchasing patterns.

6. conjoint analysis : Conjoint analysis is a technique used to understand how consumers make trade-offs between different product attributes . It helps researchers determine the relative importance of various attributes and optimize product design or pricing strategies. For example, a market researcher might use conjoint analysis to determine the ideal price range for a new product by analyzing how consumers value different price levels in combination with other attributes.

tips and Case studies :

- When conducting statistical analysis in market research, it is important to ensure data quality and integrity . This includes cleaning and validating the data to minimize errors and inconsistencies.

- Always consider the research objectives and design when selecting the appropriate statistical analysis technique. Different techniques are suitable for different research questions and data types.

- Case Study: A market research firm conducted a survey to understand consumer preferences for a new smartphone. They used factor analysis to identify underlying factors influencing smartphone purchase decisions. The analysis revealed three main factors: price, features, and brand reputation. This helped the company tailor their marketing strategy to target different customer segments based on these factors.

In conclusion, statistical analysis techniques in market research are essential for extracting valuable insights from data. Descriptive analysis, inferential analysis, regression analysis, factor analysis, cluster analysis, and conjoint analysis are just a few examples of the techniques used. By employing these techniques effectively, market researchers can make informed decisions and drive business success.

Types of Statistical Analysis Techniques in Market Research - Statistical analysis: The Role of Statistical Analysis in Market Research

Sampling is a crucial aspect of statistical analysis that involves selecting a subset of individuals or items from a larger population to gather data. The goal of sampling is to ensure that the selected sample is representative of the entire population, allowing for accurate statistical inferences. In this section, we will explore different sampling methods commonly used in market research and discuss their strengths, limitations, and practical applications.

1. simple Random sampling :

Simple random sampling is the most basic and straightforward method of sampling. It involves randomly selecting individuals from the population, where each member has an equal chance of being included in the sample. This method ensures representativeness and minimizes bias, making it suitable for a wide range of research studies. For example, in a market research survey , simple random sampling can be used to select participants by assigning a unique number to each potential respondent and using a random number generator to select the required sample size .

2. Stratified Sampling:

Stratified sampling involves dividing the population into distinct subgroups or strata based on certain characteristics, such as age, gender, or income level. The sample is then selected from each stratum in proportion to its representation in the population. This method allows for greater precision and accuracy by ensuring that each subgroup is adequately represented in the sample. For instance, if a market research study aims to analyze consumer preferences for a specific product across different age groups, stratified sampling can be used to ensure an adequate representation of each age group in the final sample.

3. Cluster Sampling:

Cluster sampling involves dividing the population into clusters or groups, often based on geographical proximity. A sample of clusters is then randomly selected, and data is collected from all individuals within the selected clusters. This method is particularly useful when the population is spread over a large area and it is impractical or costly to sample individuals from every location. For example, in a market research study targeting a specific region, cluster sampling can be employed by randomly selecting a few cities or towns and collecting data from all households within those selected clusters.

- Determine the appropriate sampling method based on the research objectives, available resources, and characteristics of the population.

- Consider the trade-off between representativeness and practicality. While simple random sampling ensures representativeness, it may not always be feasible due to time and cost constraints.

- Use randomization techniques, such as random number generators or random selection software, to ensure unbiased and fair sampling.

Case Study:

A company wants to conduct market research to understand consumer preferences for a new product . The target population consists of consumers across different age groups, income levels, and geographical locations. To ensure representativeness, the company decides to use a combination of stratified and cluster sampling . Firstly, they divide the population into strata based on age and income level. Then, within each stratum, they randomly select clusters of households from different geographical locations. Finally, they collect data from all individuals within the selected clusters. This sampling approach allows the company to obtain a diverse and representative sample, leading to more accurate insights about consumer preferences .

Sampling methods play a crucial role in statistical analysis, as they determine the quality and reliability of the data collected. By employing appropriate sampling techniques, market researchers can ensure that their findings accurately reflect the target population, enabling businesses to make informed decisions and strategies .

Ensuring Representativeness in Statistical Analysis - Statistical analysis: The Role of Statistical Analysis in Market Research

In market research, collecting data is just the first step. Once the data has been gathered, it needs to be analyzed and interpreted to gain meaningful insights. Descriptive statistics plays a crucial role in this process, as it allows us to summarize and understand the characteristics of the market data. In this section, we will explore the importance of descriptive statistics in market research and discuss various techniques and tools that can be used to interpret the collected data.

1. Mean, Median, and Mode:

One of the fundamental concepts in descriptive statistics is the calculation of measures of central tendency . The mean, median, and mode are commonly used to summarize numerical data. For example, if we have collected data on the prices of a product in the market, calculating the mean price will give us an average value, while the median will give us the middle value when the prices are arranged in ascending or descending order. The mode, on the other hand, represents the most frequently occurring price. These measures help us understand the typical or central value of the market data.

2. Variability and Spread:

Descriptive statistics also provide insights into the variability and spread of the market data. Measures such as the range, variance, and standard deviation help us understand how much the data points deviate from the mean. For instance, if we have collected data on customer satisfaction ratings, calculating the standard deviation will give us an idea of how diverse the ratings are. A higher standard deviation indicates a wider spread of ratings and vice versa. Understanding the variability of the market data helps in identifying patterns and trends .

3. Histograms and Frequency Distributions:

Histograms and frequency distributions are graphical representations that provide a visual summary of the market data. Histograms display the distribution of data points across various intervals or bins, allowing us to observe the shape, skewness, and outliers in the data. For example, if we have collected data on the ages of customers, a histogram can help us identify the age group that constitutes the majority of customers. Frequency distributions, on the other hand, provide a tabular summary of the data, indicating the number of occurrences or frequencies within specified intervals.

4. Case Study: Market Segmentation:

Descriptive statistics can be used in market segmentation analysis , where the goal is to divide a market into distinct groups based on certain characteristics. For example, a company may collect data on customer demographics , purchasing behavior, and preferences. descriptive statistics can help summarize this data by calculating the mean, median, or mode of specific variables for each segment. By analyzing these statistics, companies can gain insights into the different segments and tailor their marketing strategies accordingly.

- Always consider the context and purpose of the analysis when selecting appropriate descriptive statistics.

- Visualize the data using graphs, histograms, or charts to better understand the patterns and trends .

- Be mindful of outliers or extreme values that may skew the summary statistics and consider their impact on the interpretation.

Descriptive statistics is a valuable tool that helps researchers summarize and interpret market data effectively . By calculating measures of central tendency, understanding variability, and utilizing graphical representations, market researchers gain insights into the characteristics of the market and make informed decisions. Whether it's analyzing customer satisfaction ratings, market segmentation, or pricing data, descriptive statistics forms the foundation of a comprehensive market research analysis.

Summarizing and Interpreting Market Data - Statistical analysis: The Role of Statistical Analysis in Market Research

Inferential statistics plays a crucial role in market research by allowing us to draw conclusions and make predictions based on a sample of data. This branch of statistics helps us to make inferences about a larger population, providing valuable insights for decision-making and strategic planning . Let's delve into some key aspects of inferential statistics and how it is applied in market research.

1. understanding Confidence intervals :

Confidence intervals are a fundamental concept in inferential statistics. They represent a range of values within which we believe the true population parameter lies. For example, if we want to estimate the average income of a population, we can calculate a confidence interval to express our level of certainty. A 95% confidence interval means that if we were to repeat our sampling process multiple times, we would expect the true population parameter to fall within the calculated interval 95% of the time. This allows us to quantify the uncertainty associated with our estimates.

2. Hypothesis Testing:

Hypothesis testing helps us make decisions and draw conclusions about a population based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha). By collecting and analyzing data, we can determine whether the evidence supports rejecting the null hypothesis in favor of the alternative hypothesis . For instance, a market researcher might want to test whether a new advertising campaign has a significant impact on sales. By conducting hypothesis testing, they can determine if the observed increase in sales is statistically significant or simply due to chance.

3. Regression Analysis:

regression analysis allows us to explore relationships between variables and make predictions. Market researchers often use regression analysis to understand how different factors influence consumer behavior or market trends. For example, a researcher might want to predict the sales volume based on variables like price, advertising expenditure, and competitor activity. By fitting a regression model to the data , they can estimate the impact of each variable and make predictions about future sales under different scenarios.

4. Case Study: Customer Satisfaction Survey:

Suppose a company wants to assess the overall satisfaction level of its customers . They conduct a survey and collect responses from a random sample of customers. Using inferential statistics, the company can calculate the confidence interval for the proportion of satisfied customers in the entire population. This interval provides an estimate of the true proportion, along with the associated uncertainty. Additionally, hypothesis testing can be employed to determine if there is a significant difference in satisfaction levels between different customer segments, such as age groups or geographic regions.

5. Tips for Effective Use of Inferential Statistics in Market Research:

- Ensure the sample size is sufficiently large to obtain reliable results.

- Randomly select the sample to minimize bias and increase generalizability.

- Clearly define the population of interest to ensure accurate inferences.

- Use appropriate statistical tests and models based on the research question.

- Consider the assumptions underlying inferential statistics and validate them.

Inferential statistics empowers market researchers to go beyond the data they have and make meaningful conclusions and predictions about the larger population. By understanding confidence intervals, conducting hypothesis tests, utilizing regression analysis , and applying these techniques to real-world case studies , researchers can gain valuable insights to inform decision-making and drive business success .

Drawing Conclusions and Making Predictions in Market Research - Statistical analysis: The Role of Statistical Analysis in Market Research

hypothesis testing is a crucial step in statistical analysis that allows market researchers to assess the significance of their findings. By comparing observed data with a null hypothesis, researchers can determine whether their results are statistically significant or simply due to chance. This process helps to ensure that the conclusions drawn from market research are reliable and can be used to make informed business decisions . In this section, we will explore the concept of hypothesis testing in market research and provide examples, tips, and case studies to illustrate its importance and application.

1. Understanding Hypothesis Testing:

Hypothesis testing involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha) based on the research question. The null hypothesis assumes that there is no significant difference or relationship between variables , while the alternative hypothesis suggests otherwise. By collecting data and performing statistical tests, researchers can evaluate the likelihood of rejecting the null hypothesis in favor of the alternative hypothesis.

2. Example:

Suppose a market researcher wants to determine whether a new marketing campaign has led to a significant increase in sales. The null hypothesis would state that there is no difference in sales before and after the campaign, while the alternative hypothesis would suggest a significant increase. By collecting sales data before and after the campaign and conducting a statistical test, the researcher can assess the significance of the findings and determine whether they support the alternative hypothesis .

3. Tips for Effective Hypothesis Testing:

- Clearly define the research question and the variables involved.

- Choose the appropriate statistical test based on the nature of the data and research question.

- Determine the level of significance (alpha) to use, typically set at 0.05 or 0.01.

- Ensure that the sample size is sufficient to yield reliable results.

- Interpret the results in the context of the research question and consider the practical significance of the findings.

4. Case Study: A/B Testing in E-commerce:

A/B testing is a common application of hypothesis testing in market research , particularly in e-commerce. By randomly assigning users to different versions of a website or marketing campaign, researchers can assess the impact of changes on user behavior , such as click-through rates or conversion rates. Hypothesis testing is used to determine whether the observed differences in user behavior are statistically significant and can be attributed to the changes being tested.

In conclusion, hypothesis testing plays a vital role in assessing the significance of market research findings. It allows researchers to make evidence-based decisions by evaluating the likelihood of rejecting the null hypothesis in favor of the alternative hypothesis. By understanding the principles of hypothesis testing, utilizing appropriate statistical tests, and interpreting the results in the context of the research question, market researchers can ensure the reliability and validity of their findings.

Assessing the Significance of Market Research Findings - Statistical analysis: The Role of Statistical Analysis in Market Research

Regression analysis is a powerful statistical technique that plays a crucial role in market research . It allows us to uncover relationships and patterns within market data , helping businesses make informed decisions and develop effective strategies. By examining the interplay between variables, regression analysis provides valuable insights into how changes in one variable impact another. In this section, we will explore the fundamentals of regression analysis, its applications in market research , and some tips and case studies to illustrate its effectiveness.

1. Understanding Regression Analysis:

Regression analysis is based on the concept of the dependent and independent variables . The dependent variable is the one we want to predict or explain, while the independent variable(s) are the factors that influence or affect the dependent variable. For example, in market research, the dependent variable could be sales, and the independent variables could be factors like advertising expenditure, pricing, and customer demographics. By analyzing the relationship between these variables, we can gain insights into how they impact sales .

2. simple Linear regression :

Simple linear regression is the most basic form of regression analysis, where we examine the relationship between two variables. For instance, we could assess how changes in advertising expenditure impact sales. By fitting a line to the data points , we can determine the slope and intercept, which describe the relationship between the variables. This analysis helps us understand the strength and direction of the relationship , enabling us to make predictions or draw conclusions.

3. Multiple Regression:

Multiple regression expands upon simple linear regression by considering more than one independent variable. It allows us to analyze how multiple factors simultaneously influence the dependent variable. For example, a market researcher might explore how pricing, advertising expenditure, and customer satisfaction jointly impact sales. Multiple regression provides a more comprehensive understanding of the relationships between variables, enabling businesses to make more accurate predictions and optimize their strategies.

4. Tips for Conducting Regression Analysis:

To ensure accurate and meaningful results, it is important to follow some best practices when conducting regression analysis in market research . Here are a few tips to keep in mind:

- Ensure data quality: Clean and reliable data is essential for valid regression analysis. Remove outliers, check for missing values, and address any other data issues before conducting the analysis.

- Consider variable selection: Carefully select the independent variables based on their relevance and potential impact on the dependent variable. Including unnecessary variables can lead to overfitting and less reliable results.

- Check assumptions: Regression analysis relies on several assumptions, such as linearity, independence of errors, and normality. Validate these assumptions to ensure the reliability of your analysis.

5. Case Studies:

Let's take a look at a couple of case studies where regression analysis has proven invaluable in market research:

- Case Study 1: A retail company wants to understand the impact of online advertising on website visits. By conducting a regression analysis, they find a strong positive relationship between advertising expenditure and website visits. This insight allows them to allocate their advertising budget effectively and increase their online visibility .

- Case Study 2: A telecom company wants to determine the factors influencing customer churn. Through multiple regression analysis , they discover that factors such as call duration, customer complaints, and contract length significantly impact customer churn . Armed with this knowledge, they develop targeted retention strategies to reduce customer churn.

In conclusion, regression analysis is a powerful tool in market research that allows us to uncover relationships and patterns within market data. By examining the interplay between variables, we can gain valuable insights to enhance decision-making and strategy development. By following best practices and considering relevant case studies, businesses can harness the full potential of regression analysis to stay ahead in today's competitive market landscape.

Uncovering Relationships and Patterns in Market Data - Statistical analysis: The Role of Statistical Analysis in Market Research

Data visualization plays a crucial role in market research, as it allows analysts to effectively communicate complex information and uncover valuable insights . By presenting data in the form of graphs and charts, researchers can easily identify patterns, trends, and relationships that may not be apparent in raw data. In this section, we will explore the power of data visualization in enhancing market research insights and provide examples, tips, and case studies to demonstrate its effectiveness.

1. Visualizing trends and patterns:

One of the primary benefits of using graphs and charts in market research is the ability to visualize trends and patterns in data . For example, a line graph can be used to show the sales performance of different products over time, enabling analysts to identify growth or decline trends. Similarly, a scatter plot can reveal correlations between variables, helping researchers understand the relationships between different factors impacting consumer behavior.

2. comparing data sets :

Graphs and charts are also useful for comparing data sets . Bar charts, for instance, can be employed to compare market share of different brands or companies within a specific industry. By visually representing the data, analysts can quickly identify the dominant players and potential opportunities for growth.

3. Presenting survey results:

When conducting market research surveys , visualizing the results can be highly effective in conveying key findings. Pie charts and stacked bar graphs can be used to represent survey responses, making it easier for stakeholders to grasp the distribution of opinions or preferences. This visual representation can be particularly useful when presenting to clients or stakeholders who may not have a strong statistical background.

4. Tips for effective data visualization :

To maximize the impact of data visualization in market research , consider the following tips:

- Choose the right type of graph or chart for the data you want to present. Different types of visualizations are suitable for different purposes, so it's important to select the most appropriate one to convey your message effectively .

- Keep the design clean and uncluttered. Avoid unnecessary decorations or excessive data labels that may distract from the main insights you want to convey.

- Use color strategically to highlight important information or to differentiate between different categories or variables. However, be cautious not to overwhelm the audience with too many colors or conflicting color schemes.

- Provide clear and concise titles, labels, and legends to ensure that the audience understands the information being presented without confusion.

5. Case study: Visualizing consumer preferences:

Let's consider a case study where a market research firm wants to understand consumer preferences for different smartphone brands. By conducting a survey and visualizing the results using a stacked bar graph, they discover that brand A is the most preferred among the respondents, followed by brand B and brand C. This insight helps the firm advise their client, a smartphone manufacturer, on potential marketing strategies and product improvements.

In conclusion, data visualization is a powerful tool in market research that enhances insights by presenting complex information in a visually appealing and easily understandable format. By leveraging graphs and charts, analysts can identify trends, compare data sets, present survey results, and effectively communicate their findings to stakeholders. By following best practices and utilizing appropriate visualization techniques, market researchers can unlock valuable insights that drive informed decision-making .

Enhancing Market Research Insights through Graphs and Charts - Statistical analysis: The Role of Statistical Analysis in Market Research

The field of market research is constantly evolving, and with advancements in technology, the future of statistical analysis in this industry is set to witness significant changes. In this section, we will explore some of the emerging trends and technologies that are shaping the future of statistical analysis in market research.

1. Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are revolutionizing the way data is analyzed in market research. These technologies can process vast amounts of data at lightning-fast speeds, enabling researchers to uncover patterns, trends, and insights that were previously difficult to identify. For example, AI-powered algorithms can analyze social media data to understand consumer sentiment towards a particular brand or product, providing valuable insights for marketing strategies .

2. predictive analytics : Predictive analytics uses historical data and statistical algorithms to forecast future trends and behavior. Market researchers can leverage this technique to predict consumer preferences, market demand, and even identify potential risks or opportunities. For instance, a company can use predictive analytics to estimate the sales volume of a new product launch based on historical sales data, market trends, and other relevant factors.

3. Internet of Things (IoT): The IoT has opened up new avenues for collecting and analyzing data in market research . With the proliferation of connected devices, researchers can gather real-time data from various sources, such as wearable devices, smart appliances, and even in-store sensors. By analyzing this data, market researchers can gain deeper insights into consumer behavior and preferences , enabling businesses to tailor their offerings accordingly.

4. big Data analytics : The era of big data has brought about a paradigm shift in statistical analysis. Market researchers now have access to enormous volumes of structured and unstructured data, which can provide valuable insights when analyzed effectively. Advanced statistical techniques, such as data mining and text analytics, can help researchers uncover hidden patterns , correlations, and trends within this vast sea of data.

5. Visualization Tools: In the future, visualization tools will play a crucial role in statistical analysis for market research. These tools enable researchers to present complex data in a visually appealing and easily understandable manner. Infographics, interactive dashboards, and data visualization software can help researchers communicate their findings effectively to stakeholders and make data-driven decisions.

Case Study: Company XYZ, a global consumer goods company, used AI-powered sentiment analysis to analyze social media conversations about their brand . By identifying key themes and sentiments expressed by consumers, they were able to identify areas of improvement in their products and marketing campaigns , leading to increased customer satisfaction and brand loyalty .

Tip: stay updated with the latest advancements in statistical analysis tools and techniques. Continuous learning and upskilling will ensure that you can leverage the full potential of emerging technologies in market research.

The future of statistical analysis in market research is promising, with emerging trends and technologies revolutionizing the way data is collected, analyzed, and interpreted. As businesses strive to gain a competitive edge in today's fast-paced market , staying abreast of these developments will be crucial for success.

Emerging Trends and Technologies - Statistical analysis: The Role of Statistical Analysis in Market Research

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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How to Do Market Research: The Complete Guide

Learn how to do market research with this step-by-step guide, complete with templates, tools and real-world examples.

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Market research is the systematic process of gathering, analyzing and interpreting information about a specific market or industry.

What are your customers’ needs? How does your product compare to the competition? What are the emerging trends and opportunities in your industry? If these questions keep you up at night, it’s time to conduct market research.

Market research plays a pivotal role in your ability to stay competitive and relevant, helping you anticipate shifts in consumer behavior and industry dynamics. It involves gathering these insights using a wide range of techniques, from surveys and interviews to data analysis and observational studies.

In this guide, we’ll explore why market research is crucial, the various types of market research, the methods used in data collection, and how to effectively conduct market research to drive informed decision-making and success.

What is market research?

The purpose of market research is to offer valuable insight into the preferences and behaviors of your target audience, and anticipate shifts in market trends and the competitive landscape. This information helps you make data-driven decisions, develop effective strategies for your business, and maximize your chances of long-term growth.

Why is market research important? 

By understanding the significance of market research, you can make sure you’re asking the right questions and using the process to your advantage. Some of the benefits of market research include:

• Informed decision-making: Market research provides you with the data and insights you need to make smart decisions for your business. It helps you identify opportunities, assess risks and tailor your strategies to meet the demands of the market. Without market research, decisions are often based on assumptions or guesswork, leading to costly mistakes.
• Customer-centric approach: A cornerstone of market research involves developing a deep understanding of customer needs and preferences. This gives you valuable insights into your target audience, helping you develop products, services and marketing campaigns that resonate with your customers.
• Competitive advantage: By conducting market research, you’ll gain a competitive edge. You’ll be able to identify gaps in the market, analyze competitor strengths and weaknesses, and position your business strategically. This enables you to create unique value propositions, differentiate yourself from competitors, and seize opportunities that others may overlook.
• Risk mitigation: Market research helps you anticipate market shifts and potential challenges. By identifying threats early, you can proactively adjust their strategies to mitigate risks and respond effectively to changing circumstances. This proactive approach is particularly valuable in volatile industries.
• Resource optimization: Conducting market research allows organizations to allocate their time, money and resources more efficiently. It ensures that investments are made in areas with the highest potential return on investment, reducing wasted resources and improving overall business performance.
• Adaptation to market trends: Markets evolve rapidly, driven by technological advancements, cultural shifts and changing consumer attitudes. Market research ensures that you stay ahead of these trends and adapt your offerings accordingly so you can avoid becoming obsolete. 

As you can see, market research empowers businesses to make data-driven decisions, cater to customer needs, outperform competitors, mitigate risks, optimize resources and stay agile in a dynamic marketplace. These benefits make it a huge industry; the global market research services market is expected to grow from $76.37 billion in 2021 to $108.57 billion in 2026 . Now, let’s dig into the different types of market research that can help you achieve these benefits.

Types of market research 

• Qualitative research
• Quantitative research
• Exploratory research
• Descriptive research
• Causal research
• Cross-sectional research
• Longitudinal research

Despite its advantages, 23% of organizations don’t have a clear market research strategy. Part of developing a strategy involves choosing the right type of market research for your business goals. The most commonly used approaches include:

1. Qualitative research

Qualitative research focuses on understanding the underlying motivations, attitudes and perceptions of individuals or groups. It is typically conducted through techniques like in-depth interviews, focus groups and content analysis — methods we’ll discuss further in the sections below. Qualitative research provides rich, nuanced insights that can inform product development, marketing strategies and brand positioning.

2. Quantitative research

Quantitative research, in contrast to qualitative research, involves the collection and analysis of numerical data, often through surveys, experiments and structured questionnaires. This approach allows for statistical analysis and the measurement of trends, making it suitable for large-scale market studies and hypothesis testing. While it’s worthwhile using a mix of qualitative and quantitative research, most businesses prioritize the latter because it is scientific, measurable and easily replicated across different experiments.

3. Exploratory research

Whether you’re conducting qualitative or quantitative research or a mix of both, exploratory research is often the first step. Its primary goal is to help you understand a market or problem so you can gain insights and identify potential issues or opportunities. This type of market research is less structured and is typically conducted through open-ended interviews, focus groups or secondary data analysis. Exploratory research is valuable when entering new markets or exploring new product ideas.

4. Descriptive research

As its name implies, descriptive research seeks to describe a market, population or phenomenon in detail. It involves collecting and summarizing data to answer questions about audience demographics and behaviors, market size, and current trends. Surveys, observational studies and content analysis are common methods used in descriptive research. 

5. Causal research

Causal research aims to establish cause-and-effect relationships between variables. It investigates whether changes in one variable result in changes in another. Experimental designs, A/B testing and regression analysis are common causal research methods. This sheds light on how specific marketing strategies or product changes impact consumer behavior.

6. Cross-sectional research

Cross-sectional market research involves collecting data from a sample of the population at a single point in time. It is used to analyze differences, relationships or trends among various groups within a population. Cross-sectional studies are helpful for market segmentation, identifying target audiences and assessing market trends at a specific moment.

7. Longitudinal research

Longitudinal research, in contrast to cross-sectional research, collects data from the same subjects over an extended period. This allows for the analysis of trends, changes and developments over time. Longitudinal studies are useful for tracking long-term developments in consumer preferences, brand loyalty and market dynamics.

Each type of market research has its strengths and weaknesses, and the method you choose depends on your specific research goals and the depth of understanding you’re aiming to achieve. In the following sections, we’ll delve into primary and secondary research approaches and specific research methods.

Primary vs. secondary market research

Market research of all types can be broadly categorized into two main approaches: primary research and secondary research. By understanding the differences between these approaches, you can better determine the most appropriate research method for your specific goals.

Primary market research 

Primary research involves the collection of original data straight from the source. Typically, this involves communicating directly with your target audience — through surveys, interviews, focus groups and more — to gather information. Here are some key attributes of primary market research:

• Customized data: Primary research provides data that is tailored to your research needs. You design a custom research study and gather information specific to your goals.
• Up-to-date insights: Because primary research involves communicating with customers, the data you collect reflects the most current market conditions and consumer behaviors.
• Time-consuming and resource-intensive: Despite its advantages, primary research can be labor-intensive and costly, especially when dealing with large sample sizes or complex study designs. Whether you hire a market research consultant, agency or use an in-house team, primary research studies consume a large amount of resources and time.

Secondary market research 

Secondary research, on the other hand, involves analyzing data that has already been compiled by third-party sources, such as online research tools, databases, news sites, industry reports and academic studies.

Here are the main characteristics of secondary market research:

• Cost-effective: Secondary research is generally more cost-effective than primary research since it doesn’t require building a research plan from scratch. You and your team can look at databases, websites and publications on an ongoing basis, without needing to design a custom experiment or hire a consultant. 
• Leverages multiple sources: Data tools and software extract data from multiple places across the web, and then consolidate that information within a single platform. This means you’ll get a greater amount of data and a wider scope from secondary research.
• Quick to access: You can access a wide range of information rapidly — often in seconds — if you’re using online research tools and databases. Because of this, you can act on insights sooner, rather than taking the time to develop an experiment. 

So, when should you use primary vs. secondary research? In practice, many market research projects incorporate both primary and secondary research to take advantage of the strengths of each approach.

One rule of thumb is to focus on secondary research to obtain background information, market trends or industry benchmarks. It is especially valuable for conducting preliminary research, competitor analysis, or when time and budget constraints are tight. Then, if you still have knowledge gaps or need to answer specific questions unique to your business model, use primary research to create a custom experiment. 

Market research methods

• Surveys and questionnaires
• Focus groups
• Observational research
• Online research tools
• Experiments
• Content analysis
• Ethnographic research

How do primary and secondary research approaches translate into specific research methods? Let’s take a look at the different ways you can gather data: 

1. Surveys and questionnaires

Surveys and questionnaires are popular methods for collecting structured data from a large number of respondents. They involve a set of predetermined questions that participants answer. Surveys can be conducted through various channels, including online tools, telephone interviews and in-person or online questionnaires. They are useful for gathering quantitative data and assessing customer demographics, opinions, preferences and needs. On average, customer surveys have a 33% response rate , so keep that in mind as you consider your sample size.

2. Interviews

Interviews are in-depth conversations with individuals or groups to gather qualitative insights. They can be structured (with predefined questions) or unstructured (with open-ended discussions). Interviews are valuable for exploring complex topics, uncovering motivations and obtaining detailed feedback. 

3. Focus groups

The most common primary research methods are in-depth webcam interviews and focus groups. Focus groups are a small gathering of participants who discuss a specific topic or product under the guidance of a moderator. These discussions are valuable for primary market research because they reveal insights into consumer attitudes, perceptions and emotions. Focus groups are especially useful for idea generation, concept testing and understanding group dynamics within your target audience.

4. Observational research

Observational research involves observing and recording participant behavior in a natural setting. This method is particularly valuable when studying consumer behavior in physical spaces, such as retail stores or public places. In some types of observational research, participants are aware you’re watching them; in other cases, you discreetly watch consumers without their knowledge, as they use your product. Either way, observational research provides firsthand insights into how people interact with products or environments.

5. Online research tools

You and your team can do your own secondary market research using online tools. These tools include data prospecting platforms and databases, as well as online surveys, social media listening, web analytics and sentiment analysis platforms. They help you gather data from online sources, monitor industry trends, track competitors, understand consumer preferences and keep tabs on online behavior. We’ll talk more about choosing the right market research tools in the sections that follow.

6. Experiments

Market research experiments are controlled tests of variables to determine causal relationships. While experiments are often associated with scientific research, they are also used in market research to assess the impact of specific marketing strategies, product features, or pricing and packaging changes.

7. Content analysis

Content analysis involves the systematic examination of textual, visual or audio content to identify patterns, themes and trends. It’s commonly applied to customer reviews, social media posts and other forms of online content to analyze consumer opinions and sentiments.

8. Ethnographic research

Ethnographic research immerses researchers into the daily lives of consumers to understand their behavior and culture. This method is particularly valuable when studying niche markets or exploring the cultural context of consumer choices.

How to do market research

• Set clear objectives
• Identify your target audience
• Choose your research methods
• Use the right market research tools
• Collect data
• Analyze data 
• Interpret your findings
• Identify opportunities and challenges
• Make informed business decisions
• Monitor and adapt

Now that you have gained insights into the various market research methods at your disposal, let’s delve into the practical aspects of how to conduct market research effectively. Here’s a quick step-by-step overview, from defining objectives to monitoring market shifts.

1. Set clear objectives

When you set clear and specific goals, you’re essentially creating a compass to guide your research questions and methodology. Start by precisely defining what you want to achieve. Are you launching a new product and want to understand its viability in the market? Are you evaluating customer satisfaction with a product redesign? 

Start by creating SMART goals — objectives that are specific, measurable, achievable, relevant and time-bound. Not only will this clarify your research focus from the outset, but it will also help you track progress and benchmark your success throughout the process. 

You should also consult with key stakeholders and team members to ensure alignment on your research objectives before diving into data collecting. This will help you gain diverse perspectives and insights that will shape your research approach.

2. Identify your target audience

Next, you’ll need to pinpoint your target audience to determine who should be included in your research. Begin by creating detailed buyer personas or stakeholder profiles. Consider demographic factors like age, gender, income and location, but also delve into psychographics, such as interests, values and pain points.

The more specific your target audience, the more accurate and actionable your research will be. Additionally, segment your audience if your research objectives involve studying different groups, such as current customers and potential leads.

If you already have existing customers, you can also hold conversations with them to better understand your target market. From there, you can refine your buyer personas and tailor your research methods accordingly.

3. Choose your research methods

Selecting the right research methods is crucial for gathering high-quality data. Start by considering the nature of your research objectives. If you’re exploring consumer preferences, surveys and interviews can provide valuable insights. For in-depth understanding, focus groups or observational research might be suitable. Consider using a mix of quantitative and qualitative methods to gain a well-rounded perspective. 

You’ll also need to consider your budget. Think about what you can realistically achieve using the time and resources available to you. If you have a fairly generous budget, you may want to try a mix of primary and secondary research approaches. If you’re doing market research for a startup , on the other hand, chances are your budget is somewhat limited. If that’s the case, try addressing your goals with secondary research tools before investing time and effort in a primary research study. 

4. Use the right market research tools

Whether you’re conducting primary or secondary research, you’ll need to choose the right tools. These can help you do anything from sending surveys to customers to monitoring trends and analyzing data. Here are some examples of popular market research tools:

• Market research software: Crunchbase is a platform that provides best-in-class company data, making it valuable for market research on growing companies and industries. You can use Crunchbase to access trusted, first-party funding data, revenue data, news and firmographics, enabling you to monitor industry trends and understand customer needs.

• Survey and questionnaire tools: SurveyMonkey is a widely used online survey platform that allows you to create, distribute and analyze surveys. Google Forms is a free tool that lets you create surveys and collect responses through Google Drive.
• Data analysis software: Microsoft Excel and Google Sheets are useful for conducting statistical analyses. SPSS is a powerful statistical analysis software used for data processing, analysis and reporting.
• Social listening tools: Brandwatch is a social listening and analytics platform that helps you monitor social media conversations, track sentiment and analyze trends. Mention is a media monitoring tool that allows you to track mentions of your brand, competitors and keywords across various online sources.
• Data visualization platforms: Tableau is a data visualization tool that helps you create interactive and shareable dashboards and reports. Power BI by Microsoft is a business analytics tool for creating interactive visualizations and reports.

5. Collect data

There’s an infinite amount of data you could be collecting using these tools, so you’ll need to be intentional about going after the data that aligns with your research goals. Implement your chosen research methods, whether it’s distributing surveys, conducting interviews or pulling from secondary research platforms. Pay close attention to data quality and accuracy, and stick to a standardized process to streamline data capture and reduce errors. 

6. Analyze data

Once data is collected, you’ll need to analyze it systematically. Use statistical software or analysis tools to identify patterns, trends and correlations. For qualitative data, employ thematic analysis to extract common themes and insights. Visualize your findings with charts, graphs and tables to make complex data more understandable.

If you’re not proficient in data analysis, consider outsourcing or collaborating with a data analyst who can assist in processing and interpreting your data accurately.

7. Interpret your findings

Interpreting your market research findings involves understanding what the data means in the context of your objectives. Are there significant trends that uncover the answers to your initial research questions? Consider the implications of your findings on your business strategy. It’s essential to move beyond raw data and extract actionable insights that inform decision-making.

Hold a cross-functional meeting or workshop with relevant team members to collectively interpret the findings. Different perspectives can lead to more comprehensive insights and innovative solutions.

8. Identify opportunities and challenges

Use your research findings to identify potential growth opportunities and challenges within your market. What segments of your audience are underserved or overlooked? Are there emerging trends you can capitalize on? Conversely, what obstacles or competitors could hinder your progress?

Lay out this information in a clear and organized way by conducting a SWOT analysis, which stands for strengths, weaknesses, opportunities and threats. Jot down notes for each of these areas to provide a structured overview of gaps and hurdles in the market.

9. Make informed business decisions

Market research is only valuable if it leads to informed decisions for your company. Based on your insights, devise actionable strategies and initiatives that align with your research objectives. Whether it’s refining your product, targeting new customer segments or adjusting pricing, ensure your decisions are rooted in the data.

At this point, it’s also crucial to keep your team aligned and accountable. Create an action plan that outlines specific steps, responsibilities and timelines for implementing the recommendations derived from your research. 

10. Monitor and adapt

Market research isn’t a one-time activity; it’s an ongoing process. Continuously monitor market conditions, customer behaviors and industry trends. Set up mechanisms to collect real-time data and feedback. As you gather new information, be prepared to adapt your strategies and tactics accordingly. Regularly revisiting your research ensures your business remains agile and reflects changing market dynamics and consumer preferences.

Online market research sources

As you go through the steps above, you’ll want to turn to trusted, reputable sources to gather your data. Here’s a list to get you started:

• Crunchbase: As mentioned above, Crunchbase is an online platform with an extensive dataset, allowing you to access in-depth insights on market trends, consumer behavior and competitive analysis. You can also customize your search options to tailor your research to specific industries, geographic regions or customer personas.

• Academic databases: Academic databases, such as ProQuest and JSTOR , are treasure troves of scholarly research papers, studies and academic journals. They offer in-depth analyses of various subjects, including market trends, consumer preferences and industry-specific insights. Researchers can access a wealth of peer-reviewed publications to gain a deeper understanding of their research topics.
• Government and NGO databases: Government agencies, nongovernmental organizations and other institutions frequently maintain databases containing valuable economic, demographic and industry-related data. These sources offer credible statistics and reports on a wide range of topics, making them essential for market researchers. Examples include the U.S. Census Bureau , the Bureau of Labor Statistics and the Pew Research Center .
• Industry reports: Industry reports and market studies are comprehensive documents prepared by research firms, industry associations and consulting companies. They provide in-depth insights into specific markets, including market size, trends, competitive analysis and consumer behavior. You can find this information by looking at relevant industry association databases; examples include the American Marketing Association and the National Retail Federation .
• Social media and online communities: Social media platforms like LinkedIn or Twitter (X) , forums such as Reddit and Quora , and review platforms such as G2 can provide real-time insights into consumer sentiment, opinions and trends. 

Market research examples

At this point, you have market research tools and data sources — but how do you act on the data you gather? Let’s go over some real-world examples that illustrate the practical application of market research across various industries. These examples showcase how market research can lead to smart decision-making and successful business decisions.

Example 1: Apple’s iPhone launch

Apple ’s iconic iPhone launch in 2007 serves as a prime example of market research driving product innovation in tech. Before the iPhone’s release, Apple conducted extensive market research to understand consumer preferences, pain points and unmet needs in the mobile phone industry. This research led to the development of a touchscreen smartphone with a user-friendly interface, addressing consumer demands for a more intuitive and versatile device. The result was a revolutionary product that disrupted the market and redefined the smartphone industry.

Example 2: McDonald’s global expansion

McDonald’s successful global expansion strategy demonstrates the importance of market research when expanding into new territories. Before entering a new market, McDonald’s conducts thorough research to understand local tastes, preferences and cultural nuances. This research informs menu customization, marketing strategies and store design. For instance, in India, McDonald’s offers a menu tailored to local preferences, including vegetarian options. This market-specific approach has enabled McDonald’s to adapt and thrive in diverse global markets.

Example 3: Organic and sustainable farming

The shift toward organic and sustainable farming practices in the food industry is driven by market research that indicates increased consumer demand for healthier and environmentally friendly food options. As a result, food producers and retailers invest in sustainable sourcing and organic product lines — such as with these sustainable seafood startups — to align with this shift in consumer values. 

The bottom line? Market research has multiple use cases and is a critical practice for any industry. Whether it’s launching groundbreaking products, entering new markets or responding to changing consumer preferences, you can use market research to shape successful strategies and outcomes.

Market research templates

You finally have a strong understanding of how to do market research and apply it in the real world. Before we wrap up, here are some market research templates that you can use as a starting point for your projects:

• Smartsheet competitive analysis templates : These spreadsheets can serve as a framework for gathering information about the competitive landscape and obtaining valuable lessons to apply to your business strategy.
• SurveyMonkey product survey template : Customize the questions on this survey based on what you want to learn from your target customers.
• HubSpot templates : HubSpot offers a wide range of free templates you can use for market research, business planning and more.
• SCORE templates : SCORE is a nonprofit organization that provides templates for business plans, market analysis and financial projections.
• SBA.gov : The U.S. Small Business Administration offers templates for every aspect of your business, including market research, and is particularly valuable for new startups. 

Strengthen your business with market research

When conducted effectively, market research is like a guiding star. Equipped with the right tools and techniques, you can uncover valuable insights, stay competitive, foster innovation and navigate the complexities of your industry.

Throughout this guide, we’ve discussed the definition of market research, different research methods, and how to conduct it effectively. We’ve also explored various types of market research and shared practical insights and templates for getting started. 

Now, it’s time to start the research process. Trust in data, listen to the market and make informed decisions that guide your company toward lasting success.

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What is Statistical Analysis? Types, Methods, Software, Examples

Appinio Research · 29.02.2024 · 31min read

Ever wondered how we make sense of vast amounts of data to make informed decisions? Statistical analysis is the answer. In our data-driven world, statistical analysis serves as a powerful tool to uncover patterns, trends, and relationships hidden within data. From predicting sales trends to assessing the effectiveness of new treatments, statistical analysis empowers us to derive meaningful insights and drive evidence-based decision-making across various fields and industries. In this guide, we'll explore the fundamentals of statistical analysis, popular methods, software tools, practical examples, and best practices to help you harness the power of statistics effectively. Whether you're a novice or an experienced analyst, this guide will equip you with the knowledge and skills to navigate the world of statistical analysis with confidence.

What is Statistical Analysis?

Statistical analysis is a methodical process of collecting, analyzing, interpreting, and presenting data to uncover patterns, trends, and relationships. It involves applying statistical techniques and methodologies to make sense of complex data sets and draw meaningful conclusions.

Importance of Statistical Analysis

Statistical analysis plays a crucial role in various fields and industries due to its numerous benefits and applications:

• Informed Decision Making : Statistical analysis provides valuable insights that inform decision-making processes in business, healthcare, government, and academia. By analyzing data, organizations can identify trends, assess risks, and optimize strategies for better outcomes.
• Evidence-Based Research : Statistical analysis is fundamental to scientific research, enabling researchers to test hypotheses, draw conclusions, and validate theories using empirical evidence. It helps researchers quantify relationships, assess the significance of findings, and advance knowledge in their respective fields.
• Quality Improvement : In manufacturing and quality management, statistical analysis helps identify defects, improve processes, and enhance product quality. Techniques such as Six Sigma and Statistical Process Control (SPC) are used to monitor performance, reduce variation, and achieve quality objectives.
• Risk Assessment : In finance, insurance, and investment, statistical analysis is used for risk assessment and portfolio management. By analyzing historical data and market trends, analysts can quantify risks, forecast outcomes, and make informed decisions to mitigate financial risks.
• Predictive Modeling : Statistical analysis enables predictive modeling and forecasting in various domains, including sales forecasting, demand planning, and weather prediction. By analyzing historical data patterns, predictive models can anticipate future trends and outcomes with reasonable accuracy.
• Healthcare Decision Support : In healthcare, statistical analysis is integral to clinical research, epidemiology, and healthcare management. It helps healthcare professionals assess treatment effectiveness, analyze patient outcomes, and optimize resource allocation for improved patient care.

Statistical Analysis Applications

Statistical analysis finds applications across diverse domains and disciplines, including:

• Business and Economics : Market research , financial analysis, econometrics, and business intelligence.
• Healthcare and Medicine : Clinical trials, epidemiological studies, healthcare outcomes research, and disease surveillance.
• Social Sciences : Survey research, demographic analysis, psychology experiments, and public opinion polls.
• Engineering : Reliability analysis, quality control, process optimization, and product design.
• Environmental Science : Environmental monitoring, climate modeling, and ecological research.
• Education : Educational research, assessment, program evaluation, and learning analytics.
• Government and Public Policy : Policy analysis, program evaluation, census data analysis, and public administration.
• Technology and Data Science : Machine learning, artificial intelligence, data mining, and predictive analytics.

These applications demonstrate the versatility and significance of statistical analysis in addressing complex problems and informing decision-making across various sectors and disciplines.

Fundamentals of Statistics

Understanding the fundamentals of statistics is crucial for conducting meaningful analyses. Let's delve into some essential concepts that form the foundation of statistical analysis.

Basic Concepts

Statistics is the science of collecting, organizing, analyzing, and interpreting data to make informed decisions or conclusions. To embark on your statistical journey, familiarize yourself with these fundamental concepts:

• Population vs. Sample : A population comprises all the individuals or objects of interest in a study, while a sample is a subset of the population selected for analysis. Understanding the distinction between these two entities is vital, as statistical analyses often rely on samples to draw conclusions about populations.
• Independent Variables : Variables that are manipulated or controlled in an experiment.
• Dependent Variables : Variables that are observed or measured in response to changes in independent variables.
• Parameters vs. Statistics : Parameters are numerical measures that describe a population, whereas statistics are numerical measures that describe a sample. For instance, the population mean is denoted by μ (mu), while the sample mean is denoted by x̄ (x-bar).

Descriptive Statistics

Descriptive statistics involve methods for summarizing and describing the features of a dataset. These statistics provide insights into the central tendency, variability, and distribution of the data. Standard measures of descriptive statistics include:

• Mean : The arithmetic average of a set of values, calculated by summing all values and dividing by the number of observations.
• Median : The middle value in a sorted list of observations.
• Mode : The value that appears most frequently in a dataset.
• Range : The difference between the maximum and minimum values in a dataset.
• Variance : The average of the squared differences from the mean.
• Standard Deviation : The square root of the variance, providing a measure of the average distance of data points from the mean.
• Graphical Techniques : Graphical representations, including histograms, box plots, and scatter plots, offer visual insights into the distribution and relationships within a dataset. These visualizations aid in identifying patterns, outliers, and trends.

Inferential Statistics

Inferential statistics enable researchers to draw conclusions or make predictions about populations based on sample data. These methods allow for generalizations beyond the observed data. Fundamental techniques in inferential statistics include:

• Null Hypothesis (H0) : The hypothesis that there is no significant difference or relationship.
• Alternative Hypothesis (H1) : The hypothesis that there is a significant difference or relationship.
• Confidence Intervals : Confidence intervals provide a range of plausible values for a population parameter. They offer insights into the precision of sample estimates and the uncertainty associated with those estimates.
• Regression Analysis : Regression analysis examines the relationship between one or more independent variables and a dependent variable. It allows for the prediction of the dependent variable based on the values of the independent variables.
• Sampling Methods : Sampling methods, such as simple random sampling, stratified sampling, and cluster sampling , are employed to ensure that sample data are representative of the population of interest. These methods help mitigate biases and improve the generalizability of results.

Probability Distributions

Probability distributions describe the likelihood of different outcomes in a statistical experiment. Understanding these distributions is essential for modeling and analyzing random phenomena. Some common probability distributions include:

• Normal Distribution : The normal distribution, also known as the Gaussian distribution, is characterized by a symmetric, bell-shaped curve. Many natural phenomena follow this distribution, making it widely applicable in statistical analysis.
• Binomial Distribution : The binomial distribution describes the number of successes in a fixed number of independent Bernoulli trials. It is commonly used to model binary outcomes, such as success or failure, heads or tails.
• Poisson Distribution : The Poisson distribution models the number of events occurring in a fixed interval of time or space. It is often used to analyze rare or discrete events, such as the number of customer arrivals in a queue within a given time period.

Types of Statistical Analysis

Statistical analysis encompasses a diverse range of methods and approaches, each suited to different types of data and research questions. Understanding the various types of statistical analysis is essential for selecting the most appropriate technique for your analysis. Let's explore some common distinctions in statistical analysis methods.

Parametric vs. Non-parametric Analysis

Parametric and non-parametric analyses represent two broad categories of statistical methods, each with its own assumptions and applications.

• Parametric Analysis : Parametric methods assume that the data follow a specific probability distribution, often the normal distribution. These methods rely on estimating parameters (e.g., means, variances) from the data. Parametric tests typically provide more statistical power but require stricter assumptions. Examples of parametric tests include t-tests, ANOVA, and linear regression.
• Non-parametric Analysis : Non-parametric methods make fewer assumptions about the underlying distribution of the data. Instead of estimating parameters, non-parametric tests rely on ranks or other distribution-free techniques. Non-parametric tests are often used when data do not meet the assumptions of parametric tests or when dealing with ordinal or non-normal data. Examples of non-parametric tests include the Wilcoxon rank-sum test, Kruskal-Wallis test, and Spearman correlation.

Descriptive vs. Inferential Analysis

Descriptive and inferential analyses serve distinct purposes in statistical analysis, focusing on summarizing data and making inferences about populations, respectively.

• Descriptive Analysis : Descriptive statistics aim to describe and summarize the features of a dataset. These statistics provide insights into the central tendency, variability, and distribution of the data. Descriptive analysis techniques include measures of central tendency (e.g., mean, median, mode), measures of dispersion (e.g., variance, standard deviation), and graphical representations (e.g., histograms, box plots).
• Inferential Analysis : Inferential statistics involve making inferences or predictions about populations based on sample data. These methods allow researchers to generalize findings from the sample to the larger population. Inferential analysis techniques include hypothesis testing, confidence intervals, regression analysis, and sampling methods. These methods help researchers draw conclusions about population parameters, such as means, proportions, or correlations, based on sample data.

Exploratory vs. Confirmatory Analysis

Exploratory and confirmatory analyses represent two different approaches to data analysis, each serving distinct purposes in the research process.

• Exploratory Analysis : Exploratory data analysis (EDA) focuses on exploring data to discover patterns, relationships, and trends. EDA techniques involve visualizing data, identifying outliers, and generating hypotheses for further investigation. Exploratory analysis is particularly useful in the early stages of research when the goal is to gain insights and generate hypotheses rather than confirm specific hypotheses.
• Confirmatory Analysis : Confirmatory data analysis involves testing predefined hypotheses or theories based on prior knowledge or assumptions. Confirmatory analysis follows a structured approach, where hypotheses are tested using appropriate statistical methods. Confirmatory analysis is common in hypothesis-driven research, where the goal is to validate or refute specific hypotheses using empirical evidence. Techniques such as hypothesis testing, regression analysis, and experimental design are often employed in confirmatory analysis.

Methods of Statistical Analysis

Statistical analysis employs various methods to extract insights from data and make informed decisions. Let's explore some of the key methods used in statistical analysis and their applications.

Hypothesis Testing

Hypothesis testing is a fundamental concept in statistics, allowing researchers to make decisions about population parameters based on sample data. The process involves formulating null and alternative hypotheses, selecting an appropriate test statistic, determining the significance level, and interpreting the results. Standard hypothesis tests include:

• t-tests : Used to compare means between two groups.
• ANOVA (Analysis of Variance) : Extends the t-test to compare means across multiple groups.
• Chi-square test : Assessing the association between categorical variables.

Regression Analysis

Regression analysis explores the relationship between one or more independent variables and a dependent variable. It is widely used in predictive modeling and understanding the impact of variables on outcomes. Key types of regression analysis include:

• Simple Linear Regression : Examines the linear relationship between one independent variable and a dependent variable.
• Multiple Linear Regression : Extends simple linear regression to analyze the relationship between multiple independent variables and a dependent variable.
• Logistic Regression : Used for predicting binary outcomes or modeling probabilities.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups. It partitions the total variability in the data into components attributable to different sources, such as between-group differences and within-group variability. ANOVA is commonly used in experimental design and hypothesis testing scenarios.

Time Series Analysis

Time series analysis deals with analyzing data collected or recorded at successive time intervals. It helps identify patterns, trends, and seasonality in the data. Time series analysis techniques include:

• Trend Analysis : Identifying long-term trends or patterns in the data.
• Seasonal Decomposition : Separating the data into seasonal, trend, and residual components.
• Forecasting : Predicting future values based on historical data.

Survival Analysis

Survival analysis is used to analyze time-to-event data, such as time until death, failure, or occurrence of an event of interest. It is widely used in medical research, engineering, and social sciences to analyze survival probabilities and hazard rates over time.

Factor Analysis

Factor analysis is a statistical method used to identify underlying factors or latent variables that explain patterns of correlations among observed variables. It is commonly used in psychology, sociology, and market research to uncover underlying dimensions or constructs.

Cluster Analysis

Cluster analysis is a multivariate technique that groups similar objects or observations into clusters or segments based on their characteristics. It is widely used in market segmentation, image processing, and biological classification.

Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving most of the variability in the data. It identifies orthogonal axes (principal components) that capture the maximum variance in the data. PCA is useful for data visualization, feature selection, and data compression.

How to Choose the Right Statistical Analysis Method?

Selecting the appropriate statistical method is crucial for obtaining accurate and meaningful results from your data analysis.

Understanding Data Types and Distribution

Before choosing a statistical method, it's essential to understand the types of data you're working with and their distribution. Different statistical methods are suitable for different types of data:

• Continuous vs. Categorical Data : Determine whether your data are continuous (e.g., height, weight) or categorical (e.g., gender, race). Parametric methods such as t-tests and regression are typically used for continuous data , while non-parametric methods like chi-square tests are suitable for categorical data.
• Normality : Assess whether your data follows a normal distribution. Parametric methods often assume normality, so if your data are not normally distributed, non-parametric methods may be more appropriate.

Assessing Assumptions

Many statistical methods rely on certain assumptions about the data. Before applying a method, it's essential to assess whether these assumptions are met:

• Independence : Ensure that observations are independent of each other. Violations of independence assumptions can lead to biased results.
• Homogeneity of Variance : Verify that variances are approximately equal across groups, especially in ANOVA and regression analyses. Levene's test or Bartlett's test can be used to assess homogeneity of variance.
• Linearity : Check for linear relationships between variables, particularly in regression analysis. Residual plots can help diagnose violations of linearity assumptions.

Considering Research Objectives

Your research objectives should guide the selection of the appropriate statistical method.

• What are you trying to achieve with your analysis? : Determine whether you're interested in comparing groups, predicting outcomes, exploring relationships, or identifying patterns.
• What type of data are you analyzing? : Choose methods that are suitable for your data type and research questions.
• Are you testing specific hypotheses or exploring data for insights? : Confirmatory analyses involve testing predefined hypotheses, while exploratory analyses focus on discovering patterns or relationships in the data.

Consulting Statistical Experts

If you're unsure about the most appropriate statistical method for your analysis, don't hesitate to seek advice from statistical experts or consultants:

• Collaborate with Statisticians : Statisticians can provide valuable insights into the strengths and limitations of different statistical methods and help you select the most appropriate approach.
• Utilize Resources : Take advantage of online resources, forums, and statistical software documentation to learn about different methods and their applications.
• Peer Review : Consider seeking feedback from colleagues or peers familiar with statistical analysis to validate your approach and ensure rigor in your analysis.

By carefully considering these factors and consulting with experts when needed, you can confidently choose the suitable statistical method to address your research questions and obtain reliable results.

Statistical Analysis Software

Choosing the right software for statistical analysis is crucial for efficiently processing and interpreting your data. In addition to statistical analysis software, it's essential to consider tools for data collection, which lay the foundation for meaningful analysis.

What is Statistical Analysis Software?

Statistical software provides a range of tools and functionalities for data analysis, visualization, and interpretation. These software packages offer user-friendly interfaces and robust analytical capabilities, making them indispensable tools for researchers, analysts, and data scientists.

• Graphical User Interface (GUI) : Many statistical software packages offer intuitive GUIs that allow users to perform analyses using point-and-click interfaces. This makes statistical analysis accessible to users with varying levels of programming expertise.
• Scripting and Programming : Advanced users can leverage scripting and programming capabilities within statistical software to automate analyses, customize functions, and extend the software's functionality.
• Visualization : Statistical software often includes built-in visualization tools for creating charts, graphs, and plots to visualize data distributions, relationships, and trends.
• Data Management : These software packages provide features for importing, cleaning, and manipulating datasets, ensuring data integrity and consistency throughout the analysis process.

Popular Statistical Analysis Software

Several statistical software packages are widely used in various industries and research domains. Some of the most popular options include:

• R : R is a free, open-source programming language and software environment for statistical computing and graphics. It offers a vast ecosystem of packages for data manipulation, visualization, and analysis, making it a popular choice among statisticians and data scientists.
• Python : Python is a versatile programming language with robust libraries like NumPy, SciPy, and pandas for data analysis and scientific computing. Python's simplicity and flexibility make it an attractive option for statistical analysis, particularly for users with programming experience.
• SPSS : SPSS (Statistical Package for the Social Sciences) is a comprehensive statistical software package widely used in social science research, marketing, and healthcare. It offers a user-friendly interface and a wide range of statistical procedures for data analysis and reporting.
• SAS : SAS (Statistical Analysis System) is a powerful statistical software suite used for data management, advanced analytics, and predictive modeling. SAS is commonly employed in industries such as healthcare, finance, and government for data-driven decision-making.
• Stata : Stata is a statistical software package that provides tools for data analysis, manipulation, and visualization. It is popular in academic research, economics, and social sciences for its robust statistical capabilities and ease of use.
• MATLAB : MATLAB is a high-level programming language and environment for numerical computing and visualization. It offers built-in functions and toolboxes for statistical analysis, machine learning, and signal processing.

Data Collection Software

In addition to statistical analysis software, data collection software plays a crucial role in the research process. These tools facilitate data collection, management, and organization from various sources, ensuring data quality and reliability.

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How to Choose the Right Statistical Analysis Software?

When selecting software for statistical analysis and data collection, consider the following factors:

• Compatibility : Ensure the software is compatible with your operating system, hardware, and data formats.
• Usability : Choose software that aligns with your level of expertise and provides features that meet your analysis and data collection requirements.
• Integration : Consider whether the software integrates with other tools and platforms in your workflow, such as data visualization software or data storage systems.
• Cost and Licensing : Evaluate the cost of licensing or subscription fees, as well as any additional costs for training, support, or maintenance.

By carefully evaluating these factors and considering your specific analysis and data collection needs, you can select the right software tools to support your research objectives and drive meaningful insights from your data.

Statistical Analysis Examples

Understanding statistical analysis methods is best achieved through practical examples. Let's explore three examples that demonstrate the application of statistical techniques in real-world scenarios.

Example 1: Linear Regression

Scenario : A marketing analyst wants to understand the relationship between advertising spending and sales revenue for a product.

Data : The analyst collects data on monthly advertising expenditures (in dollars) and corresponding sales revenue (in dollars) over the past year.

Analysis : Using simple linear regression, the analyst fits a regression model to the data, where advertising spending is the independent variable (X) and sales revenue is the dependent variable (Y). The regression analysis estimates the linear relationship between advertising spending and sales revenue, allowing the analyst to predict sales based on advertising expenditures.

Result : The regression analysis reveals a statistically significant positive relationship between advertising spending and sales revenue. For every additional dollar spent on advertising, sales revenue increases by an estimated amount (slope coefficient). The analyst can use this information to optimize advertising budgets and forecast sales performance.

Example 2: Hypothesis Testing

Scenario : A pharmaceutical company develops a new drug intended to lower blood pressure. The company wants to determine whether the new drug is more effective than the existing standard treatment.

Data : The company conducts a randomized controlled trial (RCT) involving two groups of participants: one group receives the new drug, and the other receives the standard treatment. Blood pressure measurements are taken before and after the treatment period.

Analysis : The company uses hypothesis testing, specifically a two-sample t-test, to compare the mean reduction in blood pressure between the two groups. The null hypothesis (H0) states that there is no difference in the mean reduction in blood pressure between the two treatments, while the alternative hypothesis (H1) suggests that the new drug is more effective.

Result : The t-test results indicate a statistically significant difference in the mean reduction in blood pressure between the two groups. The company concludes that the new drug is more effective than the standard treatment in lowering blood pressure, based on the evidence from the RCT.

Example 3: ANOVA

Scenario : A researcher wants to compare the effectiveness of three different teaching methods on student performance in a mathematics course.

Data : The researcher conducts an experiment where students are randomly assigned to one of three groups: traditional lecture-based instruction, active learning, or flipped classroom. At the end of the semester, students' scores on a standardized math test are recorded.

Analysis : The researcher performs an analysis of variance (ANOVA) to compare the mean test scores across the three teaching methods. ANOVA assesses whether there are statistically significant differences in mean scores between the groups.

Result : The ANOVA results reveal a significant difference in mean test scores between the three teaching methods. Post-hoc tests, such as Tukey's HSD (Honestly Significant Difference), can be conducted to identify which specific teaching methods differ significantly from each other in terms of student performance.

These examples illustrate how statistical analysis techniques can be applied to address various research questions and make data-driven decisions in different fields. By understanding and applying these methods effectively, researchers and analysts can derive valuable insights from their data to inform decision-making and drive positive outcomes.

Statistical Analysis Best Practices

Statistical analysis is a powerful tool for extracting insights from data, but it's essential to follow best practices to ensure the validity, reliability, and interpretability of your results.

• Clearly Define Research Questions : Before conducting any analysis, clearly define your research questions or objectives . This ensures that your analysis is focused and aligned with the goals of your study.
• Choose Appropriate Methods : Select statistical methods suitable for your data type, research design , and objectives. Consider factors such as data distribution, sample size, and assumptions of the chosen method.
• Preprocess Data : Clean and preprocess your data to remove errors, outliers, and missing values. Data preprocessing steps may include data cleaning, normalization, and transformation to ensure data quality and consistency.
• Check Assumptions : Verify that the assumptions of the chosen statistical methods are met. Assumptions may include normality, homogeneity of variance, independence, and linearity. Conduct diagnostic tests or exploratory data analysis to assess assumptions.
• Transparent Reporting : Document your analysis procedures, including data preprocessing steps, statistical methods used, and any assumptions made. Transparent reporting enhances reproducibility and allows others to evaluate the validity of your findings.
• Consider Sample Size : Ensure that your sample size is sufficient to detect meaningful effects or relationships. Power analysis can help determine the minimum sample size required to achieve adequate statistical power.
• Interpret Results Cautiously : Interpret statistical results with caution and consider the broader context of your research. Be mindful of effect sizes, confidence intervals, and practical significance when interpreting findings.
• Validate Findings : Validate your findings through robustness checks, sensitivity analyses, or replication studies. Cross-validation and bootstrapping techniques can help assess the stability and generalizability of your results.
• Avoid P-Hacking and Data Dredging : Guard against p-hacking and data dredging by pre-registering hypotheses, conducting planned analyses, and avoiding selective reporting of results. Maintain transparency and integrity in your analysis process.

By following these best practices, you can conduct rigorous and reliable statistical analyses that yield meaningful insights and contribute to evidence-based decision-making in your field.

Conclusion for Statistical Analysis

Statistical analysis is a vital tool for making sense of data and guiding decision-making across diverse fields. By understanding the fundamentals of statistical analysis, including concepts like hypothesis testing, regression analysis, and data visualization, you gain the ability to extract valuable insights from complex datasets. Moreover, selecting the appropriate statistical methods, choosing the right software, and following best practices ensure the validity and reliability of your analyses. In today's data-driven world, the ability to conduct rigorous statistical analysis is a valuable skill that empowers individuals and organizations to make informed decisions and drive positive outcomes. Whether you're a researcher, analyst, or decision-maker, mastering statistical analysis opens doors to new opportunities for understanding the world around us and unlocking the potential of data to solve real-world problems.

How to Collect Data for Statistical Analysis in Minutes?

Introducing Appinio , your gateway to effortless data collection for statistical analysis. As a real-time market research platform, Appinio specializes in delivering instant consumer insights, empowering businesses to make swift, data-driven decisions.

With Appinio, conducting your own market research is not only feasible but also exhilarating. Here's why:

• Obtain insights in minutes, not days:  From posing questions to uncovering insights, Appinio accelerates the entire research process, ensuring rapid access to valuable data.
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• Targeted surveys, global reach:  Define your target audience with precision using our extensive array of demographic and psychographic characteristics, and reach respondents in over 90 countries effortlessly.

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Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education

Description

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Introduction

Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

From Idea to Insight: A 7-Step Market Research Guide

• by Alice Ananian
• September 4, 2024

In today’s fast-paced business world, guesswork is a luxury no one can afford. Enter market research: your secret weapon for making bold, informed decisions that propel your business forward. Whether you’re an ambitious entrepreneur, a savvy small business owner, or a cutting-edge marketing professional, mastering the market research process is the key to unlocking unprecedented growth and staying ahead of the competition.

Ready to transform raw data into golden opportunities? This guide will walk you through seven essential steps that turn the complex art of market research into a streamlined, powerful tool for success. From defining laser-focused objectives to leveraging cutting-edge AI analysis, you’re about to embark on a journey that will reshape how you understand your market, your customers, and your business potential.

The 7-Step Market Research Process: An Overview

Before diving into the details, let’s take a quick look at the seven steps that comprise an effective market research process:

• Define Your Research Objectives
• Develop Your Research Plan
• Collect Relevant Data
• Analyze and Interpret the Data
• Present Your Findings
• Make Informed Decisions
• Monitor and Iterate

Following this structured approach ensures that your market research is comprehensive, focused, and yields valuable insights. It’s worth noting that modern tools, such as AI-powered market research platforms like Prelaunch.com’s AI Market Research feature , can significantly streamline this process, making it more efficient and accessible for businesses of all sizes.

Now, let’s explore each step in detail.

Step 1: Define Your Research Objectives

The first and perhaps most crucial step in the market research process is defining your research objectives. This step sets the foundation for your entire research effort and ensures that you’re asking the right questions to get the information you need.

Identifying the problem or opportunity

Start by clearly articulating the business problem you’re trying to solve or the opportunity you’re looking to explore. Are you considering launching a new product? Trying to understand why sales are declining? Or perhaps you’re looking to enter a new market? Clearly defining the issue at hand will help focus your research efforts.

Setting clear, measurable goals

Once you’ve identified the problem or opportunity, set specific, measurable, achievable, relevant, and time-bound (SMART) goals for your research. For example, instead of a vague goal like “understand customer preferences,” you might set a goal to “identify the top three features that 70% of our target market considers essential in a new product within the next two months.”

Formulating research questions

Based on your goals, develop a set of research questions that will guide your data collection efforts. These questions should be specific and directly related to your objectives. For instance, if your goal is to understand customer preferences, you might ask questions like:

• What features do customers value most in similar products?
• How much are customers willing to pay for these features?
• What unmet needs exist in the current market?

By clearly defining your research objectives, you’ll ensure that your market research efforts are focused and yield the insights you need to make informed business decisions.

Step 2: Develop Your Research Plan

With your objectives clearly defined, the next step is to develop a comprehensive research plan. This plan will serve as your roadmap, outlining how you’ll gather the information needed to answer your research questions.

Choosing research methodologies

Decide whether qualitative research, quantitative research, or a combination of both will best serve your objectives:

• Qualitative research : This method explores the “why” and “how” of consumer behavior through in-depth interviews, focus groups, or observational studies. It’s excellent for gaining deep insights into customer motivations and perceptions.
• Quantitative research : This approach focuses on numerical data and statistical analysis. Surveys and polls are common quantitative methods that can provide measurable data on consumer preferences and behaviors.

Often, a mixed-method approach combining both qualitative and quantitative research can provide the most comprehensive insights.

Determining your target audience

Identify the specific group of people from whom you need to gather information. This could be based on demographics, psychographics, or behavioral characteristics. The more precisely you define your target audience, the more relevant and valuable your research findings will be.

Selecting appropriate data collection methods

Choose the most suitable methods for collecting data from your target audience. Some options include:

• Surveys (online, phone, or in-person)
• Interviews (structured or unstructured)
• Focus groups
• Observational studies
• Secondary data analysis

Consider factors such as cost, time constraints, and the type of information you need when selecting your methods. AI-powered tools like Prelaunch.com’s AI Market Research feature can be particularly helpful in this stage, offering efficient ways to gather and analyze data from various sources.

By developing a thorough research plan, you’ll ensure that your data collection efforts are efficient, targeted, and aligned with your research objectives.

Step 3: Collect Relevant Data

With your research plan in place, it’s time to gather the data that will form the basis of your insights. This step involves implementing the data collection methods you’ve chosen and ensuring that you’re gathering high-quality, relevant information.

Primary research methods

Primary research involves collecting original data directly from your target audience. This can include:

• Conducting surveys: Use online platforms, email, or in-person methods to gather quantitative data from a large sample of your target audience.
• Performing interviews: Engage in one-on-one conversations with key individuals to gain in-depth qualitative insights.
• Organizing focus groups : Bring together small groups of people to discuss your research topics in a moderated setting.
• Observational studies: Watch and record how people interact with products or services in real-world settings.

Secondary research sources

Secondary research involves analyzing existing data from various sources. This can be a cost-effective way to gather background information and supplement your primary research. Sources may include:

• Industry reports and market studies
• Government databases and publications
• Academic research papers
• Competitor websites and annual reports
• Trade association publications

Leveraging AI for efficient data collection

Modern AI-powered tools can significantly enhance your data collection efforts. These tools can:

• Automate the process of gathering and organizing secondary research data
• Analyze large datasets quickly to identify trends and patterns
• Generate survey questions based on your research objectives
• Provide real-time insights as data is collected

By leveraging both traditional methods and advanced AI tools, you can ensure that you’re collecting a comprehensive and diverse set of data to inform your market research.

Step 4: Analyze and Interpret the Data

Once you’ve collected your data, the next crucial step is to analyze and interpret it. This process involves transforming raw data into actionable insights that can guide your business decisions.

Data cleaning and preparation

Before analysis can begin, it’s essential to clean and prepare your data:

• Remove any duplicate or irrelevant entries
• Check for and correct any errors or inconsistencies
• Standardize data formats for easier analysis
• Organize data into a structure that facilitates analysis

Statistical analysis techniques

Depending on the type of data you’ve collected and your research objectives, you may employ various statistical analysis techniques :

• Descriptive statistics: Calculate means, medians, modes, and standard deviations to summarize your data.
• Inferential statistics: Use techniques like hypothesis testing and regression analysis to draw conclusions about larger populations based on your sample data.
• Correlation analysis: Identify relationships between different variables in your dataset.
• Segmentation analysis: Group your data into meaningful segments based on shared characteristics.

Identifying patterns and trends

As you analyze your data, look for patterns, trends, and insights that address your research objectives:

• Compare results across different demographic groups or market segments
• Identify common themes in qualitative data
• Look for unexpected or surprising findings that challenge your assumptions
• Consider how different data points relate to each other and what story they tell together

Remember that the goal of this step is not just to summarize data, but to derive meaningful insights that can inform your business strategy. Be open to unexpected findings and be prepared to dig deeper into areas that seem particularly relevant or intriguing.

Step 5: Present Your Findings

After analyzing your data, it’s time to communicate your findings effectively to stakeholders. The way you present your research can significantly impact how it’s received and acted upon.

Creating clear and visually appealing reports

• Organize your findings logically, starting with an executive summary of key insights
• Use charts, graphs, and infographics to visualize data and make it easier to understand
• Include relevant quotes or case studies from qualitative research to bring your data to life
• Ensure your report is well-structured with clear headings and subheadings

Tailoring presentations to different stakeholders

• Consider the specific interests and needs of your audience (e.g., executives, marketing team, product developers)
• Adjust the level of detail and technical language based on your audience’s expertise
• Focus on the findings most relevant to each stakeholder group

Highlighting key insights and actionable recommendations

• Clearly state the main takeaways from your research
• Connect your findings directly to your initial research objectives
• Provide specific, actionable recommendations based on your insights
• Include potential implications of your findings for different areas of the business

Remember, the goal is not just to share information, but to tell a compelling story with your data that motivates action and informs strategy.

Step 6: Make Informed Decisions

The true value of market research lies in its ability to inform better business decisions. This step is where you translate your research findings into strategic action.

Connecting research findings to business objectives

• Revisit your initial research objectives and evaluate how your findings address them
• Identify which insights are most critical for achieving your business goals
• Consider both the opportunities and potential risks highlighted by your research

Assessing risks and opportunities

• Use your research to evaluate the potential success of new products, services, or marketing strategies
• Identify potential obstacles or challenges that your research has uncovered
• Consider how your findings might impact different scenarios or future market conditions

Developing data-driven strategies

• Create action plans based on your research insights
• Set specific, measurable goals for implementing changes or new initiatives
• Assign responsibilities and timelines for acting on your research findings
• Ensure that all strategic decisions are directly supported by your research data

Remember that while your research should guide your decisions, it’s also important to balance data with experience, intuition, and other business considerations.

Step 7: Monitor and Iterate

The market research process doesn’t end with implementation. Continuous monitoring and iteration are crucial for long-term success.

Implementing decisions based on research

• Put your data-driven strategies into action
• Ensure that all team members understand the research findings and their role in implementing changes

Tracking results and KPIs

• Set up systems to monitor the impact of your decisions
• Track relevant key performance indicators (KPIs) that align with your research objectives
• Regularly review performance against your goals and expectations

Conducting follow-up research for continuous improvement

• Plan for periodic follow-up research to assess the effectiveness of your strategies
• Be prepared to adjust your approach based on new data and changing market conditions
• Consider implementing ongoing research methods, such as customer feedback loops or regular market surveys

By viewing market research as an ongoing process rather than a one-time event, you can ensure that your business remains agile and responsive to market changes.

Mastering the market research process is essential for making informed business decisions in today’s competitive landscape. By following these 7 steps – defining objectives, developing a plan, collecting data, analyzing results, presenting findings, making decisions, and monitoring outcomes – you can gain valuable insights that drive business growth and innovation.

As markets evolve and consumer preferences change, ongoing market research will be key to staying ahead. Embrace this process as a fundamental part of your business strategy, and you’ll be well-equipped to make decisions that resonate with your target audience and drive your business forward.

Alice Ananian

Alice has over 8 years experience as a strong communicator and creative thinker. She enjoys helping companies refine their branding, deepen their values, and reach their intended audiences through language.

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Data Analytics in Marketing Research: Definition, Types, Process, and More

Data Analytics is a critical function affecting all aspects of the business. This article covers broad data analytic topics for those new to the area of data analytics. At Sawtooth Software, we focus on marketing research and primary data collection through survey research, so this article specifically calls out the use of data analytics in marketing sciences.

Before diving deep into the breadth of data analytics, let’s summarize key takeaways you will gain from this guide:

With that introduction, let’s dive deeper into the field of Data Analytics .

Table of Contents

What Is Data Analytics?

At its core, Data Analytics involves the computational analysis of data or statistics. Data can involve numeric values, text, graphics, video or audio files. The value of data analytics lies in its ability to transform vast amounts of raw, often unstructured data into actionable insights. These insights can then guide decision-making, optimize operations, and unveil opportunities for innovation.

Consider a retail business that leverages data analytics to understand customer purchasing patterns, preferences, and behaviors. By analyzing sales data, customer feedback, social media trends, along with primary survey data, the business can tailor its product offerings, improve customer service, predict future trends, and optimize products and pricing for new or existing products. This practical application underscores the transformative power of data analytics in driving business strategy and growth.

Data Analytics vs. Data Science

While often used interchangeably, Data Analytics and Data Science involve nuanced differences, with complementary roles within an organization. Data Analytics focuses on processing and performing statistical analysis on existing datasets. In contrast, Data Science typically involves heavier programming, developing algorithms, and model-building to derive additional insights to solve complex problems and predict future outcomes. Data scientists often leverage machine learning and AI (Artificial Intelligence) in building algorithms, models, and applications.

The impact of both fields on Decision-Making is important. Data analytics provides a more immediate, focused insight primarily aimed at enhancing operational efficiency and answering specific questions. Data Science, on the other hand, dives deeper into predictive analysis, machine learning, and AI to forecast future trends and behaviors.

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Types of Data Analysis

Data Analysis can be broadly categorized into four main types, each serving a unique purpose in the data analytics landscape. Understanding these types helps you to apply the right analytical approach to your data to derive meaningful conclusions and strategies.

Descriptive Analytics

This type of analytics focuses on the “what” and is the most basic and commonly used. For market research surveys, descriptive analytics summarizes responses to demographic, psychographic, attitudinal, brand usage data, and the like. For historical data, it aims to provide a clear picture of what has happened in the past by summarizing such things as sales data, operations data, advertising data, and website click traffic. Descriptive analytics answers the "What happened?" question by analyzing key performance indicators (KPIs) and metrics. For example, a business might use descriptive analytics to understand its sales trends, customer engagement levels, or production efficiencies over the past year.

Diagnostic Analytics

Moving beyond the “what” to understand the “why,” diagnostic analytics involves a deeper dive into data to examine patterns of association or correlation, with the hope to uncover root causes of attitudes, preference, events or trends. It employs techniques such as correlation analysis, t-tests, chi-square tests, key drivers analysis, and tree-based analysis (such as CART or random forests). For customer satisfaction research key drivers analysis tries to explain how overall customer satisfaction or loyalty can be improved by improving the features or elements of the product or service delivery. An organization might also leverage diagnostic analytics to identify why certain groups of respondents are more likely to be price sensitive or why customer churn increased in a specific period.

Predictive Analytics

This forward-looking analysis leverages data and models that can predict future outcomes. Conjoint analysis is a widely used predictive analytics approach for studying how changes to product features and prices affect demand. MaxDiff (best-worst scaling) is often used to assess which product claims will likely increase new product trial, or which side effects would most discourage patients from undergoing a cancer treatment therapy. Machine learning algorithms such as random forests can score a database to predict which customers are most likely to be receptive to an offer. As another example, a financial institution might use predictive analytics to assess the risk of loan default based on a customer's credit history, transaction data, and market conditions.

Prescriptive Analytics

An advanced form of analytics, prescriptive analytics, goes a step further by recommending actions you can take to affect desired outcomes. It not only predicts what will happen but also suggests various courses of action and the potential implications of each. This type of analytics is particularly valuable in complex decision-making environments. For example, a conjoint analysis market simulator leveraging optimization search routines can determine the right mix of product features and price to reach a particularly valuable market segment .

Each of these types of data analysis plays a critical role in an organization's data-driven decision-making process, enabling businesses to understand their past performance, diagnose issues, create successful products and services, predict future trends, and make informed choices that align with their strategic objectives.

Data Analytics Real-World Example

Consider the case of a data analyst working for an e-commerce platform. By analyzing customer purchase history, the analyst identifies a trend of increased sales in eco-friendly products ( descriptive analytics , the “what”). A survey is designed and conducted to dig deeper into which customers are preferring eco-friendly products, why they prefer them, and for which usage occasions ( diagnostic analytics , the “why”). Within another market research survey, a conjoint analysis or MaxDiff study is included for determining the right product claims, product features, and pricing, targeted to which market segments to develop new products for sales growth ( predictive and prescriptive analytics ).

The role of a data analyst is dynamic and impactful, bridging the gap between data and strategic decision-making. It's a role that requires not only technical skills but also curiosity, creativity, and a keen understanding of the business landscape.

The Data Analysis Process

Breaking down a data analytics process into systematic steps can demystify the journey, making it more approachable and manageable. The Data Analysis Process is a structured approach that guides data analysts from the initial phase of understanding the business problem to the final stage of delivering actionable insights.

Step 1: Defining the Question

The first and perhaps most critical step in the data analysis process is defining the question . This involves understanding the business objectives, the decisions that need to be supported by the data, and the specific questions that the analysis aims to answer. A well-defined question not only provides direction for the analysis but also ensures that the outcomes are relevant and actionable.

Step 2: Collecting Clean Data

Data collection is the next step, where data analysts gather the necessary data from various sources. This could include internal databases, secondary sources of data, customer surveys, and more. Ensuring the cleanliness of the data is paramount at this stage; hence, data cleaning and preprocessing become essential tasks. This involves removing inaccuracies, inconsistencies, handling missing values, and trimming outliers to ensure the data is reliable and accurate for analysis. For market research surveys, this also involves identifying unreliable respondents, fraudulent respondents, and records completed by survey bots.

Step 3: Data Analysis and Interpretation

With clean data in hand, analysts proceed to the heart of the process: data analysis and interpretation . This involves applying statistical methods and analytical models to the data to identify patterns, trends, and correlations. The choice of techniques varies depending on the data and the questions at hand, ranging from simple descriptive statistics to complex predictive models.

Step 4: Data Visualization and Sharing Findings

Data visualization plays a crucial role in this phase, as it transforms complex data sets into visual representations that are easier to understand and interpret. Tools like charts, graphs, and dashboards are used to illustrate the findings compellingly and intuitively.

Finally, sharing the findings with stakeholders is an integral part of the data analysis process. This involves not just presenting the data, but also providing insights, recommendations, and potential implications in a clear and persuasive manner. Effective communication is key here, as the ultimate goal is to inform decision-making and drive action based on the data insights.

For product optimization and pricing research, market simulators from conjoint analysis can be even more useful to a decision-maker than charts and graphs. They allow the manager to test thousands of potential product formulations and prices, to find the right products to best reach target market segments.

Example Scenario

Imagine a data analyst working for a healthcare provider, tasked with reducing patient wait times. By following the data analysis process, the analyst:

• Defines the question: What factors contribute to increased wait times?
• Collects and cleans data from patient records, appointment systems, and feedback surveys.
• Analyzes the data to identify patterns, such as peak times for appointments and common delays in the patient check-in process.
• Visualizes the findings using graphs that highlight peak congestion times and the factors causing delays.
• Shares the insights with the healthcare management team, recommending adjustments to appointment scheduling and check-in processes to reduce wait times.

This systematic approach not only provides actionable insights but also showcases the power of data analytics in solving real-world problems.

Understanding the data analysis process is foundational for anyone looking to delve into data analytics, providing a roadmap for transforming data into insights that can drive informed decision-making.

Tools and Techniques

The field of Data Analytics is supported by a variety of tools and techniques designed to extract, analyze, and interpret data. Market research surveys are often a key source of data. The choice of the right analytics tools and the application of specific analytical techniques can significantly impact the quality of the insights generated. In this section, we will explore some of the key data analytics techniques and highlight commonly used tools, especially for primary survey research, providing tips on how to choose the right ones for specific projects.

Key Data Analytics Techniques

Statistical Testing: When summarizing data using means (for continuous data) or percent of observations falling into different categories (for categorical or nominal data), we often want to know whether the differences we’re observing between groups of respondents, branches of a company, or time periods are statistically meaningful (that they were unlikely to occur by chance).

Correlation Analysis : A statistical approach that examines whether there is a positive, negative, or no correlation between two continuous variables. The square of the correlation coefficient indicates the percent of variance in one variable that is explained by the other.

Regression Analysis : A statistical method used to examine the relationship between dependent (outcome) and independent (predictor) variables. There are regression techniques for predicting continuous variables (ordinary least squares) as well as for categorical outcomes (logistic regression). Regression analysis is particularly useful for identifying relationships between variables, making predictions, and forecasting.

Tree-Based Analysis : These techniques are used for finding which variables tend to predict or explain some outcome, such as purchase of a product, or diagnosis with a disease. Common examples are Classification and Regression Trees (CART) and Random Forests, a combination of multiple trees that can be ensembled for a more accurate consensus prediction.

Time-Series Analysis : Focused on analyzing data points collected or recorded at specific time intervals. This technique is crucial for trend analysis, seasonal pattern identification, and forecasting.

Cluster Analysis : A family of methods used to group a set of objects (such as respondents) in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It’s extensively used in market segmentation and targeting strategies. Common approaches include k-means clustering, latent class clustering, and ensemble approaches that leverage multiple techniques to achieve a more robust consensus solution.

Conjoint Analysis and MaxDiff: Discrete choice methods often used in market research and economics for assessing the importance of features, measuring price sensitivity , and predicting demand for products or services.

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Commonly Used Data Analytics Tools

Excel : A versatile tool for basic data analysis, familiar to most professionals, capable of handling various data analysis functions including pivot tables, basic statistical functions, and data visualization.

SQL : Essential for data extraction, especially from relational databases. SQL allows analysts to query specific data from large databases efficiently.

Python/R : Both are powerful programming languages favored in data analytics for their libraries and packages that support data manipulation, statistical analysis, and machine learning.

Tableau/Power BI : These tools are leaders in data visualization, providing robust platforms for creating dynamic and interactive dashboards and reports.

Sawtooth Software : Provides tools, support services, and consulting services for designing and fielding market research surveys, as well as conducting conjoint analysis, MaxDiff, and cluster analysis.

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Choosing the Right Tools and Techniques

Selecting the appropriate tools and techniques depends on several factors:

Project Requirements : The nature of the data and the specific questions you are trying to answer will guide your choice. For instance, Python might be preferred for its machine learning capabilities, while Tableau is chosen for sophisticated visualizations.

Data Size and Complexity : Large datasets and complex analyses might require more advanced tools like Python or R, whereas Excel (limited to around 1 million rows and 16 thousand columns) could suffice for smaller, simpler datasets.

Skill Set : The proficiency of the data analyst in using these tools also plays a significant role. It’s essential to balance the choice of tool with the analyst's comfort level and expertise.

Budget and Resources : Some tools require significant investment, both in terms of licenses and training. Open-source options like Python and R offer powerful functionalities at no cost.

Example Application

Consider a retail company looking to optimize its inventory levels based on historical sales data. The data analyst might use:

• SQL to extract sales data from the company's database.
• Python for conducting time-series analysis to identify sales trends and predict future demand.
• Tableau to create visualizations that illustrate these trends and forecasts, facilitating strategic discussions on inventory management.

Through the strategic application of these tools and techniques, data analysts can uncover valuable insights that drive informed decision-making and strategic planning within organizations.

The exploration of tools and techniques underscores the versatility and power of data analytics. Whether through statistical analysis, predictive modeling, or insightful visualizations, these tools empower analysts to turn data into strategic assets.

Importance and Uses of Data Analytics

Data analytics has become a pivotal element of business strategy, influencing decisions across all levels of an organization. Its importance cannot be overstated, as it provides the insights needed for businesses to innovate, stay competitive, and improve operational efficiency. This section explores the significance of data analytics across various domains, including healthcare, product optimization and pricing, and its relevance for small enterprises and startups.

Embracing data analytics allows organizations to move from intuition-based decisions to informed strategies. As we advance, the integration of data analytics into every aspect of business operations and strategy will become more pronounced, highlighting its critical role in shaping the future of industries worldwide.

Transforming Business Success

Data analytics empowers businesses to make informed decisions by providing a deep understanding of customer behavior, market trends, and operational performance. It enables companies to:

• Optimize Operations : By analyzing data, businesses can identify inefficiencies in their operations and find ways to reduce costs and improve productivity.
• Enhance Customer Experience : Data analytics allows businesses to understand their customers' preferences and behaviors, leading to improved revenues, customer satisfaction and loyalty.
• Product Innovation/Optimization and Pricing : Survey research methods such as conjoint analysis and MaxDiff are especially useful for optimizing features for and pricing products/services, keeping companies at the forefront of innovation and competitiveness.

In healthcare, data analytics plays a critical role in improving patient outcomes and operational efficiency. By analyzing patient data, healthcare providers can:

• Predict Outbreaks : Data analytics can help in predicting disease outbreaks, enabling healthcare systems to prepare and respond effectively.
• Personalize Treatment : Analytics (including MaxDiff and conjoint analysis) can elicit real-time preferences from patients that can lead to better personalized treatment plans, improving patient care and outcomes. Several groups of physicians and academic researchers have presented research at Sawtooth Software conferences on using these tools for facilitating better communication between patients and doctors and selecting treatment plans for diseases such as cancer to result in improved outcomes.
• Improve Operational Efficiency : Data analytics can optimize hospital operations, reducing wait times and improving patient flow.

Product Optimization and Pricing

Repositioning existing products, developing new products, and setting effective pricing strategies are vital to most any business. By using gold standard tools for survey research such as conjoint analysis and MaxDiff, businesses can:

• Find Optimal Sets of Features : Conjoint analysis can within a single survey research project evaluate 1000s of potential feature configurations, determining which feature sets will compete best relative to specific competitors.
• Identify Profitable Target Segments : Conjoint analysis or MaxDiff are excellent techniques for identifying and sizing market segments that have specific needs and are associated with different levels of price sensitivity.
• Measure Price Elasticity: Choice-Based Conjoint (CBC) analysis is particularly valuable for estimating price elasticity of demand for the firm’s brand(s), as well as assessing how changes to competitor’s prices affect quantity demanded for the firm’s brand(s) ( cross-elasticity ).

Relevance for Small Enterprises and Startups

For small enterprises and startups, data analytics offers a competitive edge, enabling them to:

• Make Informed Decisions : Even with limited resources, small businesses can use data analytics to make strategic decisions based on market trends and customer feedback.
• Identify Opportunities : Analytics can reveal market gaps and customer needs, providing startups with insights to innovate and capture new markets.

The Role of a Data Analyst

In the heart of data-driven organizations lies the Data Analyst , a professional whose responsibilities are as varied as they are critical. Understanding the role of a data analyst not only highlights the importance of data analytics in modern business but also sheds light on the skills and perspectives needed to excel in this field.

Responsibilities and Tasks

A data analyst's journey often begins with problem formulation and developing hypotheses and strategies for solving a business or organizational problem. Next often follows data collection, ensuring the quality and accuracy of the data sourced from various channels, including survey research. This foundational step is critical, as the integrity of the data directly impacts the insights derived from it. The analyst then proceeds to clean and preprocess the data, preparing it for analysis. This involves handling missing values, removing duplicates, trimming outliers, and ensuring the data is in a format suitable for analysis.

The core of a data analyst's role involves statistical analysis and data modeling to interpret the data. They employ a range of techniques, from simple descriptive statistics to more complex predictive models, to unearth trends, patterns, and correlations within the data.

However, the role extends beyond just analyzing data. Data visualization and reporting are equally important, as these allow the analyst to communicate their findings in a clear, compelling manner. Whether through dashboards, reports, or presentations, the ability to present data in an accessible way is crucial for informing decision-making processes within an organization.

Professional Insights

From the perspective of a seasoned data analyst, the job is not just about numbers and algorithms; it's about solving challenging business and organizational problems and storytelling with data. It involves translating complex datasets into actionable insights that can drive strategy and impact. An effective data analyst combines analytical skills with business acumen, understanding the broader context in which the data exists.

Career Opportunities in Data Analytics

The field of data analytics offers a dynamic career landscape, characterized by a high demand for skilled professionals capable of turning data into actionable insights. As businesses across industries continue to recognize the value of data-driven decision-making, the demand for data analysts has surged, creating a wealth of opportunities for those equipped with the right skills and knowledge. This section will explore career prospects, including job growth, and discuss the relevance of degrees and certifications in data analytics.

Job Growth and Demand

The demand for data analysts is projected to grow significantly in the coming years. According to industry reports and labor statistics, the job market for data analysts is expected to grow much faster than the average for all occupations. This growth is driven by the increasing volume of data generated by businesses and the need to analyze this data to make informed decisions.

• Projected Job Growth : Data analytics roles are expected to see one of the highest rates of job growth across all sectors.
• Industries Hiring : While technology and finance traditionally lead in hiring data analysts, healthcare, marketing, and retail are rapidly catching up, reflecting the broad applicability of data analytics skills.

Salary Ranges

Salaries for data analysts can vary widely based on experience, location, and industry. However, data analysts typically command competitive salaries, reflecting the high demand and specialized skill set required for the role.

• Entry-Level Positions : Even at entry levels, data analysts can expect salaries that are competitive, with potential for rapid growth as experience and skills develop.
• Senior Roles : Experienced data analysts, especially those with specialized skills or leadership roles, can command significantly higher salaries.

Degrees and Certifications

While a degree in data science, statistics, computer science, or a related field can provide a strong foundation, the field of data analytics also values practical experience and specialized skills.

• Relevant Degrees : Bachelors and masters degrees in relevant fields are highly valued, but not always required.
• Certifications : Certifications can supplement academic degrees and provide evidence of specialized skills in data analytics tools and methodologies. Popular certifications include Certified Analytics Professional (CAP), Google Data Analytics Professional Certificate, and various platform-specific certifications (e.g., Tableau, SAS).

Making It in Data Analytics

Success in a data analytics career is not solely determined by technical skills. Employers also value problem-solving abilities, business acumen, and the capacity to communicate complex findings in a clear and actionable manner. Continuous learning and adaptation to new tools, technologies, and methodologies are essential in this rapidly evolving field.

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Data analytics is not just a tool but a strategic asset that can drive significant business value, enhance operational efficiency, and foster innovation across various sectors. From improving healthcare outcomes to enabling small businesses to compete more effectively, the applications of data analytics are vast and varied.

As we embrace the future, the importance of data analytics in driving business success and societal improvement will only continue to grow. For those considering a career in data analytics or looking to implement data-driven strategies in their operations, the potential is limitless. The benefits of data-driven decision-making underscore the transformative power of data analytics, making it an indispensable part of modern business and governance.

Whether you are a budding data analyst, a business leader, or simply curious about the potential of data analytics, the journey into this field is not only rewarding but essential for those looking to make an impact in the digital age. 

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Statistical analysis: What It Is, Types, Uses & How to Do It

Statistical analysis offers us a valuable set of interpretations for understanding the results of an investigation. It equips us to identify trends within different data points, develop statistical models, and design surveys and research studies. This capacity becomes particularly crucial when dealing with large volumes of data, as it enables us to analyze data values and collect relevant insights for shaping future trends, ultimately making sense of big data by applying various statistical tools.

We know data analysis involves an in-depth review of each part of a whole to understand its structure and interpret its operation. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions. Statistics, on the other hand, is the science that uses probabilities as a basis to influence the possible outcomes of situations that are determined by numerical data when collecting, interpreting and determining their validity.

What is Statistical Analysis?

Statistical analysis collects, cleans, summarizes, interprets, and draws conclusions from data. It involves using statistical techniques and methods to analyze and make sense of information, helping researchers, analysts, and decision-makers understand patterns, relationships, and trends within data sets. Statistical analysis is fundamental to scientific research, business intelligence, quality control, and decision-making in various fields.

Hence, you engage in statistical analysis when you gather and interpret data with the purpose of uncovering patterns and trends. This signifies that, while it constitutes a form of data analysis on its own, it is undertaken with an interpretive perspective, which proves invaluable in making informed decisions and comprehending a company’s potential customers, their behaviors, and their experiences.

“Today, statistics is a tool that cannot be lacking in the analysis of data from an investigation, because from the conception of the idea of ​​what is going to be investigated, through the definition of objectives, hypotheses, variables, collection of the data, organization, review, classification, tabulation and production of the results for their analysis it is important to know how to give an appropriate use to the different measures and statistical models for the analysis. When it is accomplished, the results obtained represent a true contribution to solving the problems inherent to the field where the activities inherent to the different investigations are carried out. ” —Profr. Gerardo Bauce

With a statistical analysis, we can answer questions such as the following:

• Who are our clients?
• How much does a client pay in a visit?
• What is the age of our clients?
• How can we categorize our types of clients?
• What types of experiences do our clients enjoy?

Identifying patterns of behavior or different trends in a set of data helps companies observe and record the buying behavior of their customers, both to improve their products or services and to facilitate an updated and improved shopping experience , obtaining satisfied customers and great brand awareness as a result.

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Types of Statistical Analysis

Statistical analysis is indispensable to data analysis, research, and informed decision-making. It encompasses a wide array of techniques, each tailored to specific purposes, making it a versatile tool in data analytics. Here are some of the most common types of statistical analysis:

Descriptive Analysis:

Descriptive statistics help in the presentation of data, making it more understandable through charts and tables, particularly valuable for market analysis or when working with categorical data.

Inferential Analysis:

Inferential statistics delve into relationships between variables, often involving hypothesis tests and drawing conclusions from sample data to generalize to a larger population.

Predictive Analysis:

Predictive statistical analysis harnesses the power of machine learning, data mining, and data modeling to discern patterns, thereby facilitating the anticipation of future events by drawing insights from historical data. This practice is fundamental not only in data analysis but also in guiding pivotal business decisions.

Prescriptive Analysis:

This statistical analysis provides recommendations and informed decisions based on the data. It’s invaluable for guiding strategic actions.

Exploratory Data Analysis:

This method explores unknown data associations and uncovers potential relationships, similar to inferential analysis, but emphasizes data landscape exploration.

Causal Analysis:

Causal statistical analysis unravels cause-and-effect relationships within raw data, delving into what specific events occur and their impacts on other variables. It is vital for understanding the market dynamics or conducting hypothesis tests.

The choice of statistical analysis method hinges on research questions, data type, and underlying assumptions. Researchers, statisticians, and data analysts meticulously select the appropriate method according to their objectives and the nature of the data they collect and analyze. Statistical software and data sets form the core of their toolbox, facilitating effective data collection, analysis, and informed decision-making.

How to perform a functional statistical analysis

In order to perform statistical analysis, we need to collect and review the data samples available in the results of the study to be analyzed.

Although there is no single way to carry out an interpretive analysis, there are practices that can be replicated in any study if they are carried out in the appropriate way to the information provided. These tips will allow us to carry out a useful analysis.

• Give a clear and realistic description of the data we have.
• Analyze how the data is related to the study subjects.
• Design a model that considers and describes the relationship between the data and the study subjects.
• Evaluate the model to determine its validity.
• Consider scenarios and tests using predictive analytics.

Advantages of Statistical Analysis

Statistical analysis, a cornerstone of data science, offers several advantages in various fields, including research, business, and decision-making. By effectively analyzing data, statistical analytics supports critical advantages:

Data Interpretation: Statistical analysis plays a pivotal role in data science, helping researchers and analysts summarize and interpret complex data, making it more accessible and enabling them to draw meaningful insights. This capability is especially crucial when dealing with vast data sets.

Objectivity: In data science, statistical analytics provides an objective and systematic approach to decision-making and hypothesis testing, reducing the influence of bias in interpreting collected data. This impartiality enhances the reliability of findings.

Generalization: One of the primary strengths of statistical analysis is its ability to generalize research findings from a sample to the entire data population, thereby enhancing the external validity of research studies.

Data Reduction: When dealing with extensive data sets, statistical analysis assists in data reduction, extracting key patterns and relationships. Simplifying these large data sets makes it easier to work with, facilitating more effective communication of results.

Comparisons: Statistical analysis, including calculating measures like standard deviation, makes comparing different groups or conditions within the collected data easier. It aids in identifying significant differences or similarities, a crucial step in decision-making and research.

Prediction: Statistical analysis goes beyond merely analyzing data; it enables the development of predictive models. These models are invaluable for forecasting trends, making predictions, and aiding in informed decision-making.

Statistical analysis is the backbone for gathering and analyzing data sets in data science. This robust framework is critical for extracting meaningful insights from collected data, thus enhancing decision-making and propelling progress in numerous fields

Uses of Statistics in Data Analysis

When we comprehensively understand the trends within our market, we gain a competitive advantage. We can employ statistical analysis to anticipate future behaviors by implementing suitable risk management strategies. Furthermore, by leveraging specific data on consumer behavior , we can discern their preferences, pinpoint the products or services that resonate most and least with them, and strategize on how to effectively engage them in making a purchase.

In a landscape where new trends and behaviors among clients and even employees constantly emerge, reviewing and analyzing complex data is imperative. For this purpose, we recommend using survey software like QuestionPro, which offers specialized tools and functions for designing a robust statistical analysis. This approach ensures that the data collected, the determination of sample sizes, and the selection of sample groups are aligned with the analysis’s objectives and represent the entire population. Engaging skilled statistical analysts in this process further enhances the effectiveness of the data collection and testing of statistical hypotheses.

There’s always new trends and behaviors among clients or even employees that we need to constantly review, for this reason, to carry out more complex data with specific tools and functions to design a statistical analysis, we recommend using survey software such as QuestionPro.

Uses of Statistical Analysis with Examples

Statistical analysis is widely adopted across various fields, serving numerous critical purposes. Here are some typical applications of this analysis, along with examples:

Business and Economics:

• Market Research: Businesses can analyze preferences and trends by collecting data from a representative sample of customers. For instance, a company might conduct surveys to determine which product features are most appealing to customers.
• Financial Analysis: In the financial sector, statistical analysis is instrumental in understanding stock market trends and forecasting future stock prices. Using historical data makes it possible to create models for predicting alterations in stock prices.

Healthcare:

• Clinical Trials: In the healthcare sector, statistical analysis is employed in clinical trials to assess the efficacy of new medications. Researchers compare patient outcomes in a control group with those in a treatment group to determine the drug’s impact.
• Epidemiology: Statistical analysis helps epidemiologists analyze disease patterns in populations. For example, data is examined during a disease outbreak like COVID-19 to understand how the disease spreads across different regions.

Manufacturing and Quality Control:

• Quality Assurance: Statistical process control (SPC) is applied in manufacturing to oversee and enhance production processes. It ensures consistent, high-quality product output. Statistical analysis allows real-time monitoring of critical parameters to detect variations and take corrective action.
• Defect Analysis: In quality control, the analysis of product defects involves collecting data through random sampling . For instance, a sample of widgets may be inspected to determine if they meet quality standards. This analysis aids in identifying and addressing defects effectively.

These examples highlight how data sets are collected through methods such as random sampling, allowing for the determination of sample sizes and the selection of representative sample groups. Statistical analysts play a crucial role in applying appropriate statistical techniques and methods to address specific research questions in these fields. This analysis is a versatile tool that enhances decision-making, problem-solving, and insights generation across diverse domains.

Statistical analysis, facilitated by advanced statistical analysis software, is a fundamental and versatile tool pivotal across many disciplines and industries. It empowers researchers, analysts, and decision-makers to extract valuable insights, make informed choices, and draw meaningful conclusions from data. Whether leveraging sophisticated statistical methods to reveal intricate patterns, employing statistical software to make accurate predictions, or conducting statistical tests to identify causal relationships, this analysis is an indispensable cornerstone for data-driven decision-making. It serves as the conduit between raw data and actionable knowledge, enabling evidence-based decision-making in areas as diverse as healthcare and economics, environmental science, and manufacturing.

Moreover, statistical analysis is pivotal in enhancing our understanding of the world and helps us mitigate risks, optimize processes, and tackle complex problems. Employing a wide array of statistical tests and techniques bridges the gap between the abundance of raw data and the practical insights needed for effective decision-making. Whether it’s assessing the impact of interventions on a dependent variable in healthcare research or optimizing manufacturing processes for improved product quality, statistical analysis is a linchpin in our ability to decipher the complex tapestry of data surrounding us and turn it into actionable information.

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Table of Contents

Types of statistical analysis, importance of statistical analysis, benefits of statistical analysis, statistical analysis process, statistical analysis methods, statistical analysis software, statistical analysis examples, career in statistical analysis, choose the right program, become proficient in statistics today, understanding statistical analysis: techniques and applications.

Statistical analysis is the process of collecting and analyzing data in order to discern patterns and trends. It is a method for removing bias from evaluating data by employing numerical analysis. This technique is useful for collecting the interpretations of research, developing statistical models, and planning surveys and studies.

Statistical analysis is a scientific tool in AI and ML that helps collect and analyze large amounts of data to identify common patterns and trends to convert them into meaningful information. In simple words, statistical analysis is a data analysis tool that helps draw meaningful conclusions from raw and unstructured data. 

The conclusions are drawn using statistical analysis facilitating decision-making and helping businesses make future predictions on the basis of past trends. It can be defined as a science of collecting and analyzing data to identify trends and patterns and presenting them. Statistical analysis involves working with numbers and is used by businesses and other institutions to make use of data to derive meaningful information. 

Given below are the 6 types of statistical analysis:

Descriptive Analysis

Descriptive statistical analysis involves collecting, interpreting, analyzing, and summarizing data to present them in the form of charts, graphs, and tables. Rather than drawing conclusions, it simply makes the complex data easy to read and understand.

Inferential Analysis

The inferential statistical analysis focuses on drawing meaningful conclusions on the basis of the data analyzed. It studies the relationship between different variables or makes predictions for the whole population.

Predictive Analysis

Predictive statistical analysis is a type of statistical analysis that analyzes data to derive past trends and predict future events on the basis of them. It uses machine learning algorithms, data mining , data modelling , and artificial intelligence to conduct the statistical analysis of data.

Prescriptive Analysis

The prescriptive analysis conducts the analysis of data and prescribes the best course of action based on the results. It is a type of statistical analysis that helps you make an informed decision. 

Exploratory Data Analysis

Exploratory analysis is similar to inferential analysis, but the difference is that it involves exploring the unknown data associations. It analyzes the potential relationships within the data. 

Causal Analysis

The causal statistical analysis focuses on determining the cause and effect relationship between different variables within the raw data. In simple words, it determines why something happens and its effect on other variables. This methodology can be used by businesses to determine the reason for failure. 

Statistical analysis eliminates unnecessary information and catalogs important data in an uncomplicated manner, making the monumental work of organizing inputs appear so serene. Once the data has been collected, statistical analysis may be utilized for a variety of purposes. Some of them are listed below:

• The statistical analysis aids in summarizing enormous amounts of data into clearly digestible chunks.
• The statistical analysis aids in the effective design of laboratory, field, and survey investigations.
• Statistical analysis may help with solid and efficient planning in any subject of study.
• Statistical analysis aid in establishing broad generalizations and forecasting how much of something will occur under particular conditions.
• Statistical methods, which are effective tools for interpreting numerical data, are applied in practically every field of study. Statistical approaches have been created and are increasingly applied in physical and biological sciences, such as genetics.
• Statistical approaches are used in the job of a businessman, a manufacturer, and a researcher. Statistics departments can be found in banks, insurance businesses, and government agencies.
• A modern administrator, whether in the public or commercial sector, relies on statistical data to make correct decisions.
• Politicians can utilize statistics to support and validate their claims while also explaining the issues they address.

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Statistical analysis can be called a boon to mankind and has many benefits for both individuals and organizations. Given below are some of the reasons why you should consider investing in statistical analysis:

• It can help you determine the monthly, quarterly, yearly figures of sales profits, and costs making it easier to make your decisions.
• It can help you make informed and correct decisions.
• It can help you identify the problem or cause of the failure and make corrections. For example, it can identify the reason for an increase in total costs and help you cut the wasteful expenses.
• It can help you conduct market research or analysis and make an effective marketing and sales strategy.
• It helps improve the efficiency of different processes.

Given below are the 5 steps to conduct a statistical analysis that you should follow:

• Step 1: Identify and describe the nature of the data that you are supposed to analyze.
• Step 2: The next step is to establish a relation between the data analyzed and the sample population to which the data belongs. 
• Step 3: The third step is to create a model that clearly presents and summarizes the relationship between the population and the data.
• Step 4: Prove if the model is valid or not.
• Step 5: Use predictive analysis to predict future trends and events likely to happen. 

Although there are various methods used to perform data analysis, given below are the 5 most used and popular methods of statistical analysis:

Mean or average mean is one of the most popular methods of statistical analysis. Mean determines the overall trend of the data and is very simple to calculate. Mean is calculated by summing the numbers in the data set together and then dividing it by the number of data points. Despite the ease of calculation and its benefits, it is not advisable to resort to mean as the only statistical indicator as it can result in inaccurate decision making. 

Standard Deviation

Standard deviation is another very widely used statistical tool or method. It analyzes the deviation of different data points from the mean of the entire data set. It determines how data of the data set is spread around the mean. You can use it to decide whether the research outcomes can be generalized or not. 

Regression is a statistical tool that helps determine the cause and effect relationship between the variables. It determines the relationship between a dependent and an independent variable. It is generally used to predict future trends and events.

Hypothesis Testing

Hypothesis testing can be used to test the validity or trueness of a conclusion or argument against a data set. The hypothesis is an assumption made at the beginning of the research and can hold or be false based on the analysis results. 

Sample Size Determination

Sample size determination or data sampling is a technique used to derive a sample from the entire population, which is representative of the population. This method is used when the size of the population is very large. You can choose from among the various data sampling techniques such as snowball sampling, convenience sampling, and random sampling. 

Everyone can't perform very complex statistical calculations with accuracy making statistical analysis a time-consuming and costly process. Statistical software has become a very important tool for companies to perform their data analysis. The software uses Artificial Intelligence and Machine Learning to perform complex calculations, identify trends and patterns, and create charts, graphs, and tables accurately within minutes. 

Look at the standard deviation sample calculation given below to understand more about statistical analysis.

The weights of 5 pizza bases in cms are as follows:

Calculation of Mean = (9+2+5+4+12)/5 = 32/5 = 6.4

Calculation of mean of squared mean deviation = (6.76+19.36+1.96+5.76+31.36)/5 = 13.04

Sample Variance = 13.04

Standard deviation = √13.04 = 3.611

A Statistical Analyst's career path is determined by the industry in which they work. Anyone interested in becoming a Data Analyst may usually enter the profession and qualify for entry-level Data Analyst positions right out of high school or a certificate program — potentially with a Bachelor's degree in statistics, computer science, or mathematics. Some people go into data analysis from a similar sector such as business, economics, or even the social sciences, usually by updating their skills mid-career with a statistical analytics course.

Statistical Analyst is also a great way to get started in the normally more complex area of data science. A Data Scientist is generally a more senior role than a Data Analyst since it is more strategic in nature and necessitates a more highly developed set of technical abilities, such as knowledge of multiple statistical tools, programming languages, and predictive analytics models.

Aspiring Data Scientists and Statistical Analysts generally begin their careers by learning a programming language such as R or SQL. Following that, they must learn how to create databases, do basic analysis, and make visuals using applications such as Tableau. However, not every Statistical Analyst will need to know how to do all of these things, but if you want to advance in your profession, you should be able to do them all.

Based on your industry and the sort of work you do, you may opt to study Python or R , become an expert at data cleaning, or focus on developing complicated statistical models.

You could also learn a little bit of everything, which might help you take on a leadership role and advance to the position of Senior Data Analyst. A Senior Statistical Analyst with vast and deep knowledge might take on a leadership role leading a team of other Statistical Analysts. Statistical Analysts with extra skill training may be able to advance to Data Scientists or other more senior data analytics positions.

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Hope this article assisted you in understanding the importance of statistical analysis in every sphere of life. Artificial Intelligence (AI) can help you perform statistical analysis and data analysis very effectively and efficiently. 

If you are a science wizard and fascinated by statistical analysis, check out this amazing Post Graduate Program In Data Analytics in collaboration with Purdue. With a comprehensive syllabus and real-life projects, this course is one of the most popular courses and will help you with all that you need to know about Artificial Intelligence. 

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Popular statistical data analysis tools and techniques used in market research

• Statistical data analysis   is a method used for performing numerous statistical operations. It’s a kind of quantitative research, which seeks to quantify the data, and usually, relates some form of statisticalanalysis.
• It is a survey-based analysis technique that helps market research to determine how people value various attributes (feature, function, benefits) which makes up an individual product or service.

Introduction

Statistics is fundamentally a science that includes data interpretation, data collection, and data validation.  Statistical data analysis  is a process of accomplishing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data, and applies some form of statistical analysis. Quantitative data involves descriptive data, such as survey data and observational data. Market Research and Competitive Statistical Analysis : Market research is how product managers gather information about customer needs and market drivers. Competitive Statistical is the subset of market research. Numerous data analysis tools and techniques are existing in the market, having its own set of functions. The choice of tools should always be based on the type of analysis conducted, and the data used.

Data analysis techniques

Regression Analysis

The statistical techniques used to carry out the relationship between two or more variables. A variation in the dependent variables mainly depends on and is associated with, a change in one or more independent variables.

 Linear regression- It uses an independent variable to predict the result of the dependent variable.

 Multiple regressions –It uses at least two independent variables.

For example: Analyzing a particular organization or retail shop, before and after advertising. The money the store spends on publicity will be considered as an independent variable; the number of people will be the dependent variable. Online Statistical Analyses Services Help gives you a clear guidance about conducting an effective survey.

The resulting graph obtained from the regression analysis shows the regression curve. It shows the impact of money on publicity, whether it increases or decreases:  only in some rare cases data will be directly proportional, so some degree of the curve will always be on the graph.

Analysis of Variance (ANOVA) Test

It is used along with regression to identify the effect of independent variables over the dependent variable. It compares multiple groups simultaneously to find the relationship between them, e.g. analyzing whether a different type of advertisements gets different consumer responses.  

Conjoint Analysis

It is a survey-based analysis technique that helps market research to determine how people value various attributes (feature, function, benefits) which makes up an individual product or service. The ultimate aim of conjoit analysis is to identify at what combination the attributes is effective for the respondent choice. A controlled set of possible products or services is displayed to the survey respondent. By analyzing how they decide on choosing the products, the implicit evaluation of the individual elements makes up the product or service can be determined. These implicit valuations generate market models that evaluate market share, income and even success of new designs.

Market researchers always love to predict people. To identify some fundamental reasons, why people make complex choices. And Conjoint analysis is close to this; it inquires people to create trade-offs while creating decisions, just as they carry out in the physical world, then examines the outcomes to give the most popular result. If you find difficulty with the survey-based analysis, you reach Professional Help With Analysis Of Statistical Data to get efficient research work.

A t-test help to associate two data groups which have different mean values, e.g. do male and female have different mean heights? The t-test enables the user to interpret whether differences are meaningful or merely coincidental.  It is used to identify the significant difference between the mean of two clusters, which may be connected in certain aspects. It is one of many tests used for  hypothesis testing  in statistics.

  Professional Help with Analysis of Statistical Data helps you to get an effective test.

Calculating a t-test requires three fundamental data values,

• The mean difference that is a difference between the mean values from each data set
• The standard deviation of each group, and
• The number of data values.

Descriptive Analysis :

It takes the historical data, Key performance indicators, and elaborates the performance based on the selected benchmark. It considers the past trends and how they might impact future performance. 

Dispersion Analysis : It is an area onto which a data set is spread. This technique helps the data analysts to identify the variability factor which is under study.

Factor Analysis : This method used to determine the existence of a relationship between groups of variables. This process reveals factors or variables which describe the patterns in the relationship among the original variables.

Data Analysis Tools

Numerous Data analysis tools are existing in the market, having its own set of functions. The choice of tools should always be based on the type of analysis conducted, and the data used. The following are the list of few Data Analysis tools used for compelling. 

It comes under the BI Tool category, created for individual data analysis purpose. The soul of Tableau is the Pivot Table and Pivot Chart and works towards demonstrating data in the most user-friendly way. It also contains a data cleaning feature along with brilliant analytical functions.

It started as a plugin for Excel, and later it detached from it to create one of the most powerful data analytical tools. It has three different versions. It consists of a power pivot and DAX language, which help implement sophisticated advanced analytics similar to writing Excel formulas. Online Statistical Analyses Services help you with handling various data analysis tools.

R & Python

R & Python are the powerful and flexible programming language; R is used for statistical analysis, such as cluster classification algorithms, normal distribution, and regression analysis. It also helps in an individual predictive analysis like customer behavior, their expenditure, and their preferred items based on their browsing history, and so on. It also includes machine learning and artificial intelligence concepts.

SAS is a data analytics and data manipulation programming language, which can effortlessly access data from various resources. SAS has presented a broad set of customer profiling products for web, social media, and marketing analytics. It can predict their behaviors, manage, and optimize communications.

Conclusion Statistical consulting  for dissertations is our exclusive focus. And which is essential for many fields and offers extensive potentials to people in need of it.  Statistical Analysis and Consulting Services are popular and applied in every aspect of society because it ensures adequate and successful functioning of an organization.

• Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis.  Indian journal of anaesthesia ,  60 (9), 662.
• Cizek, P., Härdle, W. K., & Weron, R. (Eds.). (2005).  Statistical tools for finance and insurance . Springer Science & Business Media.
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2020 Top 50 U.S. Market Research and Data Analytics Companies

Diane Bowers

A full ranking of the top market research and data analytics companies in the U.S. for 2020

The “2020 Top 50 U.S. Report”—formerly known as “The Gold Report”—is developed by Diane Bowers and produced in partnership with the Insights Association and Michigan State University . The report is also sponsored by the AMA, ESOMAR and the Global Research Business Network . The report includes a ranking of the top 50 companies, a breakdown of trends by Bowers , and an analysis of the market research and analytics industry  by Michael Brereton, Melanie Courtright and Reg Baker.

50. RTi Research

Founded: 1979 2019 U.S. revenue: $12.9 million Percent change from 2018: -3% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $12.9 million U.S. employees: 45

In a world awash in data, the challenge is to turn data into something meaningful, something that can be communicated simply and acted upon effectively. RTi Research meets that challenge head-on, turning data into meaning through smart research design, flawless execution and innovative storytelling. Everything the company does is aimed at helping its clients move their ideas and insights through their organizations to influence change. 

RTi has conducted research in just about every category in the U.S. and globally. Informed by 40 years of experience across categories and cultures, RTi knows what works and what doesn’t, when to leverage new technology and methods, and when traditional approaches are best.

49. Hypothesis

Founded: 2000 2019 U.S. revenue: $18.3 million Percent change from 2018: -4.7% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $18.3 million U.S. employees: 61

Hypothesis uses insights, strategy and design to help important brands do amazing things. The company specializes in tough questions that take creative, multidimensional approaches, thoughtful strategy and a broad business perspective. Hypothesis’ approach combines inventive consumer-centric qualitative research, advanced analytics, strategic thinking and data visualization. Its award-winning design team translates complex information into compelling, easy-to-understand deliverables to socialize learnings and engage teams. 

In 2018, Hypothesis added important new capabilities with the launch of Momentum, a strategy that turns insight into application with downstream marketing and implementation planning. The Momentum team has worked alongside Hypothesis consultants on strategic engagements with clients focused on brand strategy, product development, and led dozens of workshops with senior and C-level executives to socialize insights and ideate on next steps. 

In 2019, Hypothesis’ focus on growth continued with its expansion to the Midwest and establishment of its Chicago office. From this office, the company will be able to service new and current clients in the Midwest and on the East Coast.

48. Bellomy Research

Founded: 1976 2019 U.S. revenue: $21 million Percent change from 2018: 1.4% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $21 million U.S. employees: 116

Bellomy is a privately held, family-owned, full-service market intelligence company. Bellomy focuses on driving successful business outcomes through the design and delivery of solutions that yield deeper customer understanding. The company surrounds its clients’ business challenges with an unparalleled mix of knowledge and experience, marketing science and proprietary research technology. 

Bellomy’s work involves both B2C and B2B environments—with qualitative and quantitative insight solutions spanning market segmentation, customer experience and journeys (including digital user experiences), brand equity, product innovation, shopper insights, marketing optimization, social research platforms and research technology. Bellomy works with clients across a broad range of categories and industries including consumer packaged goods, financial services, automotive, retail, restaurant and hospitality, telecommunications and technology, apparel and textiles, utilities, healthcare, insurance and home improvement.

Bellomy serves as an extension of its clients’ marketing research and customer experience departments by integrating a broad set of capabilities and areas of expertise, including segmentation, customer (and digital experience), shopper insights, social research platforms, brand equity, product innovation and marketing optimization. In addition, Bellomy clients leverage SmartIDEAS, the firm’s enterprise consumer knowledge and insight platform. 

47. Edelman Intelligence

Founded: 1999 2019 U.S. revenue: $21 million Percent change from 2018: 12.9% 2019 non-U.S. revenue: $11.5 million Percent from outside U.S.: 35.4% 2019 worldwide revenue: $32.5 million U.S. employees: 131

Edelman Intelligence (EI) is the global research and analytics consultancy of Edelman, the world’s largest global communications firm. Based in New York, with employees in 18 offices internationally, EI houses more than 200 consultants, strategists, researchers, data scientists, data visualization specialists and analysts worldwide. Its specialists are method-agnostic and leverage the best of primary and secondary research, advanced analytics and business science to solve business and communications issues for its clients. EI’s offering spans the full spectrum of client needs, from mapping the current environment and targeting key audiences, to optimizing content and measuring business impact. 

EI partners with early-stage start-ups and Fortune 100 companies alike, providing strategic research, analytics, and insights-based marketing and communications counsel for a broad range of stakeholders and scopes, including government and public affairs, corporate reputation and risk strategy, crisis and issues management, employee experience and talent advisory, executive positioning, strategic communications and public relations, marketing and branding strategy, customer experience and insights, mergers, acquisitions and market entry strategy and more.

Key accomplishments in 2019 included advancement of its Edelman Trust Management (ETM) capabilities, including an evolution of its offering focused specifically on providing guidance for measuring and building trust in brands. Developed building from its 20-plus years studying trust through the Edelman Trust Barometer and the initial iteration of ETM (which explores corporate trust), this proprietary model for brand trust measurement was created in partnership with renowned academics from Harvard Business School and INSEAD, Edelman Brand experts and external marketing thought leaders. In recent months, this model has been engineered to consider fundamental transformations to consumer/brand relationship dynamics that the COVID-19 pandemic has accelerated.

46. KS&R

Founded: 1983 2019 U.S. revenue: $21.7 million Percent change from 2018: -1.4% 2019 non-U.S. revenue: $3.6 million Percent from outside U.S.: 14.2% 2019 worldwide revenue: $25.3 million U.S. employees: 100

KS&R is a privately held strategic consultancy and full-service marketing research company. For nine consecutive years, KS&R has received the highest Gold Index composite score of any provider in the Prevision/Inside Research survey of marketing research buyers. This is a testament to the company’s passion for excellence and client-first business philosophy—wherein KS&R empowers its clients with timely, fact-based insights so they can make smarter decisions and be confident in their actions.

KS&R creates and executes global custom market research solutions for some of the best-known corporations in the world in more than 100 countries and 50 languages. It has extensive and diverse industry experience with particular strength in healthcare (pharma and device), technology, entercom, transportation, professional services, and retail and e-commerce. Team members often include business strategists with client-side experience and deep industry knowledge.

In 2019, KS&R leveraged its expansive network of pharmacy panels to build world-class capabilities for pharma inventory measurement and healthcare insights. Its marketing scientists have driven marked advances in pricing decision support, which have now been validated by positive in-market results. KS&R expanded its portfolio to include insights fusion across multiple channels of content (primary research, social media, web-based information, etc.). And finally, it introduced its KS&R Win-Loss program that provides actionable insights for how organizations can improve their value proposition and sales performance to close more deals.

Founded: 1911 2019 U.S. revenue: $22.7 million Percent change from 2018: 12.9% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $22.7 million U.S. employees: 78

NAXION guides strategic business decisions globally in healthcare, information technology, financial services, energy, heavy equipment and other B2B markets, drawing on depth of marketing experience in key verticals and skilled application of sophisticated and inventive methodologies. The firm’s NAscence Group helps life science innovators develop commercialization strategy through clinical trials design and selection of target indications, forecasting, brand planning and other research-based consulting services.

Engagements routinely include market segmentation, opportunity assessment and innovation, demand forecasting and pricing, positioning, brand health, market monitoring and lifecycle management. The firm deploys multiple data streams including primary research (qualitative and quantitative), secondary data, customer databases and other complex datasets to develop an integrative perspective on business problems. The firm also builds custom panels for B2B markets.

Project leaders with sector experience and research proficiency are supported by in-house methodologists and a wide portfolio of advanced analytic tools, including proprietary modeling services and software, all of them highly customized. The firm continues to invest significant resources in intellectual capital to enhance enterprise decision support with cutting-edge methods, including specialized “small data” choice models, new predictive techniques using big data and brand-customized text analytics. Its Farsight suite supports the building of highly dynamic models capable of producing forecasts for complex market scenarios, including paradigm-shift technologies, and gives market monitoring programs a forward-looking perspective that guides timely market interventions. Other services include litigation and regulatory support, often involving expert testimony in cases involving trademark confusion, deceptive advertising and brand equity. NAXION’s strong commitment to operational excellence is reflected in ISO certification and in-house operations capabilities to deliver exceptional levels of quality control. 

Founded: 1991 2019 U.S. revenue: $24.2 million Percent change from 2018: -3.6% 2019 non-U.S. revenue: $1.2 million Percent from outside U.S.: 4.7% 2019 worldwide revenue: $25.4 million U.S. employees: 144

Gongos is a consultative agency that places customers at the heart of business strategy. Partnering with insights, analytics, marketing, strategy and customer experience groups, Gongos operationalizes customer centricity by helping companies both understand their customer needs and deliver on them better than anyone else.

From product innovation to portfolio management, customer experience to consumer journeys, pricing strategies to marketing optimization, and trend analysis to predictive modeling, Gongos provides both outside-in and inside-out approaches across organizations to drive greater customer attraction, retention and lifetime value.

Gongos further serves as a translator to help cross-functional teams fuel the competency to gain and apply consumer wisdom, transform decisions into action and navigate organizational change. Coalescing enterprise data with primary research and curating insights for multiple audiences further empowers stakeholders to achieve greater ROI by ensuring information is designed to influence actions and behaviors from executives to the frontline. 

Gongos’ consultative tools stem from change management principles that help organizations navigate the transformation often necessary to create a more outside-in perspective as they reorient around the customer. Gongos’ approaches to engage multiple audiences include communication strategies and tactics grounded in frameworks such as its adoption-to-advocacy model and human-centered design.

43. Maru/Matchbox **

Founded: 2016 2019 U.S. revenue: $28 million Percent change from 2018: 3.7% 2019 non-U.S. revenue: $14 million Percent from outside U.S.: 33.3% 2019 worldwide revenue: $42 million U.S. employees: 150

Maru/Matchbox began disrupting the market research industry in 2000. Powered by proprietary technology, its expert teams are deeply invested in key sectors of the economy, including consumer goods and services, financial services, retail, technology, healthcare, public services, and media and entertainment. Maru/Marchbox provides organizations with the tools and insights to connect with the people that matter most, so they can build and maintain a competitive advantage. 

In 2019, Maru/Matchbox released a series of innovative research solutions. 

• Digital Media Measurement is a campaign evaluation approach that enables clients to better understand how content, channels and brands interact to deliver effective communication. 
• Creative Insight measures people’s implicit and explicit responses to advertising, giving clients a complete picture of how their ad is working. It is designed to evaluate any type of ad or brand communication, across all channels, with best-in-class benchmarks.
• Lissted analyzes how members of communities relevant to clients react to content, tweets and even websites. 
• Brand Emotion utilizes visual semiotics to identify and leverage the emotional profile of a brand. 

Maru/Matchbox continues to demonstrate innovation and thought leadership through relentless publication of articles and whitepapers.

42. Chadwick Martin Bailey (CMB)

Founded: 1984 2019 U.S. revenue: $28.7 million Percent change from 2018: 20.6% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $28.7 million U.S. employees: 90

CMB is a research and strategy firm, helping the world’s leading brands engage, innovate and grow amid deep disruption. The company leverages the best of advanced analytics, consumer psychology and market strategy to tackle critical business initiatives, including market identification, segmentation, brand health, loyalty and advocacy, and product and service development.

For more than 35 years, CMB has helped the most successful brands and their executives give voice to their market through a relentless business decision focus, creative problem-solving and storytelling, deeply consultative approach and flawless execution. With dedicated financial services, media and entertainment, tech and telecom, retail and healthcare practices, CMB’s expert teams understand the complex and evolving technological, social, cultural and economic forces that drive disruption and create opportunity.

In 2020, CMB continued its growth trajectory, including building expertise in gaming and digital platforms and expanding its qualitative and advanced analytics teams. A thought leader in the application of consumer psychology to real world business issues, CMB conducted self-funded research among tens of thousands of consumers to capture the four core benefits that motivate decision-making—identity, emotion, social and functional—providing an in-depth look at more than 80 global brands. Further self-funded research explored the accelerating journey and path to purchase of today’s gamers.

41. Screen Engine/ASI

Founded: 2010 2019 U.S. revenue: $33 million Percent change from 2018: 10% 2019 non-U.S. revenue: $1.9 million Percent from outside U.S.: 5.4% 2019 worldwide revenue: $34.9 million U.S. employees: 132

Screen Engine/ASI is a research-based consumer insights firm that stands for delivering its entertainment and media clients actionable insights and recommendations, not simply data. SE/ASI strives to help clients mitigate risk and maximize the potential for success. Through its Motion Picture and TV Groups, SE/ASI works across all distribution platforms for both domestic and internationally produced content. 

The company is centered on assessing the “abilities” of content as it migrates from the earliest stages of development through multi-channel distribution. The Motion Picture Group is the leader in traditional and digital in theater and online recruited audience screenings. Offerings also include PostTrak, a syndicated domestic and international in-theater exit poll, and ScreenExperts, an early assessment of critical response, creative ad testing, positioning and brand studies, custom work, and location-based and online focus group research. A cross-platform team within this group works with home entertainment, over-the-top and gaming clients. 

The TV group is the leader in location-based ViewTrac dial testing of pilots, programs and ongoing series and conducts online dial testing as well. Other offerings include location-based and online focus groups, promo testing, positioning and brand studies, and a variety of custom studies including custom trackers. SE/ASI syndicates Tracktion trackers including a TV tracker, a theatrical movie tracker, a home entertainment tracker and a premium video-on-demand tracker. All groups work in the company’s media lab equipped for biometric and new technology research. When appropriate, SE/ASI engages in advanced analytics techniques including, but not limited to, segmentation, conjoint, maxdiff and TURF analysis. 

40. MarketVision Research

Founded: 1983 2019 U.S. revenue: $33.2 million Percent change from 2018: 2.5% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $33.2 million U.S. employees: 140

MarketVision Research is a full-service marketing research firm, providing clients with actionable insights about their markets, customers, brands and products. Research areas of focus include product and portfolio development, pricing, branding, segmentation and customer experience. The company offers a full suite of quantitative and qualitative research capabilities and works across industry groups. These include:

• Optimization and discrete choice modeling as it applies to product and service development, branding, packaging and pricing.
• Online communities that are managed and developed entirely in-house with a focus on improving participant engagement and with additional support for mobile participation.
• Hybrid research, which uses 20 in-house moderators, along with marketing science professionals and global project managers, to facilitate qualitative and quantitative research seamlessly.

39. The Link Group

Founded: 1994 2019 U.S. revenue: $34.2 million Percent change from 2018: 23.9% 2019 non-U.S. revenue: $0.3 million Percent from outside U.S.: 0.9% 2019 worldwide revenue: $34.5 million U.S. employees: 85

The Link Group executes research for Fortune 500 firms in the healthcare, retail, CPG and finance industries across both qualitative and quantitative methodologies and around the globe. TLG attributes its success to its core business philosophy: smarter research and better service. Its commitment to smarter research has allowed the company to take a creative, custom approach to its clients’ business needs that results in actionable and insightful reports. TLG delivers better service by maintaining a consistent research team across projects, allowing the team to anticipate and respond to client needs. This business philosophy has resulted in 99% of revenue coming from repeat clients.

This past year, TLG has continued to hone its research approaches to help elevate traditional research methods. For its messaging and positioning work, TLG developed a framework that triangulates quantitative survey data to determine how well messaging concepts will activate, communicate and engage the customer. In its segmentation studies, TLG blends science and art to create models that align with the client’s brand strategic vision by creating differences that are meaningful and actionable from a marketing perspective. TLG has leveraged its knowledge of behavioral economics to develop a validated, proprietary quantitative methodology—LinkEQ—that allows the company to reveal latent emotional associations.

Founded: 1983 2019 U.S. revenue: $34.3 million Percent change from 2018: -1.2% 2019 non-U.S. revenue: $1.2 million Percent from outside U.S.: 3.4% 2019 worldwide revenue: $35.5 million U.S. employees: 233

SSRS is a full-service market and survey research firm led by a core of dedicated professionals with advanced degrees in the social sciences. 

SSRS surveys support numerous media and academic partners looking to report on public attitudes and beliefs about a wide range of salient issues such as elections and public policy. SSRS is the polling partner for CNN, and conducts public opinion polling for ABC News, The Washington Post, Politico and CBS News. 

Beyond national polls, SSRS regularly conducts research at a state level, and among subpopulations such as Latinos and political partisans, and specializes in reaching hard-to-reach and low-incidence populations. SSRS has extensive experience in public policy, public affairs and health policy research. Since the Affordable Care Act was signed into law, SSRS has completed numerous studies surrounding its implementation and assessing Americans’ attitudes and experiences with the law. 

Since 2016, SSRS conducts the monthly Kaiser Family Foundation Health Tracking Poll. SSRS is well-known for its weekly telephone Omnibus poll. The firm also offers the SSRS Opinion Panel, which allows clients to conduct probabilistic surveys quickly at low cost. The SSRS/Luker on Trends Sports Poll is the first and longest-running tracking study focusing on sports in the U.S. 

37. BVA Group **

Founded: 1970 2019 U.S. revenue: $36 million Percent change from 2018: 2.6% 2019 non-U.S. revenue: $147 million Percent from outside U.S.: 80.3% 2019 worldwide revenue: $183 million U.S. employees: 120

BVA Group is a fast-growing research and consulting firm, an expert in behavioral science, ranked in the top 20 worldwide agencies. BVA brings data to life and converts deep understanding of customers and citizens into behavior change strategies. BVA operates both for public and private clients with methodologies fueled by data science and behavioral science. 

Its FMCG specialist—PRS IN VIVO—is a global leader in packaging and shopper research. PRS IN VIVO helps consumer marketers to succeed through: 

• In-store and online studies to better understand shopper behavior, in both physical and e-commerce shopping contexts.
• Qualitative studies to develop, screen and refine new product, packaging and merchandising concepts.
• Quantitative studies to pre-test and quantify new packaging, merchandising and display systems (for physical stores and e-commerce).
• Volume forecasting and product testing for both innovations and brand restages.
• “Nudge” initiatives to facilitate behavioral change, create new consumer habits and drive category growth. 

BVA Group is a European leader in customer experience research. More than 100 leading brands use BVA’s behavioral insights to provide seamless shopper journeys and design successful new products and services, including solutions from its multi-awarded Global Nudge-Unit.

36. radius | illumination

Founded: 1960 2019 U.S. revenue: $42 million Percent change from 2018: — 2019 non-U.S. revenue: $1 million Percent from outside U.S.: 2.3% 2019 worldwide revenue: $43 million U.S. employees: 127

Radius│illumination is the product of a merger between Radius Global Market Research and Illumination Research in 2018. Together, it’s one of the largest independent custom insights providers in the world. Its focus is on guiding brands at critical points along their growth journey, tackling issues such as identifying compelling innovations, creating relevant customer segmentations and developing strategies for deeper loyalty and engagement.

Radius | illumination partners with Fortune 500 leaders as well as challenger, disruptor and emerging brands in the U.S., Europe, Asia and the Middle East. Its top sectors include financial services, personal care, healthcare and pharmaceuticals, technology, home improvement and durables, media and entertainment, packaged foods, beverage, retail and transportation.

Its 2020 initiatives to fuel brand growth for its clients include:

• Provide agile and robust solutions such as InnovationSprint to accelerate new product and service development.
• Increase its information design capabilities so clients can easily take action on the results.
• Focus on driving deeper insights by combining its advanced analytics strength with immersive customer understanding in its designs.
• Expand solutions through the integration of new technologies and behavioral approaches.

35. Market Force **

Founded: 2005 2019 U.S. revenue: $50 million Percent change from 2018: 2% 2019 non-U.S. revenue: $7 million Percent from outside U.S.: 12.3% 2019 worldwide revenue: $57 million U.S. employees: 375

Market Force Information provides location-level customer experience management solutions to protect clients’ brand reputation, delight their customers and make them more money. 

Market Force operates at scale across the globe. Each month, the company:

• Completes more than 100,000 mystery shops.
• Collects, processes and analyzes millions of employee and customer experience surveys.
• Manages more than 100,000 inbound calls to its contact center.
• Hosts more than 1 million user logins on its KnowledgeForce reporting platform.

Market Force’s multi-location solutions provide a robust framework for measuring and improving operational excellence, customer experience and financial KPIs. Measurement channels include mystery shopping, customer experience surveys, contact center calls, social media and employee engagement surveys via the KnowledgeForce technology platform and Eyes:On mobile app. Market Force employs predictive analytics to determine what matters most and the ROI for investing in improvements. The firm takes a dual-headed approach to market research services (e.g., customer segmentation, attitude trial and usage studies and custom research projects) and strategic advisory services to design and implement effective measurement systems and improve performance.

Founded: 1991 2019 U.S. revenue: $52 million Percent change from 2018: 4% 2019 non-U.S. revenue: $6 million Percent from outside U.S.: 10.3% 2019 worldwide revenue: $58 million U.S. employees: 400

As a leading customer experience management firm, SMG helps clients get smarter about their customers and employees to drive changes that boost customer loyalty and improve business performance. SMG combines technology and services to collect, analyze and share feedback and behavioral data, so it’s easier for clients to deliver and activate customer insights across their enterprise.

SMG partners with more than 350 brands around the globe to create better customer and employee experiences, which drive loyalty and performance. SMG uniquely combines technology and insights to help clients listen better, act faster and outperform competitors. SMG is a technology-enabled research firm with a global footprint—evaluating more than 150 million surveys annually, in 50 languages across 125 countries. 

Strategic solutions include omniCXTM, Brand Research and Employee Engagement. SMG’s omniCX solution uses multiple research methodologies in capturing solicited and unsolicited consumer feedback across in-store, online, contact center and social channels. Results are aggregated and reported via smg360TM—a real-time, role-based reporting platform providing access to all customer and related data. 

SMG’s research professionals partner with clients to derive business-changing insights. Within Brand Research, SMG offers traditional brand tracking as well as access to dynamic customer and competitor data through market intelligence tool BrandGeek. Fueled by SurveyMini—SMG’s location-based mobile research app—BrandGeek contains consumer feedback and behavioral data relating to more than 4,500 brands across more than 500,000 locations.

33. Hanover Research

Founded: 2003 2019 U.S. revenue: $52.7 million Percent change from 2018: 14.1% 2019 non-U.S. revenue: $2.6 million Percent from outside U.S.: 4.7% 2019 worldwide revenue: $55.3 million U.S. employees: 358

Hanover Research is a brain trust designed to level the information playing field. Hanover is made up of hundreds of researchers who support thousands of organizational decisions every year. One of the industry’s fastest-growing companies, Hanover attributes this market success to its unique positioning as the only firm that provides tailored research through an annual, fixed-fee model. 

Hanover serves more than 1,000 organizations and companies worldwide from established global organizations, to emerging companies to educational institutions. Hanover’s research informs decisions at any level and across any department capitalizing on the exposure to myriad industries and challenges. 

Founded in 2003, Hanover operates on an annual fixed-fee model, and partnership provides its clients with access to a team of high-caliber researchers, survey experts, analysts and statisticians with diverse skills in market research, information services and analytics. There is no limit on the type of challenge that can be asked for on the quantitative and qualitative approaches Hanover uses to deliver solutions—most of which are very difficult to replicate internally.

Hanover’s custom research services include:

• Secondary research: market segmentation and evaluation; labor and demographic trends and forecasts; vendor and product reviews; best practices reports. 
• Survey: survey design, administration and analysis; open-ended response coding. 
• Qualitative primary research: focus group design and administration; in-depth interview design, outreach, administration and analysis. 
• Data analysis: data segmentation and mining; conjoint analysis; linear regression; descriptive and predictive analytics; data forecasting and modeling. 

32. Directions Research

Founded: 1988 2019 U.S. revenue: $54.2 million Percent change from 2018: 17.8% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $54.2 million U.S. employees: 181

Independently recognized as one of the leading business decision insight firms in the nation, Directions Research combines a highly experienced staff with a unique mix of innovative and proven approaches to answer pressing business issues. Directions and SEEK routinely combine primary and connected data from multiple sources to create holistic and actionable analytic stories for their clients. Through digital dashboards, infographics, written reports and other unique visualizations, the firm communicates its knowledge in a manner that is right for today’s leaders. 

Directions and SEEK excel in innovation, optimization, customer and brand experience, brand strategy, strategic business intelligence and visualization across a wide range of industries. The firm offers B2C and B2B services globally, surveying audiences using a broad selection of data collection techniques and combining those insights with existing client knowledge. Directions’ and SEEK’s staff have an excellent mix of client- and supplier-side experience. The organization allows senior researchers to work with clients on a day-to-day basis.

SEEK (acquired in 2018) is a qualitative insight and innovation consultancy, operating as an independent but connected division of Directions. SEEK empathically connects brands with the humans they serve, transforming the brand-to-consumer relationship into a human-to-human one. The SEEK approach builds brand advocacy for clients with the human-centric approach to innovation, activating empathy as an innate problem-solving capability.

31. Fors Marsh Group (FMG) *

Founded: 2002 2019 U.S. revenue: $57.5 million Percent change from 2018: 22.1% 2019 non-U.S. revenue: — Percent from outside U.S.: — 2019 worldwide revenue: $57.5 million U.S. employees: 263

FMG applies behavioral and data science to improve organizational processes, business solutions and customer experiences. This work is conducted within seven core U.S. markets: health, defense, technology, finance, homeland security, policy and consumer. 

FMG’s work for its clients wins industry and federal awards. FMG has been named as a top market research company by GreenBook and the American Advertising Federation and has been named to the American Marketing Association’s list of top market research companies in the U.S. for five consecutive years. FMG was also a finalist for the American Council for Technology and Industry Advisory Council’s Igniting Innovation 2018 award for creating an innovative e-learning program that improved program awareness and usability for the General Services Administration’s Center for Acquisition Professional Excellence. 

For 2019 and beyond, FMG is focused on continuing this momentum and expanding in important areas. In its human capital practice, FMG is furthering its work in the cybersecurity industry to help the Department of Defense attract top cyber talent and to protect the nation’s infrastructure. FMG is also expanding its efforts in public service recruiting through new partnerships with the U.S. Army, U.S. National Guard and AmeriCorps. The company is proud that its partnership with these institutions will help shape the future of the U.S. For its health division, FMG is leveraging its deep experience in health communications to fight the opioid crisis by reducing stigma and removing barriers that victims face in receiving help—potentially one of the biggest challenges facing America today. 

30. National Research Group (NRG) **

Founded: 1978 2019 U.S. revenue: $59 million Percent change from 2018: 1.7% 2019 non-U.S. revenue: $4 million Percent from outside U.S.: 6.3% 2019 worldwide revenue: $63 million U.S. employees: 200

National Research Group, acquired by Stagwell Media from Nielsen in 2015, is a leading global insights and strategy firm at the intersection of entertainment and technology. Rooted in four decades of industry expertise, the world’s leading marketers turn to NRG for insights into growth and strategy for any content, anywhere, on any device. Working at the confluence of content, culture and technology, NRG offers bold insights for storytellers everywhere. 

Some agencies specialize in qual, others focus on quant—but NRG connects the two disciplines with hybrid teams expert in both modalities. The company is a one-stop, custom consultancy that tailors its approach to solve clients’ biggest challenges. 

The foundation of NRG’s qualitative work is a team of passionate, subject matter experts who connect deeply with consumers in any environment. NRG uses qual to discover the subconscious drivers that fuel our quantitative truths. Its quantitative work is anchored in sophisticated techniques with a focus on agility, creativity and rigor. NRG is method-agnostic and works collaboratively with its clients to solve complex problems in a simple way.

29. Cello Health * **

Founded: 2004 2019 U.S. revenue: $64.5 million Percent change from 2018: 23.3% 2019 non-U.S. revenue: $58.5 million Percent from outside U.S.: 47.6% 2019 worldwide revenue: $123 million U.S. employees: 260

Cello Health consists of four global capabilities that enable the company to offer best-in-class services and an integrated partnership approach to its clients. This unique mix of capabilities, combined with its collaborative approach, results in a unique fusion of expertise, providing powerful advisory and implementation solutions.

• Cello Health Insight is a global marketing research company, providing business intelligence to the healthcare and pharmaceutical sectors. Cello Health Insight specializes in getting to the heart of its clients’ questions, using a large pool of creative and academic resources and providing design of materials and deliverables through a hand-picked project team—selected to best meet the needs of each individual project.
• Cello Health Consulting is the strategic consulting arm of Cello Health, focused on delivering business results by unlocking the potential within organizations, people, assets and brands. Cello Health Consulting works alongside clients to create practical solutions that ensure buy-in and build relationships. 
• Cello Health Communications combines science, strategy and creativity to unlock the potential of brands and assets. Its services underpin differentiated positioning and deliver brand optimization, focusing on multiple areas of development and launch, through commercial maturity.
• Cello Signal is a full-service digital capability bringing impactful messages alive in communications campaigns, content and film.

28. Macromill Group **

Founded: 2000 2019 U.S. revenue: $68.5 million Percent change from 2018: 2.2% 2019 non-U.S. revenue: $260 million Percent from outside U.S.: 79.1% 2019 worldwide revenue: $328.5 million U.S. employees: 275

Macromill Group is a rapidly growing global market research and digital marketing solutions provider bringing together the collective power of its specialist companies to provide innovative data and insights that drive clients’ smarter decisions. Macromill’s industry-leading digital research solutions deliver rapid and cost-effective solutions to the challenges businesses face today. 

The group’s leading business units are Macromill and MetrixLab. Macromill stands at the forefront of innovation, delivering unique marketing solutions. It offers exclusive access to the highest-quality online panels with more than 2 million members. Using its self-developed platform AIRs, Macromill provides full-service online research including automated survey creation and completion, data tabulation and analysis. Today, its business portfolio includes services such as offline quantitative research, mobile research, point-of-service database research (QPR), digital marketing (Accessmill), a DIY survey platform (Questant) and more. 

Metrixlab turns data from online surveys, social media, mobile devices and enterprise systems into valuable business information and actionable consumer insights. This helps leading companies drive product innovation, brand engagement and customer value. Owned and group panels provide expansive access to global respondents in mature and emerging markets. Its teams deliver strategic and tactical decision support by pushing the boundaries of data analysis innovation, combining cutting-edge technology with data science and proven marketing research methodologies. Clients across the globe rely on the company’s hyper-efficient data and insights ecosystem to deliver fast and affordable results.

27. C Space **

Founded: 1999 2019 U.S. revenue: $70 million Percent change from 2018: 2.9% 2019 non-U.S. revenue: $18 million Percent from outside U.S.: 20.5% 2019 worldwide revenue: $88 million U.S. employees: 354

C Space, part of the Interbrand Group, is a global customer agency that marries art and science to create rapid customer insight and business change. 

C Space works with some of the world’s best-known brands—such as Walmart, Samsung, IKEA and more—to build customers into the ways companies work and deliver on customer-inspired growth. By building real, ongoing relationships with customers—online and in-person—brands can stay relevant, deliver superior experiences, launch successful products and build loyalty. Through its “customer as a service” approach of research, consulting and communications, C Space helps businesses minimize risk and maximize growth.

The company integrates customers into the ways its clients work. By bringing stakeholders together around the customer, C Space’s clients create greater clarity and alignment in the actions that will most effectively drive customer growth.

C Space’s customized programs are tailored based on specific business needs and include private online communities, immersive storytelling, data and analytics, activation events, innovation projects and business consulting. C Space continues to invest in its people, existing capabilities like data and analytics, as well as new initiatives.

26. Engine Insights**

Founded: 2004 2019 U.S. revenue: $71 million Percent change from 2018: 4.4% 2019 non-U.S. revenue: $44 million Percent from outside U.S.: 38.3% 2019 worldwide revenue: $115 million U.S. employees: 240

Engine is a new kind of data-driven marketing solutions company. Powered by data, driven by results and guided by people, Engine helps its clients make connections that count—leading to bottom-line growth, an inspired workplace and business transformation. 

Engine Insights (formerly ORC International) connects traditional market research with cutting-edge products to deliver clients a 360-degree view of their customers, employees and markets. Engine’s extended suite of solutions and products are designed to support business growth, from helping clients understand and outperform the competition to operationalizing both survey and behavioral data to identify, attract, engage and retain their audiences.

Engine Insights’ client services and products include custom research and omnibus surveys; customer experience, customer retention and brand engagement studies; and data management and data analytics. 

These services help clients:

• Think beyond products and services to drive business revenue.
• Use insights to inform more relevant messaging and creative.
• Get a complete 360-degree view of their customers.
• Segment audiences for better targeting.
• Develop the perfect product and take it to market.
• Create unique experiences that engage their customers and keep them loyal for a lifetime.
• Build an internal culture that attracts, retains and engages the best talent.

Founded: 1931 2019 U.S. revenue: $71.1 million Percent change from 2018: 9% 2019 non-U.S. revenue: $6.9 million Percent from outside U.S.: 8.8% 2019 worldwide revenue: $78 million U.S. employees: 253

Since 1931, Burke has consistently redefined expectations in the marketing research industry. From segmentation to customer engagement programs, product innovation and brand tracking, Burke prides itself on designing and executing objectives-driven quantitative and qualitative research. Working across a variety of industries, Burke helps its clients gain actionable perspective on their most critical business challenges, providing a range of solutions from agile to integrated strategic decision support.

Today, Burke continues to push the boundaries of what marketing research can be, seamlessly uniting research, strategy and education. Backed by Seed Strategy—its strategic consulting subsidiary—Burke has the capabilities to support its clients throughout every phase of the product or service life cycle, with expertise in strategy, innovation, branding and marketing. In addition, Burke provides comprehensive training on research fundamentals and best practices through the Burke Institute—its dedicated education division and the industry’s leader in research and insights training. Wherever its clients find themselves on the path to success, Burke is uniquely equipped to help them move forward with clarity, confidence and purpose.

Continuing its long tradition of research innovation, Burke recently unveiled two new offerings: Geode|AI, an integrated insights system that analyzes multiple data sources to uncover patterns, relationships and critical insights that are often hidden; and Quantiment, a robust machine-learning solution that jointly extracts richer insights from structured and unstructured data.

24. YouGov *

Founded: 2000 2019 U.S. revenue: $76.8 million Percent change from 2018: 11.8% 2019 non-U.S. revenue: $107.5 million Percent from outside U.S.: 58.3% 2019 worldwide revenue: $184.3 million U.S. employees: 212

YouGov is a global provider of analysis and data generated by consumer panels in 42 markets. Its core offering of opinion data is derived from the proprietary YouGov Global Panel of more than 9 million people. The YouGov Global Panel provides the company with thousands of data points on consumer attitudes, opinions and behavior. YouGov captures these streams of data in the YouGov Cube, its unique connected data library that holds more than 10 years of historic single-source data. In 2019, YouGov panelists completed more than 25 million surveys.

YouGov’s data-led offering supports and improves a wide spectrum of marketing activities of a customer base, including media owners, brands and media agencies. YouGov works with some of the world’s most recognized brands.

Its syndicated data products include the daily brand perception tracker, YouGov BrandIndex and the media planning and segmentation tool YouGov Profiles. Its market-leading YouGov RealTime service provides a fast and cost-effective solution for reaching nationally representative and specialist samples. YouGov’s Custom Research division offers a wide range of quantitative and qualitative research, tailored by sector specialist teams to meet users’ specific requirements. YouGov data is delivered through Crunch, the most advanced analytics tool for research data, combining fast processing with drag-and-drop simplicity. YouGov has a strong record for data accuracy and innovation. 

23. Phoenix Marketing International

Founded: 1999 2019 U.S. revenue: $77 million Percent change from 2018: -3.8% 2019 non-U.S. revenue: $4.5 million Percent from outside U.S.: 5.5% 2019 worldwide revenue: $81.5 million U.S. employees: 343

Global advertising and brand specialist Phoenix Marketing International operates in all major industries, utilizing modern technology, innovative research techniques and customized approaches to help clients elevate their brand, refine their communications and optimize their customer experience. 

With the launch of Phoenix’s AdPi Brand Effect Platform, clients now have access to continuous advertising measurement and performance improvement insights through a single platform, providing the ability to analyze their campaigns at any stage in the advertising life cycle, and the flexibility to draw upon each piece as needed. Through more than 20 years of experience and testing thousands of ads per month, Phoenix developed 19 category-specific ad measurement models that uncover the drivers and creative attributes that explain the “whys” behind an ad’s creative performance, with forward-looking estimates for ad memorability and brand linkage.

Phoenix continues to evolve its CX solution, launching Competitive Customer Experience, a measurement of how consumers perceive their overall experience with a brand, including key touchpoints along the journey. Grounding recent experiences with a client’s brand, competitor brands and non-categorical benchmarking, Phoenix is able to evaluate brand opinion, understand what drives great CX outside of the category, focus on emotional drivers of brand CX, and provide an external view of culture, consistency and brand promises.

22. Concentrix **

Founded: 1983 2019 U.S. revenue: $95 million Percent change from 2018: 11.8% 2019 non-U.S. revenue: $130 million Percent from outside U.S.: 57.8% 2019 worldwide revenue: $225 million U.S. employees: 253

Concentrix is a wholly owned subsidiary of SYNNEX Corp., specializing in technology-enabled customer engagement and improving business performance for clients around the world. With more than 225,000 staff in more than 40 countries, Concentrix provides services to clients in 10 industry verticals: automotive, banking and financial services, insurance, healthcare, technology, consumer electronics, media and communications, retail and e-commerce, travel and transportation, energy and the public sector. 

The Concentrix Voice of the Customer solution combines technology with experience management services provided by its in-house team of hundreds of CX professionals. 

Powered by analytic tools and artificial intelligence, its customer feedback platform ConcentrixCX helps companies listen, analyze and act on omnichannel customer feedback at any point in the customer journey, at scale. Features include data capture and integration, real-time reporting and analytics, and coaching and employee engagement tools. Concentrix continues to invest in enhanced platform functionality—for example, multi-source data expansion of its proprietary text analytics engine, including structured and unstructured customer feedback sources such as surveys, social, messaging, complaints and email. New digital data collection capabilities include a conversational feedback bot and embedded micro-journey surveys. 

Concentrix experience management services range from program management to strategic advisory services and are custom tailored to free clients’ internal teams to focus on transformational impact. Its CX experts specialize in quantitative and qualitative techniques, delivering data-driven insights through solutions such as survey design, relational loyalty research, CX journey analytics, digital channel optimization, customer segmentation, customer effort assessment and integrated CX analytics.

21. Escalent

Founded: 1975 2019 U.S. revenue: $97.1 million Percent change from 2018: -3.4% 2019 non-U.S. revenue: $5.5 million Percent from outside U.S.: 5.4% 2019 worldwide revenue: $102.6 million U.S. employees: 352

Escalent is a human behavior and analytics firm specializing in industries facing disruption. The company transforms data and insights into an understanding of what drives human behavior, and it helps businesses turn those drivers into actions that build brands, enhance customer experiences and inspire product innovation. 

Escalent specializes in automotive and mobility, consumer and retail, energy, financial services, health, technology and telecommunications. Focusing on select industries allows Escalent to function as a trusted business partner who knows the challenges its clients face and understands how to engage their most valuable audiences. 

Escalent has three centers of excellence: Qualitative Research combines emerging technologies, anthropology and ethnography to tap into human insights that reveal real needs and potential; Marketing & Data Sciences combine survey, behavioral, transactional and third-party data to solve tough research challenges; and Insight Communities provides private, online platforms for brands to engage with groups of stakeholders to quickly and easily draw insights.

20. dunnhumby **

Founded: 2001 2019 U.S. revenue: $100 million Percent change from 2018: -3.8% 2019 non-U.S. revenue: $335 million Percent from outside U.S.: 77% 2019 worldwide revenue: $435 million U.S. employees: 230

Dunnhumby is a customer science company that analyzes data and applies insights for almost 1 billion shoppers across the globe to create personalized customer experiences in digital, mobile and retail environments. Its strategic process, proprietary insights and multichannel media capabilities build loyalty with customers to drive competitive advantage and sustained growth for clients. Dunnhumby uses data and science to understand customers, then applies that insight to create personalized experiences that build lasting emotional connections with retailers and brands. It’s a strategy that demonstrates when companies know and treat their customers better than the competition, they earn more than their loyalty—they earn a competitive advantage.

Dunnhumby was established in the U.S. to help retailers and manufacturers put the customer at the heart of their business decisions. Analyzing data from millions of customers across the country, dunnhumby enables clients to use this insight to deliver a better shopping experiences and more relevant marketing to their customers.

By putting best customers at the center of every decision, dunnhumby’s approach delivers measurable value, competitive edge and even more customer data to fuel ongoing optimization, setting clients up for long-term success.

Dunnhumby serves a prestigious list of retailers and manufacturers in grocery, consumer goods, health, beauty, personal care, food service, apparel and advertising, among others. Clients include Tesco, Procter & Gamble, Coca-Cola, Macy’s and PepsiCo.

19. Informa Financial Intelligence**

Founded: 2016 2019 U.S. revenue: $107 million Percent change from 2018: 1.9% 2019 non-U.S. revenue: $36 million Percent from outside U.S.: 25.2% 2019 worldwide revenue: $143 million U.S. employees: 500

Informa Financial Intelligence is a leading provider of business intelligence, market research and expert analysis to the financial industry. The world’s top global financial institutions and banks look to Informa Financial Intelligence for its authority, precision and forward-focused analysis. 

Informa Financial Intelligence consists of key research, analysis and industry experts, such as Informa Research Services, EPFR Global, Informa Global Markets, iMoneyNet, Informa Investment Solutions, eBenchmarkers and Mapa Research.

Informa Financial Intelligence provides fund and wealth managers, traders, insurers, analysts, and investment and retail bankers with the intelligent advantage to make informed decisions, understand past trends, forecast future performance, drive profitability and increase returns.

Because of their strong background in the financial industry, the research teams of Informa Financial Intelligence are highly qualified to help financial institutions with their market research needs. Informa’s researchers are experts in benchmarking studies, competitive intelligence, new product development and usability testing, customer and member satisfaction and loyalty research, brand and advertising awareness research, and mystery shopping services for sales and service quality evaluation, legal and match pair testing, compliance, discrimination and misleading sales practices testing. Informa is considered a leader in the use of market research to limit the risk associated with allegations of discrimination, UDAAP (unfair, deceptive, or abusive acts or practices), predatory lending and misleading sales practices.

18. NRC Health

Founded: 1991 2019 U.S. revenue: $113 million Percent change from 2018: 10.8% 2019 non-U.S. revenue: $3.6 million Percent from outside U.S.: 3.1% 2019 worldwide revenue: $128 million U.S. employees: 448

NRC Health (formerly National Research Corp.) has helped healthcare organizations illuminate and improve the moments that matter to patients, residents, physicians, nurses and staff for more than 38 years. The company offers performance measurement and improvement services to hospitals, healthcare systems, physicians, health plans, senior care organizations, home health agencies and other healthcare organizations. 

NRC Health solutions help organizations stay at the forefront of healthcare by understanding the totality of healthcare consumer and staff experiences. Primary solutions include: 

• Experience solutions capture personal experiences, while delivering insights to power a new benchmark: n=1. Developing a longitudinal profile of customers’ healthcare wants and needs allows for organizational improvement, increased provider and staff engagement, loyal relationships and personal well-being. 
• The Loyalty Index, composed of seven aspects that combine to provide a 360-degree view of healthcare consumer loyalty—a single, trackable metric to identify emerging trends in consumer behavior and benchmark against peers. 
• Market Insights is a large U.S. consumer database that gives partners access to the opinions of 310,000 healthcare consumers in 300 markets, and access to resources to better understand target audiences and gauge consumer response to communications.
• The Transparency solution calculates star ratings from existing patient, resident and family survey data, and publishes those ratings to organizations’ websites. 
• The Governance Institute supports the efforts of healthcare boards across the nation—to lead stronger organizations and build healthier communities. NRC Health partners with organizations to improve governance efficiency and effective decision-making by providing trusted, independent information, tools and resources to board members, executives and physician leaders. 

17. MaritzCX **

Founded: 1973 2019 U.S. revenue: $118 million Percent change from 2018: — 2019 non-U.S. revenue: $44 million Percent from outside U.S.: 27.2% 2019 worldwide revenue: $162 million U.S. employees: 600

MaritzCX is a software and research company that focuses on customer experience management for big business. The company offers a unique combination of award-winning CX software, industry-leading data and research science, deep vertical market expertise and managed program services. MaritzCX provides a full-service professional CX approach designed to continuously improve the customer experience across an enterprise’s customers, employees, prospects and partners. 

MaritzCX’s research insights include its leading CXStandards competitive benchmarking research that delivers quarterly benchmarks for 55 CX categories across 16 industries. Its CXEvolution study of more than 10,000 practitioners’ feedback informed large enterprises of their CX gaps. 

The company’s focus is to leverage the MaritzCX platform, its industry-leading studies and research services to drive more meaningful experiences between its clients and their customers by adding product and research services and continued thought leadership in the CX market. In addition, MaritzCX has received CMS-certification for HCAHPS surveys, becoming the industry’s first CX platform company to offer an inclusive CX-based patient experience platform.

MaritzCX specializes in solutions for key industries, including automotive, financial services, retail, technology, B2B and more. Its global reach includes more than 900 full-time employees and 800-plus part-time or contract employees in 19 offices around the world. MaritzCX provides solutions to more than 500 clients and 1.6 million users who speak 72 languages in 100 countries. MaritzCX is committed to being its clients’ customer experience research partners.

In March 2020, InMoment acquired MaritzCX.

16. DRG (Decision Resources Group) **

Founded: 1990 2019 U.S. revenue: $140 million Percent change from 2018: 2.2% 2019 non-U.S. revenue: $53 million Percent from outside U.S.: 27.5% 2019 worldwide revenue: $193 million U.S. employees: 399

DRG, the Health Science & Analytics Division of Piramal Enterprises, is a global information and technology services company that provides proprietary data and solutions to the healthcare industry. DRG has brought together best-in-class companies to provide end-to-end solutions to complex challenges in healthcare. DRG reframes these challenges, enabling its customers to see the opportunities. Pharmaceutical, biotechnology, medical technology and managed care companies rely on this analysis and data to make informed decisions critical to their success.

Framing the current status and future trends in target healthcare markets using data, primary research and secondary research is a core competency of DRG. Product offerings include high‐value analytics, syndicated research, proprietary databases, decision support tools and advisory services.

DRG has a number of key specialties, including syndicated research focused on new therapeutic opportunities; portfolio planning, changing industry dynamics and global treatment patterns; insights and data on physician and consumer healthcare e‐marketing; and proprietary databases and analytics covering more than 90% of the U.S. managed care markets. 

15. Wood Mackenzie **

Founded: 1973 2019 U.S. revenue: $150 million Percent change from 2018: 3.4% 2019 non-U.S. revenue: $335 million Percent from outside U.S.: 69.1% 2019 worldwide revenue: $485 million U.S. employees: 337

Wood Mackenzie, a Verisk business, is a leading research and consultancy business for the global energy, chemicals, metals and mining industries. Wood Mackenzie launched in 1923 as a small, relatively unknown, Edinburgh, Scotland-based stockbroker. By the 1970s, it had become one of the top three stockbrokers in the UK, renowned for the quality of its equity research. 

Its success has always been underpinned by the clear and simple principle of providing trusted research and advice that would make a difference to clients. This was true when the first oil report was published by its equity analysts in 1973 and remains just as relevant to it today. So much so that, over the past four decades, Wood Mackenzie has drawn upon its heritage to create a global research and consultancy business that has grown alongside the needs of its clients. 

Having cultivated deep expertise in upstream oil and gas, Wood Mackenzie has carefully broadened its focus to deliver the same level of detailed insight for every interconnected sector of the energy, chemicals, metals and mining industries it now serves around the world. But heritage is more than just history. Its expert analysts and consultants have connected the company to some of the most significant events of our time—creating insight for governments, boards and CEOs who have helped shape the future direction of the world’s natural resources industries and their impact on society. 

14. Material *

Founded: 1973 2019 U.S. revenue: $166.7 million Percent change from 2018: 0.3% 2019 non-U.S. revenue: $57.9 million Percent from outside U.S.: 25.8% 2019 worldwide revenue: $224.6 million U.S. employees: 1,038

In 2019, Material (under the name LRW Group) acquired five companies: Killer Visual Strategies, an award-winning visual communication agency based in Seattle; Greenberg Strategy, a Bay Area research and strategy consultancy with a strong presence in the tech community; Karma Agency, a strategic communications firm based in Philadelphia; Salt Branding, a Bay Area consultancy; and T3, an Austin, Texas-based digital marketing agency. This year, Material is taking steps to unify these companies under one brand, integrating their services and building a collaboration that will provide seamless, end-to-end marketing solutions for clients. This year, LRW Group rebranded as Material, formally integrating 10 companies into one modern, unified offering.

Material is a radical collaboration of the top research and analytics firms seamlessly paired with the most creative and strategic marketing agencies, all with the shared mission of igniting growth for the world’s top B2B and B2C brands, from Fortune 500 companies to disruptive start-ups. Material offers a full range of marketing services—from data analytics and insights, to consulting and strategy development, to customer experience programs and creative executions. Material employs a roster of 1,200 strategists, creators, technologists, designers, researchers and storytellers that work side-by-side with clients to solve modern-day problems, build customer loyalty and make an impact on the world around us.

Founded: 1969 2019 U.S. revenue: $173.7 million Percent change from 2018: 0.5% 2019 non-U.S. revenue: $52.6 million Percent from outside U.S.: 23.2% 2019 worldwide revenue: $226.3 million U.S. employees: 5,311

ICF is a global consulting services provider with more than 7,000 professionals focused on making big things possible for its commercial and government clients in the U.S., Europe and Asia. 

Clients work with ICF on issues that matter profoundly to their success, whether it’s a product or program that matters to the business or a social issue or policy that matters to the world. ICF offers comprehensive survey research services that empower clients to gain valuable and actionable insights on issues that matter. 

For more than 40 years, ICF has demonstrated design, methodological and statistical knowledge through the implementation of large and complex survey research projects. Its clients consist of U.S. federal, state and local agencies, universities, nonprofits and commercial organizations. 

Its survey research services include: 

• Analyzing, reporting and presenting findings.
• Conducting surveys through a variety of data collection methods. 
• Designing samples, data collection protocols and instruments.
• Protecting all processes and data through quality assurance and system security.

ICF recently completed the installation of a state-of-the-art, fully integrated and security-enhanced data collection system, allowing the company to securely and most efficiently collect survey research data across all modes. ICF continues to be dedicated to solving the world’s most complex challenges and tackle problems with ingenuity on issues that matter profoundly to its clients.

12. J.D. Power **

Founded: 1968 2019 U.S. revenue: $217 million Percent change from 2018: 3.3% 2019 non-U.S. revenue: $113 million Percent from outside U.S.: 34.2% 2019 worldwide revenue: $330 million U.S. employees: 744

J.D. Power is a global leader in consumer insights, advisory services and data and analytics. Those capabilities enable J.D. Power to help its clients drive customer satisfaction, growth and profitability. J.D. Power offers market research, forecasting, consulting, training and consumer surveys of product and service quality, customer satisfaction and buyer behavior. The company’s independent industry benchmark studies, innovative data and analytics products, and customized advisory services provide insights and help companies improve quality, engagement and business performance.

Annual syndicated studies are based on survey responses from millions of consumers and business customers worldwide. The firm does not review, judge or test products and services for its syndicated studies. It relies on the opinions and perspectives of consumers who have used the products and services being rated. 

J.D. Power is most often recognized for its work in the automotive industry, where its metrics have become the industry standard for measuring product quality and customer satisfaction. A team of associates worldwide conducts quality and customer satisfaction research across industries including automotive, financial services, insurance, telecommunications, travel, healthcare utilities and consumer electronics. 

11. Forrester Research Services **

Founded: 1983 2019 U.S. revenue: $233.7 million Percent change from 2018: 32.9% 2019 non-U.S. revenue: $65 million Percent from outside U.S.: 21.8% 2019 worldwide revenue: $298.7 million U.S. employees: 525

Forrester Research Services is the research component of Forrester, one of the most influential research and advisory firms in the world. Forrester works with business and technology leaders to develop customer-obsessed strategies that drive growth. Its unique insights are grounded in annual surveys of more than 675,000 consumers and business leaders worldwide, rigorous and objective methodologies, and the shared wisdom of its most innovative clients. 

Forrester’s research offerings consist of a library of cross-linked documents that interconnect its playbooks, reports, data, product rankings, best practices, evaluation tools and research archives. Research access is provided through role-based websites that facilitate client access to research and tools that are most relevant to their professional roles, including community tools that allow interaction between and among clients and analysts.

Forrester’s research and decision tools enable clients to better anticipate and capitalize on the disruptive forces affecting their businesses and organizations, providing insights and frameworks to drive growth in a complex and dynamic market. 

Founded: 1934 2019 U.S. revenue: $320 million Percent change from 2018: 3.2% 2019 non-U.S. revenue: $1,280 million Percent from outside U.S.: 80% 2019 worldwide revenue: $1,600 million U.S. employees: 860

GfK connects data and science. Innovative research solutions provide answers for key business questions around consumers, markets, brands and media—now and in the future. As a research and analytics partner, GfK promises its clients all over the world “Growth from knowledge.” 

The increasing speed of product innovation, the rise of new channels and emerging customer needs are all part of business today. GfK’s clients are businesses around the globe. To make the best possible business decisions every day, they need more than purely descriptive data—they require actionable recommendations based on advanced analytics and powered by leading-edge technology. GfK is in the unique position to leverage proprietary and third-party data to create indispensable predictive market and consumer insights and recommendations.

GfK’s industry focus provides its market researchers with a thorough understanding of business issues and questions specific to their concerns. Industries covered include automotive, consumer goods, fashion and lifestyle, media and entertainment, retail, technology, and travel and hospitality.

9. comScore * **

Founded: 1999 2019 U.S. revenue: $336.1 million Percent change from 2018: -6.5% 2019 non-U.S. revenue: $52.5 million Percent from outside U.S.: 13.5% 2019 worldwide revenue: $388.6 million U.S. employees: 870

ComScore is a global information and analytics company that measures advertising, content and the consumer audiences of each across media platforms. ComScore creates its products using a global data platform that combines information on digital platforms (smartphones, tablets and computers), television and movie screens with demographics and other descriptive information. 

ComScore has developed proprietary data science that enables measurement of person-level and household-level audiences, removing duplicated viewing across devices and over time. This combination of data and methods enables a common standard for buyers and sellers to transact on advertising. This helps companies across the media ecosystem better understand and monetize their audiences and develop marketing plans and products to more efficiently and effectively reach those audiences. ComScore’s ability to unify behavioral and other descriptive data enables it to provide audience ratings, advertising verification and granular consumer segments that describe hundreds of millions of consumers. 

ComScore offers several solutions to help advertisers maximize cross-platform marketing effectiveness—be it measuring brand impact, viewability or ad and audience delivery validation—as well as power cross-platform advertising for better targeting and stronger advertising ROI. ComScore Advanced Audience segments go beyond age and gender to help advertisers better target consumers based on lifestyles, behaviors, demographics and interests. ComScore pioneered this concept in digital, local and national TV. 

8. The NPD Group

Founded: 1966 2019 U.S. revenue: $339.5 million Percent change from 2018: 8.6% 2019 non-U.S. revenue: $104.5 million Percent from outside U.S.: 23.5% 2019 worldwide revenue: $444 million U.S. employees: 1,185

NPD’s global information and advisory services help the world’s leading brands achieve data-driven growth. NPD combines data, industry expertise and prescriptive analytics across more than 20 industries to help its clients measure markets, predict trends and improve performance.

NPD syndicated services include retail tracking, distributor tracking and consumer tracking. NPD offers weekly data, store-level enabled data for looking at geographies or custom store groupings and account-level information for participating retailers. Point-of-sale data is collected from more than 600,000 doors worldwide, plus e-commerce and mobile platforms. Consumer information is collected via online surveys and NPD’s Checkout service, which uses receipt harvesting to track and analyze purchasing and behavior. Prescriptive analytics include market forecasting, new product forecasting, pricing and promotion evaluation and segmentation. 

With deep expertise in more than 20 industries, NPD provides thought leadership to the C-suites of many of the world’s leading brands. Senior industry advisors are available for strategy sessions to guide long-range planning or address specific needs, such as preparing for earnings calls. Topics include industry and category performance, the state of retail and winning strategies of best-in-class companies.

7. Westat **

Founded: 1963 2019 U.S. revenue: $590 million Percent change from 2018: 3.5% 2019 non-U.S. revenue: $7 million Percent from outside U.S.: 1.2% 2019 worldwide revenue: $597 million U.S. employees: 1,900

Westat is a 100% employee-owned research and professional services company. Westat provides extensive survey design and operations capabilities in support of modern data collection from households, institutions, businesses and individuals. Westat applies multiple modes of data collection and survey management to achieve maximum response rates.

The company’s focus areas and capabilities include: 

• Statistical analysis and methodological research in survey design, experiments and testing, data science and analytics, statistical disclosure control and qualitative research.
• Program, process and outcome evaluation using diverse methodologies from design to implementation to guide each program to success.
• Health research, including behavioral and mental health, clinical studies and clinical trials, public and international health, healthcare delivery, patient safety and health communications campaigns.
• Social policy research and technical assistance for implementing innovative evaluation, quality improvement and service delivery systems.
• Education programs for supporting teachers, conducting evaluations and providing technical assistance.
• Transportation studies of travel behaviors, safety and human factors using advanced technologies such as instrumented vehicles and simulators, field observational studies, and online and mobile device-based surveys.

To support its research projects, Westat designs tailor-made approaches for clients as well as invests in many general and specialized IT technologies and products. Westat also provides licensing, training and support for Blaise, a major data collection software system produced by Statistics Netherlands and used internationally. 

Founded: 1975 2019 U.S. revenue: $682 million Percent change from 2018: 16.2% 2019 non-U.S. revenue: $1,685 million Percent from outside U.S.: 71.2% 2019 worldwide revenue: $2,367 million U.S. employees: 2,025

Ipsos, through its subsidiaries, engages in collecting, processing and delivering survey data for brands, companies and institutions primarily in Europe, the Middle East, Africa, the Americas and Asia Pacific. It explores market potential and market trends, tests products and advertising, helps clients build long-term relationships with customers, studies audiences and their perceptions of various media and measures public opinion trends. Ipsos offers advertising research services, including advertising tracking and brand equity evaluation services that help advertisers in the development, evaluation and improvement of their advertising efforts.

It also provides marketing research services that help clients to identify business opportunities and innovation platforms, develop strategies at point of sale, generate insights and ideas, develop and optimize their mix, and model and forecast sales volumes, as well as offers custom innovative products and solutions to address stakeholder experience and brand-building business goals.

In this unique year, Ipsos has remained strong and reaffirmed its ambition and sense of purpose to deliver reliable information for a true understanding of society, markets and people. Ipsos activates this vision for more than 5,000 customers through its presence in 90 markets both globally and locally. Ipsos covers the whole information production and analysis chain, from the collection of raw data to the activation of the insights. It has a solid tradition of innovation expressed by new methodological developments and continuously renewed product range.

5. Information Resources, Inc. (IRI) **

Founded: 1979 2019 U.S. revenue: $815 million Percent change from 2018: 1.9% 2019 non-U.S. revenue: $510 million Percent from outside U.S.: 38.5% 2019 worldwide revenue: $1,325 million U.S. employees: 3,639

IRI is a leading provider of big data, predictive analytics and forward-looking insights that help consumer packaged goods, over-the-counter healthcare organizations, retailers, financial services and media companies grow their businesses. A confluence of major external events—a change in consumer buying habits, big data coming into its own, advanced analytics and personalized consumer activation—is leading to a seismic shift in drivers of success in all industries. With the largest repository of purchase, media, social, causal and loyalty data, all integrated on an on-demand, cloud-based technology platform, IRI is empowering the personalization revolution, helping to guide its more than 5,000 clients around the world in their quest to remain relentlessly relevant, capture market share, connect with consumers, collaborate with key constituents and deliver market-leading growth.

In 2019, IRI announced the integration of artificial intelligence and machine learning into its leading suite of analytics solutions, retained 100% of its major CPG clients and welcomed new strategic partnerships with top retailers in the U.S. IRI added several innovators to its leadership team while continuing to invest in its employees by providing ongoing training. 

4. Kantar **

Founded: 1993 2019 U.S. revenue: $950 million Percent change from 2018: 2.7% 2019 non-U.S. revenue: $2,900 million Percent from outside U.S.: 75.3% 2019 worldwide revenue: $3,850 million U.S. employees: 3,585

Kantar is one of the world’s largest data, insights and consulting companies, bringing together some of the world’s leading research, data and insights expertise. Kantar’s offer covers the breadth of techniques and technologies, from purchase and media data to predicting long-term trends; from neuroscience to exit polls; from large-scale quantitative studies to qualitative research, incorporating ethnography and semiotics. 

In April 2019, all services and offerings of the various Kantar companies were combined under the Kantar brand name. This operational change enables Kantar to build platforms and offers on a global scale and to remove barriers to collaboration and co-creation within the organization to better meet clients’ needs. 

As part of this branding strategy, Kantar launched several initiatives:

• Kantar Marketplace, a global on-demand research and insights store.
• Kantar’s new Brand Guidance System that intelligently integrates validated survey measures with social, search, sales media and behavioral data to provide actionable insights to optimize brand or campaign performance.
• Integration of big data, artificial intelligence and analytical capabilities from across the company into one resource that unlocks deeper insights to fuel growth.

3. Gartner Research **

Founded: 1972 2019 U.S. revenue: $1,800 million Percent change from 2018: 4.7% 2019 non-U.S. revenue: $1,474.5 million Percent from outside U.S.: 45% 2019 worldwide revenue: $3,274.5 million U.S. employees: 4,500

Gartner Research delivers independent, objective advice to leaders across an enterprise through subscription services that include on-demand access to published research content, data and benchmarks, and direct access to a network of approximately 2,300 research experts located around the globe. Gartner Research is the fundamental building block for all Gartner products and services. It combines its proprietary research methodologies with extensive industry and academic relationships to create Gartner products and services that address each role across an enterprise. Gartner’s research agenda is defined by clients’ needs, focusing on the critical issues, opportunities and challenges they face every day. Its proprietary research content, presented in the form of reports, briefings, updates and related tools, is delivered directly to the client’s desktop via its website or product-specific portals.

Within the research segment, Global Technology Sales sells products and services to users and providers of technology, while Global Business Sales sells products and services to all other functional leaders, such as supply chain, marketing, human resources, finance, legal and sales. 

2. IQVIA * **

Founded: 2016 2019 U.S. revenue: $2,220 million Percent change from 2018: 8.6% 2019 non-U.S. revenue: $2,166 million Percent from outside U.S.: 49.4% 2019 worldwide revenue: $4,386 million U.S. employees: 6,000

IQVIA is a global provider of information, innovative technology solutions and contract research services focused on helping healthcare clients find better solutions for patients. Formed through the 2016 merger of Quintiles and IMS Health, IQVIA applies human data science—leveraging the analytic rigor and clarity of data science to the ever-expanding scope of human science—to enable companies to reimagine and develop new approaches to clinical development and commercialization, speed innovation and accelerate improvements in healthcare outcomes. 

IQVIA has three operating segments: Technology & Analytics Solutions, Research & Development Solutions and Contract Sales & Medical Solutions. Powered by the IQVIA CORE, IQVIA delivers unique and actionable insights at the intersection of large-scale analytics, transformative technology and extensive domain expertise, as well as execution capabilities to help biotech, medical device and pharmaceutical companies, medical researchers, government agencies, payers and other healthcare stakeholders tap into a deeper understanding of diseases, human behaviors and scientific advances, in an effort to advance their path toward cures.

IQVIA has one of the largest and most comprehensive collections of healthcare information in the world, which includes more than 800 million comprehensive, longitudinal, non-identified patient records spanning sales, prescription and promotional data, medical claims, electronic medical records, genomics and social media. Its scaled and growing information set contains more than 35 petabytes of proprietary data sourced from more than 150,000 data suppliers and covering more than 1 million data feeds globally. Based on this data, IQVIA delivers information and insights on more than 85% of the world’s pharmaceuticals, helping its clients run their organizations more efficiently and make better decisions to improve their clinical, commercial and financial performance. 

1. Nielsen **

Founded: 1923 2019 U.S. revenue: $3,875 million Percent change from 2018: 1.6% 2019 non-U.S. revenue: $2,623 million Percent from outside U.S.: 40.4% 2019 worldwide revenue: $6,498 million U.S. employees: 10,300

Nielsen is a global measurement and data analytics company that provides a complete and trusted view of consumers and markets worldwide. Nielsen is divided into two business units: Nielsen Global Media and Nielsen Global Connect. 

Nielsen Global Media provides media and advertising clients with unbiased and reliable metrics that create the shared understanding of the industry required for markets to function, enabling its clients to grow and succeed across the $600 billion global advertising market. Nielsen Global Media helps clients define exactly who they want to reach and optimize the outcomes they can achieve. The company’s cross-platform measurement strategy brings together the best of TV and digital measurement to ensure a more functional marketplace for the industry.

Nielsen Global Connect provides consumer packaged goods manufacturers and retailers with accurate, actionable information and a complete picture of the complex and changing marketplace that brands need to innovate and grow their businesses. Nielsen Global Connect provides data and builds tools that use predictive models to turn observations in the marketplace into business decisions and winning solutions. The business’ data and insights, combined with its open, cloud-native measurement and analytics platform that democratizes the power of data, continue to provide an essential foundation that makes markets possible in the rapidly evolving world of commerce. With Nielsen Global Connect’s set of guiding truths, businesses have the tools to create new opportunities.

* ‘% change’ calculation reflects adjustment of previously reported 2018 U.S. research revenue due to acquisition or divestiture activity or other business change during 2019.

** Some or all figures are not made available by this company so instead are based on research and estimation by the report author.

Diane Bowers is a consultant to research and data analytics businesses and industry associations in the U.S. and internationally. She previously served as the president of CASRO, board chair of the Global Research Business Network, a board member of the Americas Research Industry Alliance and a board member of The Roper Center for Public Opinion Research at Cornell University. She is also a past president of the Market Research Council and the Research Industry Coalition, and a long-time member of American Association for Public Opinion Research, AMA and ESOMAR.

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Market research industry - statistics & facts

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Comprehensive guidelines for appropriate statistical analysis methods in research

Affiliations.

• 1 Department of Anesthesiology and Pain Medicine, Daegu Catholic University School of Medicine, Daegu, Korea.
• 2 Department of Medical Statistics, Daegu Catholic University School of Medicine, Daegu, Korea.
• PMID: 39210669
• DOI: 10.4097/kja.24016

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes.

Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed.

Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making.

Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

Keywords: Algorithms; Biostatistics; Data analysis; Guideline; Statistical data interpretation; Statistical model..

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Statistical analysis methods and tests for surveys.

16 min read Get more from your survey results with tried and trusted statistical tests and analysis methods. The kind of data analysis you choose depends on your survey data, so it makes sense to understand as many statistical analysis options as possible. Here’s a one-stop guide.

Why use survey statistical analysis methods?

Using statistical analysis for survey data is a best practice for businesses and market researchers. But why?

Statistical tests can help you improve your knowledge of the market, create better experiences for your customers, give employees more of what they need to do their jobs, and sell more of your products and services to the people that want them. As data becomes more available and easier to manage using digital tools, businesses are increasingly using it to make decisions, rather than relying on gut instinct or opinion.

When it comes to survey data , collection is only half the picture. What you do with your results can make the difference between uninspiring top-line findings and deep, revelatory insights. Using data processing tools and techniques like statistical tests can help you discover:

• whether the trends you see in your data are meaningful or just happened by chance
• what your results mean in the context of other information you have
• whether one factor affecting your business is more important than others
• what your next research question should be
• how to generate insights that lead to meaningful changes

There are several types of statistical analysis for surveys . The one you choose will depend on what you want to know, what type of data you have, the method of data collection, how much time and resources you have available, and the level of sophistication of your data analysis software.

Learn how Qualtrics iQ can help you with advanced statistical analysis

Before you start

Whichever statistical techniques or methods you decide to use, there are a few things to consider before you begin.

Nail your sampling approach

One of the most important aspects of survey research is getting your sampling technique right and choosing the right sample size . Sampling allows you to study a large population without having to survey every member of it. A sample, if it’s chosen correctly, represents the larger population, so you can study your sample data and then use the results to confidently predict what would be found in the population at large.

There will always be some discrepancy between the sample data and the population, a phenomenon known as sampling error , but with a well-designed study, this error is usually so small that the results are still valuable.

There are several sampling methods, including probabilit y and non-probability sampling . Like statistical analysis, the method you choose will depend on what you want to know, the type of data you’re collecting and practical constraints around what is possible.

Define your null hypothesis and alternative hypothesis

A null hypothesis is a prediction you make at the start of your research process to help define what you want to find out. It’s called a null hypothesis because you predict that your expected outcome won’t happen – that it will be null and void. Put simply: you work to reject, nullify or disprove the null hypothesis.

Along with your null hypothesis, you’ll define the alternative hypothesis, which states that what you expect to happen will happen.

For example, your null hypothesis might be that you’ll find no relationship between two variables, and your alternative hypothesis might be that you’ll find a correlation between them. If you disprove the null hypothesis, either your alternative hypothesis is true or something else is happening. Either way, it points you towards your next research question.

Use a benchmark

Benchmarking is a way of standardising – levelling the playing field – so that you get a clearer picture of what your results are telling you. It involves taking outside factors into account so that you can adjust the parameters of your research and have a more precise understanding of what’s happening.

Benchmarking techniques use weighting to adjust for variables that may affect overall results. What does that mean? Well for example, imagine you’re interested in the growth of crops over a season. Your benchmarking will take into account variables that have an effect on crop growth, such as rainfall, hours of sunlight, any pests or diseases, type and frequency of fertiliser, so that you can adjust for anything unusual that might have happened, such as an unexpected plant disease outbreak on a single farm within your sample that would skew your results.

With benchmarks in place, you have a reference for what is “standard” in your area of interest, so that you can better identify and investigate variance from the norm.

The goal, as in so much of survey data analysis, is to make sure that your sample is representative of the whole population, and that any comparisons with other data are like-for-like.

Inferential or descriptive?

Statistical methods can be divided into inferential statistics and descriptive statistics.

• Descriptive statistics shed light on how the data is distributed across the population of interest, giving you details like variance within a group and mean values for measurements.
• Inferential statistics help you to make judgments and predict what might happen in the future, or to extrapolate from the sample you are studying to the whole population. Inferential statistics are the types of analyses used to test a null hypothesis. We’ll mostly discuss inferential statistics in this guide.

Types of statistical analysis

Regression analysis.

Regression is a statistical technique used for working out the relationship between two (or more) variables.

To understand regressions, we need a quick terminology check:

• Independent variables are “standalone” phenomena (in the context of the study) that influence dependent variables
• Dependent variables are things that change as a result of their relationship to independent variables

Let’s use an example: if we’re looking at crop growth during the month of August in Iowa, that’s our dependent variable. It’s affected by independent variables including sunshine, rainfall, pollution levels and the prevalence of certain bugs and pests.

A change in a dependent variable depends on, and is associated with, a change in one (or more) of the independent variables.

• Linear regression uses a single independent variable to predict an outcome of the dependent variable.
• Multiple regression uses at least two independent variables to predict the effect on the dependent variable. A multiple regression can be linear or non-linear.

The results from a linear regression analysis are shown as a graph with variables on the axes and a ‘regression curve’ that shows the relationships between them. Data is rarely directly proportional, so there’s usually some degree of curve rather than a straight line.

With this kind of statistical test, the null hypothesis is that there is no relationship between the dependent variable and the independent variable. The resulting graph would probably (though not always) look quite random rather than following a clear line.

Regression is a useful test statistic as you’re able to identify not only whether a relationship is statistically significant, but the precise impact of a change in your independent variable.

The T-test (aka Student’s T-test) is a tool for comparing two data groups which have different mean values. The T-test allows the user to interpret whether differences are statistically significant or merely coincidental.

For example, do women and men have different mean heights? We can tell from running a t-test that there is a meaningful difference between the average height of a man and the average height of a woman – i.e. the difference is statistically significant.

For this test statistic, the null hypothesis would be that there’s no statistically significant difference.

The results of a T-test are expressed in terms of probability (p-value). If the p-value is below a certain threshold, usually 0.05, then you can be very confident that your two groups really are different and it wasn’t just a chance variation between your sample data.

Analysis of variance (ANOVA) test

Like the T-test, ANOVA (analysis of variance) is a way of testing the differences between groups to see if they’re statistically significant. However, ANOVA allows you to compare three or more groups rather than just two.

Also like the T-test, you’ll start off with the null hypothesis that there is no meaningful difference between your groups.

ANOVA is used with a regression study to find out what effect independent variables have on the dependent variable. It can compare multiple groups simultaneously to see if there is a relationship between them.

An example of ANOVA in action would be studying whether different types of advertisements get different consumer responses. The null hypothesis is that none of them have more effect on the audience than the others and they’re all basically as effective as one another. The audience reaction is the dependent variable here, and the different ads are the independent variables.

Cluster analysis

Cluster analysis is a way of processing datasets by identifying how closely related the individual data points are. Using cluster analysis, you can identify whether there are defined groups (clusters) within a large pool of data, or if the data is continuously and evenly spread out.

Cluster analysis comes in a few different forms, depending on the type of data you have and what you’re looking to find out. It can be used in an exploratory way, such as discovering clusters in survey data around demographic trends or preferences, or to confirm and clarify an existing alternative or null hypothesis.

Cluster analysis is one of the more popular statistical techniques in market research , since it can be used to uncover market segments and customer groups.

Factor analysis

Factor analysis is a way to reduce the complexity of your research findings by trading a large number of initial variables for a smaller number of deeper, underlying ones. In performing factor analysis, you uncover “hidden” factors that explain variance (difference from the average) in your findings.

Because it delves deep into the causality behind your data, factor analysis is also a form of research in its own right, as it gives you access to drivers of results that can’t be directly measured.

Conjoint analysis

Market researchers love to understand and predict why people make the complex choices they do. Conjoint analysis comes closest to doing this: it asks people to make trade-offs when making decisions, just as they do in the real world, then analyses the results to give the most popular outcome.

For example, an investor wants to open a new restaurant in a town. They think one of the following options might be the most profitable:

The investor commissions market research. The options are turned into a survey for the residents:

• Which type of restaurant do you prefer? (Gourmet burger/Spanish tapas/Thai)
• What would you be prepared to spend per head? (£20, $40, £60)
• How far would you be willing to travel? (5km, 2km, 10km)
• Would your partner…? (Love it, be OK with it)

There are lots of possible combinations of answers – 54 in this case: (3 restaurant types) x (3 price levels) x (3 distances) x (2 partner preferences). Once the survey data is in, conjoint analysis software processes it to figure out how important each option is in driving customer decisions, which levels for each option are preferred, and by how much.

So, from conjoint analysis, the restaurant investor may discover that there’s a statistically significant preference for an expensive Spanish tapas bar on the outskirts of town – something they may not have considered before.

Get more details: What is a conjoint analysis? Conjoint types and when to use them

Crosstab analysis

Crosstab (cross-tabulation) is used in quantitative market research to analyse categorical data – that is, variables that are different and mutually exclusive, such as: ‘men’ and ‘women’, or ‘under 30’ and ‘over 30’.

Also known by names like contingency table and data tabulation, crosstab analysis allows you to compare the relationship between two variables by presenting them in easy-to-understand tables.

A statistical method called chi-squared can be used to test whether the variables in a crosstab analysis are independent or not by looking at whether the differences between them are statistically significant.

Text analysis and sentiment analysis

Analysing human language is a relatively new form of data processing, and one that offers huge benefits in experience management. As part of the Stats iQ package, TextiQ from Qualtrics uses machine learning and natural language processing to parse and categorise data from text feedback, assigning positive, negative or neutral sentiment to customer messages and reviews.

With this data from text analysis in place, you can then employ statistical tools to analyse trends, make predictions and identify drivers of positive change.

The easy way to run statistical analysis

As you can see, using statistical methods is a powerful and versatile way to get more value from your research data, whether you’re running a simple linear regression to show a relationship between two variables, or performing natural language processing to evaluate the thoughts and feelings of a huge population.

Knowing whether what you notice in your results is statistically significant or not gives you the green light to confidently make decisions and present findings based on your results, since statistical methods provide a degree of certainty that most people recognise as valid. So having results that are statistically significant is a hugely important detail for businesses as well as academics and researchers.

Fortunately, using statistical methods, even the highly sophisticated kind, doesn’t have to involve years of study. With the right tools at your disposal, you can jump into exploratory data analysis almost straight away.

Our Stats iQ™ product can perform the most complicated statistical tests at the touch of a button using our online survey software , or data brought in from other sources. Turn your data into insights and actions with CoreXM and Stats iQ . Powerful statistical analysis. No stats degree required.

Learn how Qualtrics iQ can help you understand the experience like never before

Related resources

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Sentiment Analysis 20 min read

Thematic analysis 11 min read, predictive analytics 19 min read, descriptive statistics 15 min read, statistical significance calculator 18 min read, data analysis 29 min read, regression analysis 19 min read, request demo.

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Sustainable Packaging - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)

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Description

Table of contents.

The Sustainable Packaging Market size is estimated at USD 292.71 billion in 2024, and is expected to reach USD 423.56 billion by 2029, growing at a CAGR of 7.67% during the forecast period (2024-2029).

Key Highlights

• Sustainable packaging involves developing and utilizing packaging solutions that enhance sustainability. It relies heavily on life cycle assessments and inventories to guide decisions and minimize environmental impact.
• In recent years, consumer interest in sustainability has surged significantly. The rise of circular economics has further underscored the importance of sustainable packaging. Governments worldwide, responding to public concerns over packaging waste, particularly single-use plastics, are enacting stringent regulations to curb environmental harm and bolster waste management.
• Leading the charge, countries like France, Germany, and the United Kingdom are not only enforcing robust recycling measures within the European Union but are also adopting extended producer responsibilities (EPRs). In Asia, Thailand implemented a nationwide ban on single-use plastic bags in major stores starting January 1, 2020, with the ultimate goal of reducing plastic pollution.
• According to the Ocean Conservancy statistics, 8 million metric tons of plastic enter the oceans annually, adding to the estimated 150 million metric tons already present. This is a critical issue. To put this in perspective, it is akin to dumping a New York City garbage truck's load of plastic into the ocean every minute for a year. The survey identifies 2025 as a pivotal year for eco-friendly packaging, with over 40% of respondents planning to adopt innovative and sustainable packaging techniques. Companies are increasingly pivoting toward a circular economy, utilizing compostable materials and biodegradables and rethinking container designs to reduce waste.
• However, the use of non-recyclable, non-biodegradable plastic packaging is on the rise, exacerbating carbon emissions. In response, major corporations like Amazon, Google, and Tetrapak are setting ambitious targets, aiming for net-zero carbon emissions, a move expected to entail significant capital investments.

Sustainable Packaging Market Trends

The recycled packaging segment to hold significant share in market.

• Increasing consumer demand for sustainable products is driving the growth of recycled packaging. As environmental awareness rises, consumers are actively seeking brands that prioritize sustainability, particularly in their packaging choices. This consumer shift is compelling companies across sectors to pivot toward recycled packaging, aligning with both consumer expectations and bolstering their corporate social responsibility standings. Simultaneously, advancements in recycling technologies are streamlining the incorporation of recycled materials into packaging, enhancing cost-effectiveness for manufacturers, and further amplifying market growth.
• Global governments and regulatory bodies are pivotal in expanding the recycled packaging market. They are enacting stringent regulations and policies, with a primary focus on curbing plastic waste and promoting recycling. Notably, initiatives like the European Union's Circular Economy Action Plan and plastic waste reduction mandates in countries like the United States and Canada are nudging companies toward sustainable packaging investments. These directives not only mandate the use of recycled materials but also foster innovation in packaging design and material science, aiming to bolster recyclability and minimize environmental footprints.
• Technological strides in recycling processes are pivotal for the growth of the recycled packaging market. Innovations like chemical recycling, which breaks down plastics into their original monomers, are paving the way for high-quality recycled materials in packaging. These advancements are addressing limitations seen in traditional mechanical recycling, like material degradation and the restricted recyclability of certain plastics. With ongoing technological evolution, the quality and efficacy of recycled packaging are poised to elevate, further stoking market demand.
• Businesses are increasingly recognizing the economic merits of recycled packaging. By embracing recycled materials, companies can diminish their dependence on virgin resources, trim production costs, and buffer themselves against supply chain disruptions. Moreover, brands adopting recycled packaging not only resonate with environmentally conscious consumers but also carve out a distinct niche from their competitors, bolstering their market appeal. Consequently, both major players and smaller enterprises in the packaging realm are channeling substantial investments into recycled packaging solutions, underpinning the market's robust growth trajectory.

Asia-Pacific Expected to Register Significant Market Growth

• Chinese e-commerce giants and express delivery services are actively cutting down on packaging materials. For example, SF Express introduced recyclable packaging boxes, each capable of being recycled approximately ten times. In major cities, including first-tier and several second-tier ones, the company deployed over 100,000 of these boxes. Its primary aim is to replace traditional paper boxes and plastic bags, thereby reducing the usage of foam blocks and tape. SF Express emphasized that these efforts align with China's push for sustainable logistics growth. The company has also invested in research and development to create more durable and eco-friendly packaging solutions, ensuring that the materials used can withstand multiple cycles of use without compromising the safety and integrity of the contents.
• The rise of India's middle class, the rapid expansion of organized retail, increasing exports, and the booming e-commerce industry are all paving the way for the growth of the market studied. This growth, however, necessitates environmentally friendly packaging that ensures top-notch quality with minimal environmental repercussions. Consequently, the adoption of sustainable packaging practices by companies is gaining paramount importance. Companies are increasingly focusing on biodegradable materials, reducing plastic usage, and implementing innovative designs that minimize waste. Additionally, government regulations and consumer awareness are driving the shift toward greener packaging solutions, with many firms setting ambitious sustainability targets to meet these new standards.
• A recent survey by Capgemini delved into sustainability and changing consumer behavior. It revealed that 79% of consumers are altering their buying patterns, primarily driven by social responsibility, inclusivity, and environmental concerns. Notably, 53% of all consumers and a significant 57% of those aged 18 to 24 are gravitating toward lesser-known brands due to their eco-friendliness. Moreover, over half (52%) of respondents expressed an emotional connection with businesses prioritizing sustainability. The survey also highlighted that consumers are willing to pay a premium for sustainably packaged products, indicating a strong market demand for eco-friendly options. Brands that adapt to these preferences avoid losing market share to more environmentally conscious competitors.
• Key players in the region are spearheading innovations, propelling the growth of the sustainable packaging market. These innovations include the development of new materials such as plant-based plastics, advanced recycling technologies, and smart packaging solutions that enhance product shelf life while reducing environmental impact. Collaborative efforts between industry leaders, startups, and research institutions are also fostering a culture of continuous improvement and sustainability in the packaging industry.

Sustainable Packaging Industry Overview

The sustainable packaging market exhibits a high degree of fragmentation, with prominent players such as Amcor Limited, TetraPak International SA, WestRock Company, Smurfit Kappa Group PLC, and Sonoco Products Company. These companies are actively engaged in developing innovative and eco-friendly packaging solutions to meet the growing demand for sustainable practices. They invest significantly in research and development to enhance the recyclability and biodegradability of their products, striving to reduce the environmental impact of packaging materials. Additionally, these key players often collaborate with other stakeholders in the supply chain to encourage sustainability and adhere to stringent regulatory standards.

• May 2024: Amcor and AVON, a leading cosmetics company, announced the launch of the AmPrima Plus refill pouch for AVON's Little Black Dress shower gels in China. This strategic initiative aims to reduce both carbon footprint and water consumption.
• March 2024: TOPPAN, a global player in printing and packaging solutions, revolutionized sustainable packaging with the launch of GL-SP, a barrier film utilizing biaxially oriented polypropylene (BOPP) as the substrate. This new product has been added to the GL BARRIER series, renowned for its transparent vapor-deposited barrier films, which hold a leading share in the global market.

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