market research data analysis examples

How To Present Your Market Research Results And Reports In An Efficient Way

Table of Contents

1) What Is A Market Research Report?

2) Market Research Reports Examples

3) Why Do You Need Market Research Reports

4) How To Make A Market Research Report?

5) Types Of Market Research Reports

6) Challenges & Mistakes Market Research Reports

Market research analyses are the go-to solution for many professionals, and for good reason: they save time, offer fresh insights, and provide clarity on your business. In turn, market research reports will help you to refine and polish your strategy. Plus, a well-crafted report will give your work more credibility while adding weight to any marketing recommendations you offer a client or executive.

But, while this is the case, today’s business world still lacks a way to present market-based research results efficiently. The static, antiquated nature of PowerPoint makes it a bad choice for presenting research discoveries, yet it is still widely used to present results. 

Fortunately, things are moving in the right direction. There are online data visualization tools that make it easy and fast to build powerful market research dashboards. They come in handy to manage the outcomes, but also the most important aspect of any analysis: the presentation of said outcomes, without which it becomes hard to make accurate, sound decisions. 

Here, we consider the benefits of conducting research analyses while looking at how to write and present market research reports, exploring their value, and, ultimately, getting the very most from your research results by using professional market research software .

Let’s get started.

What Is a Market Research Report?

A market research report is an online reporting tool used to analyze the public perception or viability of a company, product, or service. These reports contain valuable and digestible information like customer survey responses and social, economic, and geographical insights.

On a typical market research results example, you can interact with valuable trends and gain insight into consumer behavior and visualizations that will empower you to conduct effective competitor analysis. Rather than adding streams of tenuous data to a static spreadsheet, a full market research report template brings the outcomes of market-driven research to life, giving users a data analysis tool to create actionable strategies from a range of consumer-driven insights.

With digital market analysis reports, you can make your business more intelligent more efficient, and, ultimately, meet the needs of your target audience head-on. This, in turn, will accelerate your commercial success significantly.

Your Chance: Want to test a market research reporting software? Explore our 14-day free trial & benefit from interactive research reports!

How To Present Your Results: 4 Essential Market Research Report Templates

When it comes to sharing rafts of invaluable information, research dashboards are invaluable.

Any market analysis report example worth its salt will allow everyone to get a firm grip on their results and discoveries on a single page with ease. These dynamic online dashboards also boast interactive features that empower the user to drill down deep into specific pockets of information while changing demographic parameters, including gender, age, and region, filtering the results swiftly to focus on the most relevant insights for the task at hand.

These four market research report examples are different but equally essential and cover key elements required for market survey report success. You can also modify each and use it as a client dashboard .

While there are numerous types of dashboards that you can choose from to adjust and optimize your results, we have selected the top 3 that will tell you more about the story behind them. Let’s take a closer look.

1. Market Research Report: Brand Analysis

Our first example shares the results of a brand study. To do so, a survey has been performed on a sample of 1333 people, information that we can see in detail on the left side of the board, summarizing the gender, age groups, and geolocation.

**click to enlarge**

At the dashboard's center, we can see the market-driven research discoveries concerning first brand awareness with and without help, as well as themes and celebrity suggestions, to know which image the audience associates with the brand.

Such dashboards are extremely convenient to share the most important information in a snapshot. Besides being interactive (but it cannot be seen on an image), it is even easier to filter the results according to certain criteria without producing dozens of PowerPoint slides. For instance, I could easily filter the report by choosing only the female answers, only the people aged between 25 and 34, or only the 25-34 males if that is my target audience.

Primary KPIs:

a) Unaided Brand Awareness

The first market research KPI in this most powerful report example comes in the form of unaided brand awareness. Presented in a logical line-style chart, this particular market study report sample KPI is invaluable, as it will give you a clear-cut insight into how people affiliate your brand within their niche.

As you can see from our example, based on a specific survey question, you can see how your brand stacks up against your competitors regarding awareness. Based on these outcomes, you can formulate strategies to help you stand out more in your sector and, ultimately, expand your audience.

b) Aided Brand Awareness

This market survey report sample KPI focuses on aided brand awareness. A visualization that offers a great deal of insight into which brands come to mind in certain niches or categories, here, you will find out which campaigns and messaging your target consumers are paying attention to and engaging with.

By gaining access to this level of insight, you can conduct effective competitor research and gain valuable inspiration for your products, promotional campaigns, and marketing messages.

c) Brand image

When it comes to research reporting, understanding how others perceive your brand is one of the most golden pieces of information you could acquire. If you know how people feel about your brand image, you can take informed and very specific actions that will enhance the way people view and interact with your business.

By asking a focused question, this visual of KPIs will give you a definitive idea of whether respondents agree, disagree, or are undecided on particular descriptions or perceptions related to your brand image. If you’re looking to present yourself and your message in a certain way (reliable, charming, spirited, etc.), you can see how you stack up against the competition and find out if you need to tweak your imagery or tone of voice - invaluable information for any modern business.

d) Celebrity analysis

This indicator is a powerful part of our research KPI dashboard on top, as it will give you a direct insight into the celebrities, influencers, or public figures that your most valued consumers consider when thinking about (or interacting with) your brand.

Displayed in a digestible bar chart-style format, this useful metric will not only give you a solid idea of how your brand messaging is perceived by consumers (depending on the type of celebrity they associate with your brand) but also guide you on which celebrities or influencers you should contact.

By working with the right influencers in your niche, you will boost the impact and reach of your marketing campaigns significantly, improving your commercial awareness in the process. And this is the KPI that will make it happen.

2. Market Research Results On Customer Satisfaction

Here, we have some of the most important data a company should care about: their already-existing customers and their perception of their relationship with the brand. It is crucial when we know that it is five times more expensive to acquire a new consumer than to retain one.

This is why tracking metrics like the customer effort score or the net promoter score (how likely consumers are to recommend your products and services) is essential, especially over time. You need to improve these scores to have happy customers who will always have a much bigger impact on their friends and relatives than any of your amazing ad campaigns. Looking at other satisfaction indicators like the quality, pricing, and design, or the service they received is also a best practice: you want a global view of your performance regarding customer satisfaction metrics .

Such research results reports are a great tool for managers who do not have much time and hence need to use them effectively. Thanks to these dashboards, they can control data for long-running projects anytime.

Primary KPIs :

a) Net Promoter Score (NPS)

Another pivotal part of any informative research presentation is your NPS score, which will tell you how likely a customer is to recommend your brand to their peers.

Centered on overall customer satisfaction, your NPS Score can cover the functions and output of many departments, including marketing, sales, and customer service, but also serve as a building block for a call center dashboard . When you’re considering how to present your research effectively, this balanced KPI offers a masterclass. It’s logical, it has a cohesive color scheme, and it offers access to vital information at a swift glance. With an NPS Score, customers are split into three categories: promoters (those scoring your service 9 or 10), passives (those scoring your service 7 or 8), and detractors (those scoring your service 0 to 6). The aim of the game is to gain more promoters. By gaining an accurate snapshot of your NPS Score, you can create intelligent strategies that will boost your results over time.

b) Customer Satisfaction Score (CSAT)

The next in our examples of market research reports KPIs comes in the form of the CSAT. The vast majority of consumers that have a bad experience will not return. Honing in on your CSAT is essential if you want to keep your audience happy and encourage long-term consumer loyalty.

This magnificent, full report KPI will show how satisfied customers are with specific elements of your products or services. Getting to grips with these scores will allow you to pinpoint very specific issues while capitalizing on your existing strengths. As a result, you can take measures to improve your CSAT score while sharing positive testimonials on your social media platforms and website to build trust.

c) Customer Effort Score (CES)

When it comes to presenting research findings, keeping track of your CES Score is essential. The CES Score KPI will give you instant access to information on how easy or difficult your audience can interact with or discover your company based on a simple scale of one to ten.

By getting a clear-cut gauge of how your customers find engagement with your brand, you can iron out any weaknesses in your user experience (UX) offerings while spotting any friction, bottlenecks, or misleading messaging. In doing so, you can boost your CES score, satisfy your audience, and boost your bottom line.

3. Market Research Results On Product Innovation

This final market-driven research example report focuses on the product itself and its innovation. It is a useful report for future product development and market potential, as well as pricing decisions.

Using the same sample of surveyed people as for the first market-focused analytical report , they answer questions about their potential usage and purchase of the said product. It is good primary feedback on how the market would receive the new product you would launch. Then comes the willingness to pay, which helps set a price range that will not be too cheap to be trusted nor too expensive for what it is. That will be the main information for your pricing strategy.

a) Usage Intention

The first of our product innovation KPI-based examples comes in the form of usage intention. When you’re considering how to write a market research report, including metrics centered on consumer intent is critical.

This simple yet effective visualization will allow you to understand not only how users see your product but also whether they prefer previous models or competitor versions . While you shouldn’t base all of your product-based research on this KPI, it is very valuable, and you should use it to your advantage frequently.

b) Purchase Intention

Another aspect to consider when looking at how to present market research data is your audience’s willingness or motivation to purchase your product. Offering percentage-based information, this effective KPI provides a wealth of at-a-glance information to help you make accurate forecasts centered on your product and service offerings.

Analyzing this information regularly will give you the confidence and direction to develop strategies that will steer you to a more prosperous future, meeting the ever-changing needs of your audience on an ongoing basis.

c) Willingness To Pay (WPS)

Our final market research example KPI is based on how willing customers are to pay for a particular service or product based on a specific set of parameters. This dynamic visualization, represented in an easy-to-follow pie chart, will allow you to realign the value of your product (USPs, functions, etc.) while setting price points that are most likely to result in conversions. This is a market research presentation template that every modern organization should use to its advantage.

4. Market Research Report On Customer Demographics 

This particular example of market research report, generated with a modern dashboard creator , is a powerful tool, as it displays a cohesive mix of key demographic information in one intuitive space.

By breaking down these deep pockets of consumer-centric information, you can gain the power to develop more impactful customer communications while personalizing every aspect of your target audience’s journey across every channel or touchpoint. As a result, you can transform theoretical insights into actionable strategies that will result in significant commercial growth. 

Every section of this responsive marketing research report works in unison to build a profile of your core audience in a way that will guide your company’s consumer-facing strategies with confidence. With in-depth visuals based on gender, education level, and tech adoption, you have everything you need to speak directly to your audience at your fingertips.

Let’s look at the key performance indicators (KPIs) of this invaluable market research report example in more detail.

a) Customer By Gender

This KPI is highly visual and offers a clear-cut representation of your company’s gender share over time. By gaining access to this vital information, you can deliver a more personalized experience to specific audience segments while ensuring your messaging is fair, engaging, and inclusive.

b) Customers by education level

The next market analysis report template is a KPI that provides a logical breakdown of your customers’ level of education. By using this as a demographic marker, you can refine your products to suit the needs of your audience while crafting your content in a way that truly resonates with different customer groups.

c) Customers by technology adoption

Particularly valuable if you’re a company that sells tech goods or services, this linear KPI will show you where your customers are in terms of technological know-how or usage. By getting to grips with this information over time, you can develop your products or services in a way that offers direct value to your consumers while making your launches or promotions as successful as possible.

d) Customer age groups

By understanding your customers’ age distribution in detail, you can gain a deep understanding of their preferences. And that’s exactly what this market research report sample KPI does. Presented in a bar chart format, this KPI will give you a full breakdown of your customers’ age ranges, allowing you to build detailed buyer personas and segment your audience effectively.

Why Do You Need Market Research Reports?

As the adage goes, “Look before you leap“ – which is exactly what a research report is here for. As the headlights of a car, they will show you the pitfalls and fast lanes on your road to success: likes and dislikes of a specific market segment in a certain geographical area, their expectations, and readiness. Among other things, a research report will let you:

• Get a holistic view of the market : learn more about the target market and understand the various factors involved in the buying decisions. A broader view of the market lets you benchmark other companies you do not focus on. This, in turn, will empower you to gather the industry data that counts most. This brings us to our next point.
• Curate industry information with momentum: Whether you’re looking to rebrand, improve on an existing service, or launch a new product, time is of the essence. By working with the best market research reports created with modern BI reporting tools , you can visualize your discoveries and data, formatting them in a way that not only unearths hidden insights but also tells a story - a narrative that will gain a deeper level of understanding into your niche or industry. The features and functionality of a market analysis report will help you grasp the information that is most valuable to your organization, pushing you ahead of the pack in the process.
• Validate internal research: Doing the internal analysis is one thing, but double-checking with a third party also greatly helps avoid getting blinded by your own data.
• Use actionable data and make informed decisions: Once you understand consumer behavior as well as the market, your competitors, and the issues that will affect the industry in the future, you are better armed to position your brand. Combining all of it with the quantitative data collected will allow you to more successful product development. To learn more about different methods, we suggest you read our guide on data analysis techniques .
• Strategic planning: When you want to map out big-picture organizational goals, launch a new product development, plan a geographic market expansion, or even a merger and acquisition – all of this strategic thinking needs solid foundations to fulfill the variety of challenges that come along.
• Consistency across the board: Collecting, presenting, and analyzing your results in a way that’s smarter, more interactive, and more cohesive will ensure your customer communications, marketing campaigns, user journey, and offerings meet your audience’s needs consistently across the board. The result? Faster growth, increased customer loyalty, and more profit.
• Better communication: The right market research analysis template (or templates) will empower everyone in the company with access to valuable information - the kind that is relevant and comprehensible. When everyone is moving to the beat of the same drum, they will collaborate more effectively and, ultimately, push the venture forward thanks to powerful online data analysis techniques.
• Centralization: Building on the last point, using a powerful market research report template in the form of a business intelligence dashboard will make presenting your findings to external stakeholders and clients far more effective, as you can showcase a wealth of metrics, information, insights, and invaluable feedback from one centralized, highly visual interactive screen. 
• Brand reputation: In the digital age, brand reputation is everything. By making vital improvements in all of the key areas above, you will meet your customers’ needs head-on with consistency while finding innovative ways to stand out from your competitors. These are the key ingredients of long-term success.

How To Present Market Research Analysis Results?

Here we look at how you should present your research reports, considering the steps it takes to connect with the outcomes you need to succeed:

• Collect your data 

As with any reporting process, you first and foremost need to collect the data you’ll use to conduct your studies. Businesses conduct research studies to analyze their brand awareness, identity, and influence in the market. For product development and pricing decisions, among many others. That said, there are many ways to collect information for a market research report. Among some of the most popular ones, we find: 

• Surveys: Probably the most common way to collect research data, surveys can come in the form of open or closed questions that can be answered anonymously. They are the cheapest and fastest way to collect insights about your customers and business. 
• Interviews : These are face-to-face discussions that allow the researcher to analyze responses as well as the body language of the interviewees. This method is often used to define buyer personas by analyzing the subject's budget, job title, lifestyle, wants, and needs, among other things. 
• Focus groups : This method involves a group of people discussing a topic with a mediator. It is often used to evaluate a new product or new feature or to answer a specific question that the researcher might have. 
• Observation-based research : In this type of research, the researcher or business sits back and watches customers interact with the product without any instructions or help. It allows us to identify pain points as well as strong features. 
• Market segmentation : This study allows you to identify and analyze potential market segments to target. Businesses use it to expand into new markets and audiences. 

These are just a few of the many ways in which you can gather your information. The important point is to keep the research objective as straightforward as possible. Supporting yourself with professional BI solutions to clean, manage, and present your insights is probably the smartest choice.

2. Hone in on your research:

When looking at how to source consumer research in a presentation, you should focus on two areas: primary and secondary research. Primary research comes from your internal data, monitoring existing organizational practices, the effectiveness of sales, and the tools used for communication, for instance. Primary research also assesses market competition by evaluating the company plans of the competitors. Secondary research focuses on existing data collected by a third party, information used to perform benchmarking and market analysis. Such metrics help in deciding which market segments are the ones the company should focus its efforts on or where the brand is standing in the minds of consumers. Before you start the reporting process, you should set your goals, segmenting your research into primary and secondary segments to get to grips with the kind of information you need to work with to achieve effective results.

3. Segment your customers:

To give your market research efforts more context, you should segment your customers into different groups according to the preferences outlined in the survey or feedback results or by examining behavioral or demographic data.

If you segment your customers, you can tailor your market research and analysis reports to display only the information, charts, or graphics that will provide actionable insights into their wants, needs, or industry-based pain points. 

• Identify your stakeholders:

Once you’ve drilled down into your results and segmented your consumer groups, it’s important to consider the key stakeholders within the organization that will benefit from your information the most. 

By looking at both internal and external stakeholders, you will give your results a path to effective presentation, gaining the tools to understand which areas of feedback or data are most valuable, as well as most redundant. As a consequence, you will ensure your results are concise and meet the exact information needs of every stakeholder involved in the process.

• Set your KPIs:

First, remember that your reports should be concise and accurate - straight to the point without omitting any essential information. Work to ensure your insights are clean and organized, with participants grouped into relevant categories (demographics, profession, industry, education, etc.). Once you’ve organized your research, set your goals, and cleaned your data, you should set your KPIs to ensure your report is populated with the right visualizations to get the job done. Explore our full library of interactive KPI examples for inspiration.

• Include competitor’s analysis 

Whether you are doing product innovation research, customer demographics, pricing, or any other, including some level of insights about competitors in your reports is always recommended as it can help your business or client better understand where they stand in the market. That being said, competitor analysis is not as easy as picking a list of companies in the same industry and listing them. Your main competitor can be just a company's division in an entirely different industry. For example, Apple Music competes with Spotify even though Apple is a technology company. Therefore, it is important to carefully analyze competitors from a general but detailed level. 

Providing this kind of information in your reports can also help you find areas that competitors are not exploiting or that are weaker and use them to your advantage to become a market leader. 

• Produce your summary:

To complement your previous efforts, writing an executive summary of one or two pages that will explain the general idea of the report is advisable. Then come the usual body parts:

• An introduction providing background information, target audience, and objectives;
• The qualitative research describes the participants in the research and why they are relevant to the business;
• The survey research outlines the questions asked and answered;
• A summary of the insights and metrics used to draw the conclusions, the research methods chosen, and why;
• A presentation of the findings based on your research and an in-depth explanation of these conclusions.
• Use a mix of visualizations:

When presenting your results and discoveries, you should aim to use a balanced mix of text, graphs, charts, and interactive visualizations.

Using your summary as a guide, you should decide which type of visualization will present each specific piece of market research data most effectively (often, the easier to understand and more accessible, the better).

Doing so will allow you to create a story that will put your research information into a living, breathing context, providing a level of insight you need to transform industry, competitor, or consumer info or feedback into actionable strategies and initiatives.

• Be careful not to mislead 

Expanding on the point above, using a mix of visuals can prove highly valuable in presenting your results in an engaging and understandable way. That being said, when not used correctly, graphs and charts can also become misleading. This is a popular practice in the media, news, and politics, where designers tweak the visuals to manipulate the masses into believing a certain conclusion. This is a very unethical practice that can also happen by mistake when you don’t pick the right chart or are not using it in the correct way. Therefore, it is important to outline the message you are trying to convey and pick the chart type that will best suit those needs. 

Additionally, you should also be careful with the data you choose to display, as it can also become misleading. This can happen if you, for example, cherry-pick data, which means only showing insights that prove a conclusion instead of the bigger picture. Or confusing correlation with causation, which means assuming that because two events happened simultaneously, one caused the other. 

Being aware of these practices is of utmost importance as objectivity is crucial when it comes to dealing with data analytics, especially if you are presenting results to clients. Our guides on misleading statistics and misleading data visualizations can help you learn more about this important topic. 

• Use professional dashboards:

To optimize your market research discoveries, you must work with a dynamic business dashboard . Not only are modern dashboards presentable and customizable, but they will offer you past, predictive, and real-time insights that are accurate, interactive, and yield long-lasting results.

All market research reports companies or businesses gathering industry or consumer-based information will benefit from professional dashboards, as they offer a highly powerful means of presenting your data in a way everyone can understand. And when that happens, everyone wins.

Did you know? The interactive nature of modern dashboards like datapine also offers the ability to quickly filter specific pockets of information with ease, offering swift access to invaluable insights.

• Prioritize interactivity 

The times when reports were static are long gone. Today, to extract the maximum value out of your research data, you need to be able to explore the information and answer any critical questions that arise during the presentation of results. To do so, modern reporting tools provide multiple interactivity features to help you bring your research results to life. 

For instance, a drill-down filter lets you go into lower levels of hierarchical data without generating another graph. For example, imagine you surveyed customers from 10 different countries. In your report, you have a chart displaying the number of customers by country, but you want to analyze a specific country in detail. A drill down filter would enable you to click on a specific country and display data by city on that same chart. Even better, a global filter would allow you to filter the entire report to show only results for that specific country. 

Through the use of interactive filters, such as the one we just mentioned, you’ll not only make the presentation of results more efficient and profound, but you’ll also avoid generating pages-long reports to display static results. All your information will be displayed in a single interactive page that can be filtered and explored upon need.  

• Customize the reports 

This is a tip that is valuable for any kind of research report, especially when it comes to agencies that are reporting to external clients. Customizing the report to match your client’s colors, logo, font, and overall branding will help them grasp the data better, thanks to a familiar environment. This is an invaluable tip as often your audience will not feel comfortable dealing with data and might find it hard to understand or intimidating. Therefore, providing a familiar look that is also interactive and easier to understand will keep them engaged and collaborative throughout the process. 

Plus, customizing the overall appearance of the report will also make your agency look more professional, adding extra value to your service. 

• Know your design essentials 

When you’re presenting your market research reports sample to internal or external stakeholders, having a firm grasp on fundamental design principles will make your metrics and insights far more persuasive and compelling.

By arranging your metrics in a balanced and logical format, you can guide users toward key pockets of information exactly when needed. In turn, this will improve decision-making and navigation, making your reports as impactful as possible.

For essential tips, read our 23 dashboard design principles & best practices to enhance your analytics process.

• Think of security and privacy 

Cyberattacks are increasing at a concerning pace, making security a huge priority for organizations of all sizes today. The costs of having your sensitive information leaked are not only financial but also reputational, as customers might not trust you again if their data ends up in the wrong hands. Given that market research analysis is often performed by agencies that handle data from clients, security and privacy should be a top priority.  

To ensure the required security and privacy, it is necessary to invest in the right tools to present your research results. For instance, tools such as datapine offer enterprise-level security protocols that ensure your information is encrypted and protected at all times. Plus, the tool also offers additional security features, such as being able to share your reports through a password-protected URL or to set viewer rights to ensure only the right people can access and manipulate the data. 

• Keep on improving & evolving

Each time you gather or gain new marketing research reports or market research analysis report intel, you should aim to refine your existing dashboards to reflect the ever-changing landscape around you.

If you update your reports and dashboards according to the new research you conduct and new insights you connect with, you will squeeze maximum value from your metrics, enjoying consistent development in the process.

Types of Market Research Reports: Primary & Secondary Research

With so many market research examples and such little time, knowing how to best present your insights under pressure can prove tricky.

To squeeze every last drop of value from your market research efforts and empower everyone with access to the right information, you should arrange your information into two main groups: primary research and secondary research.

A. Primary research

Primary research is based on acquiring direct or first-hand information related to your industry or sector and the customers linked to it.

Exploratory primary research is an initial form of information collection where your team might set out to identify potential issues, opportunities, and pain points related to your business or industry. This type of research is usually carried out in the form of general surveys or open-ended consumer Q&As, which nowadays are often performed online rather than offline . 

Specific primary research is definitive, with information gathered based on the issues, information, opportunities, or pain points your business has already uncovered. When doing this kind of research, you can drill down into a specific segment of your customers and seek answers to the opportunities, issues, or pain points in question.

When you’re conducting primary research to feed into your market research reporting efforts, it’s important to find reliable information sources. The most effective primary research sources include:

• Consumer-based statistical data
• Social media content
• Polls and Q&A
• Trend-based insights
• Competitor research
• First-hand interviews

B. Secondary research

Secondary research refers to every strand of relevant data or public records you have to gain a deeper insight into your market and target consumers. These sources include trend reports, market stats, industry-centric content, and sales insights you have at your disposal.  Secondary research is an effective way of gathering valuable intelligence about your competitors. 

You can gather very precise, insightful secondary market research insights from:

• Public records and resources like Census data, governmental reports, or labor stats
• Commercial resources like Gartner, Statista, or Forrester
• Articles, documentaries, and interview transcripts

Another essential branch of both primary and secondary research is internal intelligence. When it comes to efficient market research reporting examples that will benefit your organization, looking inward is a powerful move. 

Existing sales, demographic, or marketing performance insights will lead you to valuable conclusions. Curating internal information will ensure your market research discoveries are well-rounded while helping you connect with the information that will ultimately give you a panoramic view of your target market. 

By understanding both types of research and how they can offer value to your business, you can carefully choose the right informational sources, gather a wide range of intelligence related to your specific niche, and, ultimately, choose the right market research report sample for your specific needs.

If you tailor your market research report format to the type of research you conduct, you will present your visualizations in a way that provides the right people with the right insights, rather than throwing bundles of facts and figures on the wall, hoping that some of them stick.

Taking ample time to explore a range of primary and secondary sources will give your discoveries genuine context. By doing so, you will have a wealth of actionable consumer and competitor insights at your disposal at every stage of your organization’s development (a priceless weapon in an increasingly competitive digital age). 

Dynamic market research is the cornerstone of business development, and a dashboard builder is the vessel that brings these all-important insights to life. Once you get into that mindset, you will ensure that your research results always deliver maximum value.

Common Challenges & Mistakes Of Market Research Reporting & Analysis

We’ve explored different types of market research analysis examples and considered how to conduct effective research. Now, it’s time to look at the key mistakes of market research reporting.  Let’s start with the mistakes.

The mistakes

One of the biggest mistakes that stunt the success of a company’s market research efforts is strategy. Without taking the time to gather an adequate mix of insights from various sources and define your key aims or goals, your processes will become disjointed. You will also suffer from a severe lack of organizational vision.

For your market research-centric strategy to work, everyone within the company must be on the same page. Your core aims and objectives must align throughout the business, and everyone must be clear on their specific role. If you try to craft a collaborative strategy and decide on your informational sources from the very start of your journey, your strategy will deliver true growth and intelligence.

• Measurement

Another classic market research mistake is measurement – or, more accurately, a lack of precise measurement. When embarking on market intelligence gathering processes, many companies fail to select the right KPIs and set the correct benchmarks for the task at hand. Without clearly defined goals, many organizations end up with a market analysis report format that offers little or no value in terms of decision-making or market insights.

To drive growth with your market research efforts, you must set clearly defined KPIs that align with your specific goals, aims, and desired outcomes.

• Competition

A common mistake among many new or scaling companies is failing to explore and examine the competition. This will leave you with gaping informational blindspots. To truly benefit from market research, you must gather valuable nuggets of information from every key source available. Rather than solely looking at your consumers and the wider market (which is incredibly important), you should take the time to see what approach your direct competitors have adopted while getting to grips with the content and communications.

One of the most effective ways of doing so (and avoiding such a monumental market research mistake) is by signing up for your competitors’ mailing lists, downloading their apps, and examining their social media content. This will give you inspiration for your own efforts while allowing you to exploit any gaps in the market that your competitors are failing to fill.

The challenges

• Informational quality

We may have an almost infinite wealth of informational insights at our fingertips, but when it comes to market research, knowing which information to trust can prove an uphill struggle.

When working with metrics, many companies risk connecting with inaccurate insights or leading to a fruitless informational rabbit hole, wasting valuable time and resources in the process. To avoid such a mishap, working with a trusted modern market research and analysis sample is the only way forward.

• Senior buy-in

Another pressing market research challenge that stunts organizational growth is the simple case of senior buy-in. While almost every senior decision-maker knows that market research is an essential component of a successful commercial strategy, many are reluctant to invest an ample amount of time or money in the pursuit.

The best way to overcome such a challenge is by building a case that defines exactly how your market research strategies will offer a healthy ROI to every key aspect of the organization, from marketing and sales to customer experience (CX) and beyond.

• Response rates

Low interview, focus group, or poll response rates can have a serious impact on the success and value of your market research strategy. Even with adequate senior buy-in, you can’t always guarantee that you will get enough responses from early-round interviews or poll requests. If you don’t, your market research discoveries run the risk of being shallow or offering little in the way of actionable insight.

To overcome this common challenge, you can improve the incentive you offer your market research prospects while networking across various platforms to discover new contact opportunities. Changing the tone of voice of your ads or emails will also help boost your consumer or client response rates.

Bringing Your Reports a Step Further

Even if it is still widespread for market-style research results presentation, using PowerPoint at this stage is a hassle and presents many downsides and complications. When busy managers or short-on-time top executives grab a report, they want a quick overview that gives them an idea of the results and the big picture that addresses the objectives: they need a dashboard. This can be applied to all areas of a business that need fast and interactive data visualizations to support their decision-making.

We all know that a picture conveys more information than simple text or figures, so managing to bring it all together on an actionable dashboard will convey your message more efficiently. Besides, market research dashboards have the incredible advantage of always being up-to-date since they work with real-time insights: the synchronization/updating nightmare of dozens of PowerPoint slides doesn’t exist for you anymore. This is particularly helpful for tracking studies performed over time that recurrently need their data to be updated with more recent ones.

In today’s fast-paced business environment, companies must identify and grab new opportunities as they arise while staying away from threats and adapting quickly. In order to always be a step further and make the right decisions, it is critical to perform market research studies to get the information needed and make important decisions with confidence.

We’ve asked the question, “What is a market research report?”, and examined the dynamics of a modern market research report example, and one thing’s for sure: a visual market research report is the best way to understand your customer and thus increase their satisfaction by meeting their expectations head-on. 

From looking at a sample of a market research report, it’s also clear that modern dashboards help you see what is influencing your business with clarity, understand where your brand is situated in the market, and gauge the temperature of your niche or industry before a product or service launch. Once all the studies are done, you must present them efficiently to ensure everyone in the business can make the right decisions that result in real progress. Market research reports are your key allies in the matter.

To start presenting your results with efficient, interactive, dynamic research reports and win on tomorrow’s commercial battlefield, try our dashboard reporting software and test every feature with our 14-day free trial !

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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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?

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

Turn market research into insights

Save time, highlight crucial insights, and drive strategic decision-making

Last updated

22 July 2023

Reviewed by

To outlast competitors, your business needs to stay ahead of the curve. To do this, you need to have your finger on the pulse of the market.

Conducting a market analysis can provide you with detailed information about all areas of your industry and help guide decisions for the greatest growth potential.

Benefits of conducting a market analysis

A market analysis is one of the things a business can do that benefits nearly every facet of the business. From your marketing team to your product development manager, all the way up to the CEO, the insights provided by a market analysis will help to drive important decisions and push the business forward. 

Some of the ways in which it can do that are:

Identifying customer needs and preferences

Your reputation is made or broken by how well you meet the needs and preferences of your target customers. Market analysis gives you deep insights into those needs and preferences, allowing you to tailor your products, services, and marketing strategies to better meet them. You'll build better customer satisfaction and increase brand loyalty in the process.

Identifying competitors and market share

You don't just have to meet your customers' needs; you have to do a better job of it than your competitors. This will not be possible if you don't understand the strengths and weaknesses of those competitors. A market analysis can provide that information, giving you the data you need to set yourself apart from them.

Identifying market opportunities and threats

Markets aren't static. Your business can't be static, either. Through ongoing market analysis, you'll identify opportunities and threats as they occur, allowing you to pivot gracefully to best handle those situations. You'll be able to better predict opportunities for growth and better prepare for potential threats such as new competitors or changing market conditions.

Enhancing product development and innovation

With more information about customer needs and preferences and deeper insight into emerging market trends, you'll be positioned nicely for a more efficient product development process. You'll be able to make product decisions quickly based on the knowledge you've gained and develop products the market will love.

Supporting business planning and strategy

Data plays an important role in planning and decision-making from the very first days of a startup to a large corporation planning its next few years. A market analysis helps you identify target markets, build your value proposition, and set realistic goals and objectives. They can help guide the feasibility of new business ventures or business expansions.

Component of a market analysis

A market analysis consists primarily of three components. Although they overlap, each focuses the bulk of its intent on one specific area of analysis. 

Industry examination

This part of the analysis is focused on the specific industry you operate in or are hoping to expand into. It examines the trends, characteristics, and dynamics of the industry. 

To do so, it looks at the key players in the industry and its market size and growth rate. It also examines factors impacting entry into the market, such as technological barriers, regulatory requirements, supply chain logistics, and more.

The industry analysis can be broken down into the following steps:

Industry size and growth — Determine the market size and growth rate. For a complete picture, consider historical data and future projections.

Industry structure — Identify the key players, market segments, and distribution channels within the industry. When prudent, focus on the region you'll be working within.

Market trends — Analyze the current and emerging trends, innovations, and technologies influencing the industry. Look for opportunities to capitalize on those trends.

Competitive forces — Assess the competitive landscape. Look at the bargaining power of buyers and suppliers and competitive rivalry within the industry.

Regulatory and legal factors — Examine any policies, regulations, or laws that must be accounted for when entering the industry. When needed, consult with a lawyer familiar with the industry.

Market examination

The market examination focuses on understanding a specific target market within the industry.

When conducting a market analysis, you'll gather data about customers within the industry—their demographics, buying behavior, needs and preferences, and demand for products or services. This part of your analysis helps you identify your target audience and help you begin to form your value propositions.

Conducting the market examination portion of the market analysis consists of the following steps:

Target market segmentation — Segment customer segments based on characteristics such as demographics, psychographics, behavior, location, and other factors. This helps you decide which market segments are a good fit for you.

Customer analysis — For each segment, research the needs, preferences, motivations, and purchasing behavior of those customers. For this, you can limit yourself to only those market segments you're interested in appealing to.

Market size and growth — Gather detailed data on the market size. Examine the historical size of the market to identify any trends that might impact your perception of the market. Look at future predictions to see where the market will be in years after you've entered it.

Market trends — Examine customer behavior to determine what their needs and preferences are now, how they've changed in the future, and where they might be heading. Look also for customers' behavior in the market and the strength of their demand for products and services.

Market gaps and opportunities — Armed with your data on customers and market trends, look for any gaps in the market that currently aren't being met by the existing players in the space. Explore each gap further to examine its market viability.

Competitor examination

The final area of the market analysis is the competitor examination.

During this part of the analysis, the focus is squarely on the competitors operating in the industry. A close look will be taken at their strengths and weaknesses and the strategies they use within the market. This helps you further refine your value proposition and set yourself apart from other market players.

For the competitor examination, follow these steps:

Competitive analysis — Identify key competitors in the industry and research them thoroughly. Analyze their market share, product offerings, pricing strategies, and marketing tactics. Look at their distribution and supply channels to better understand how they function in the industry.

SWOT analysis — A SWOT analysis assesses the strengths, weaknesses, opportunities, and threats posed by competitors. It tells you what you need to be wary of when dealing with your competitors and potential avenues for gaining a competitive advantage.

Differentiation — With the help of your SWOT analysis and the other data you've gathered, look for areas where gaps in the market mesh with weaknesses in the competitive landscape. These are areas you can focus on to differentiate yourself from your competition.

Competitive advantage — Understand the value proposition of your competitors, both as they state it and as customers perceive it. These factors will identify their competitive advantages. Develop a plan to work around these advantages or turn them in your favor.

8 market analysis templates

As you can see, there are many steps within the three areas of market analysis. Getting a template to guide you through the ones you're working on can save a lot of time.

Below, we've gathered eight quality templates for some of the most important aspects of market analysis. All of the companies linked provide a host of other templates to fit other aspects of the analysis as well.

1. Market research kit

2. market analysis.

This market analysis template streamlines business market research by utilizing secondary sources and analyzing market reports and industry data. It saves time, emphasizes key insights, and informs strategic decision-making.

3. SWOT analysis

This SWOT analysis template helps assess strengths, weaknesses, opportunities, and threats in a concise and organized manner. It will help facilitate strategic planning and decision-making.

4. Risk assessment 

This risk assessment template , integrated with market analysis, enables businesses to identify and evaluate potential risks associated with market dynamics and other potential barriers.

5. Competitive analysis 

This template helps to systematically evaluate the strengths and weaknesses of competitors. It provides a structured approach to research, and it analyzes its products, services, target market, marketing strategies, and financial performance.

6. Marketing SWOT analysis

This marketing SWOT analysis template allows for evaluating a company's marketing strategies. It helps identify strengths and weaknesses internally while analyzing opportunities and threats in the market. 

7. Market segmentation

This template aids in analyzing geographic, demographic, psychographic, and behavioral segments to better understand the target audience's preferences and needs. It enables effective targeting and messaging.

8. Market potential analysis

This market potential analysis template offers a comprehensive and customizable solution for analyzing market size, trends, segmentation, SWOT analysis, and new product launch strategy.

Here are 8 templates to analyze market reports, industry data, and other relevant documents.

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How to do market research in 4 steps: a lean approach to marketing research

From pinpointing your target audience and assessing your competitive advantage, to ongoing product development and customer satisfaction efforts, market research is a practice your business can only benefit from.

Learn how to conduct quick and effective market research using a lean approach in this article full of strategies and practical examples. 

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A comprehensive (and successful) business strategy is not complete without some form of market research—you can’t make informed and profitable business decisions without truly understanding your customer base and the current market trends that drive your business.

In this article, you’ll learn how to conduct quick, effective market research  using an approach called 'lean market research'. It’s easier than you might think, and it can be done at any stage in a product’s lifecycle.

How to conduct lean market research in 4 steps

What is market research, why is market research so valuable, advantages of lean market research, 4 common market research methods, 5 common market research questions, market research faqs.

We’ll jump right into our 4-step approach to lean market research. To show you how it’s done in the real world, each step includes a practical example from Smallpdf , a Swiss company that used lean market research to reduce their tool’s error rate by 75% and boost their Net Promoter Score® (NPS) by 1%.

Research your market the lean way...

From on-page surveys to user interviews, Hotjar has the tools to help you scope out your market and get to know your customers—without breaking the bank.

The following four steps and practical examples will give you a solid market research plan for understanding who your users are and what they want from a company like yours.

1. Create simple user personas

A user persona is a semi-fictional character based on psychographic and demographic data from people who use websites and products similar to your own. Start by defining broad user categories, then elaborate on them later to further segment your customer base and determine your ideal customer profile .

How to get the data: use on-page or emailed surveys and interviews to understand your users and what drives them to your business.

How to do it right: whatever survey or interview questions you ask, they should answer the following questions about the customer:

Who are they?

What is their main goal?

What is their main barrier to achieving this goal?

Pitfalls to avoid:

Don’t ask too many questions! Keep it to five or less, otherwise you’ll inundate them and they’ll stop answering thoughtfully.

Don’t worry too much about typical demographic questions like age or background. Instead, focus on the role these people play (as it relates to your product) and their goals.

How Smallpdf did it: Smallpdf ran an on-page survey for a couple of weeks and received 1,000 replies. They learned that many of their users were administrative assistants, students, and teachers.

Next, they used the survey results to create simple user personas like this one for admins:

Who are they? Administrative Assistants.

What is their main goal? Creating Word documents from a scanned, hard-copy document or a PDF where the source file was lost.

What is their main barrier to achieving it? Converting a scanned PDF doc to a Word file.

💡Pro tip: Smallpdf used Hotjar Surveys to run their user persona survey. Our survey tool helped them avoid the pitfalls of guesswork and find out who their users really are, in their own words. 

You can design a survey and start running it in minutes with our easy-to-use drag and drop builder. Customize your survey to fit your needs, from a sleek one-question pop-up survey to a fully branded questionnaire sent via email. 

We've also created 40+ free survey templates that you can start collecting data with, including a user persona survey like the one Smallpdf used.

2. Conduct observational research

Observational research involves taking notes while watching someone use your product (or a similar product).

Overt vs. covert observation

Overt observation involves asking customers if they’ll let you watch them use your product. This method is often used for user testing and it provides a great opportunity for collecting live product or customer feedback .

Covert observation means studying users ‘in the wild’ without them knowing. This method works well if you sell a type of product that people use regularly, and it offers the purest observational data because people often behave differently when they know they’re being watched. 

Tips to do it right:

Record an entry in your field notes, along with a timestamp, each time an action or event occurs.

Make note of the users' workflow, capturing the ‘what,’ ‘why,’ and ‘for whom’ of each action.

Don’t record identifiable video or audio data without consent. If recording people using your product is helpful for achieving your research goal, make sure all participants are informed and agree to the terms.

Don’t forget to explain why you’d like to observe them (for overt observation). People are more likely to cooperate if you tell them you want to improve the product.

💡Pro tip: while conducting field research out in the wild can wield rewarding results, you can also conduct observational research remotely. Hotjar Recordings is a tool that lets you capture anonymized user sessions of real people interacting with your website. 

Observe how customers navigate your pages and products to gain an inside look into their user behavior . This method is great for conducting exploratory research with the purpose of identifying more specific issues to investigate further, like pain points along the customer journey and opportunities for optimizing conversion .

With Hotjar Recordings you can observe real people using your site without capturing their sensitive information

How Smallpdf did it: here’s how Smallpdf observed two different user personas both covertly and overtly.

Observing students (covert): Kristina Wagner, Principle Product Manager at Smallpdf, went to cafes and libraries at two local universities and waited until she saw students doing PDF-related activities. Then she watched and took notes from a distance. One thing that struck her was the difference between how students self-reported their activities vs. how they behaved (i.e, the self-reporting bias). Students, she found, spent hours talking, listening to music, or simply staring at a blank screen rather than working. When she did find students who were working, she recorded the task they were performing and the software they were using (if she recognized it).

Observing administrative assistants (overt): Kristina sent emails to admins explaining that she’d like to observe them at work, and she asked those who agreed to try to batch their PDF work for her observation day. While watching admins work, she learned that they frequently needed to scan documents into PDF-format and then convert those PDFs into Word docs. By observing the challenges admins faced, Smallpdf knew which products to target for improvement.

“Data is really good for discovery and validation, but there is a bit in the middle where you have to go and find the human.”

3. Conduct individual interviews

Interviews are one-on-one conversations with members of your target market. They allow you to dig deep and explore their concerns, which can lead to all sorts of revelations.

Listen more, talk less. Be curious.

Act like a journalist, not a salesperson. Rather than trying to talk your company up, ask people about their lives, their needs, their frustrations, and how a product like yours could help.

Ask "why?" so you can dig deeper. Get into the specifics and learn about their past behavior.

Record the conversation. Focus on the conversation and avoid relying solely on notes by recording the interview. There are plenty of services that will transcribe recorded conversations for a good price (including Hotjar!).

Avoid asking leading questions , which reveal bias on your part and pushes respondents to answer in a certain direction (e.g. “Have you taken advantage of the amazing new features we just released?).

Don't ask loaded questions , which sneak in an assumption which, if untrue, would make it impossible to answer honestly. For example, we can’t ask you, “What did you find most useful about this article?” without asking whether you found the article useful in the first place.

Be cautious when asking opinions about the future (or predictions of future behavior). Studies suggest that people aren’t very good at predicting their future behavior. This is due to several cognitive biases, from the misguided exceptionalism bias (we’re good at guessing what others will do, but we somehow think we’re different), to the optimism bias (which makes us see things with rose-colored glasses), to the ‘illusion of control’ (which makes us forget the role of randomness in future events).

How Smallpdf did it: Kristina explored her teacher user persona by speaking with university professors at a local graduate school. She learned that the school was mostly paperless and rarely used PDFs, so for the sake of time, she moved on to the admins.

A bit of a letdown? Sure. But this story highlights an important lesson: sometimes you follow a lead and come up short, so you have to make adjustments on the fly. Lean market research is about getting solid, actionable insights quickly so you can tweak things and see what works.

💡Pro tip: to save even more time, conduct remote interviews using an online user research service like Hotjar Engage , which automates the entire interview process, from recruitment and scheduling to hosting and recording.

You can interview your own customers or connect with people from our diverse pool of 200,000+ participants from 130+ countries and 25 industries. And no need to fret about taking meticulous notes—Engage will automatically transcribe the interview for you.

4. Analyze the data (without drowning in it)

The following techniques will help you wrap your head around the market data you collect without losing yourself in it. Remember, the point of lean market research is to find quick, actionable insights.

A flow model is a diagram that tracks the flow of information within a system. By creating a simple visual representation of how users interact with your product and each other, you can better assess their needs.

You’ll notice that admins are at the center of Smallpdf’s flow model, which represents the flow of PDF-related documents throughout a school. This flow model shows the challenges that admins face as they work to satisfy their own internal and external customers.

Affinity diagram

An affinity diagram is a way of sorting large amounts of data into groups to better understand the big picture. For example, if you ask your users about their profession, you’ll notice some general themes start to form, even though the individual responses differ. Depending on your needs, you could group them by profession, or more generally by industry.

We wrote a guide about how to analyze open-ended questions to help you sort through and categorize large volumes of response data. You can also do this by hand by clipping up survey responses or interview notes and grouping them (which is what Kristina does).

“For an interview, you will have somewhere between 30 and 60 notes, and those notes are usually direct phrases. And when you literally cut them up into separate pieces of paper and group them, they should make sense by themselves.”

Pro tip: if you’re conducting an online survey with Hotjar, keep your team in the loop by sharing survey responses automatically via our Slack and Microsoft Team integrations. Reading answers as they come in lets you digest the data in pieces and can help prepare you for identifying common themes when it comes time for analysis.

Hotjar lets you easily share survey responses with your team

Customer journey map

A customer journey map is a diagram that shows the way a typical prospect becomes a paying customer. It outlines their first interaction with your brand and every step in the sales cycle, from awareness to repurchase (and hopefully advocacy).

The above  customer journey map , created by our team at Hotjar, shows many ways a customer might engage with our tool. Your map will be based on your own data and business model.

📚 Read more: if you’re new to customer journey maps, we wrote this step-by-step guide to creating your first customer journey map in 2 and 1/2 days with free templates you can download and start using immediately.

Next steps: from research to results

So, how do you turn market research insights into tangible business results? Let’s look at the actions Smallpdf took after conducting their lean market research: first they implemented changes, then measured the impact.

Implement changes

Based on what Smallpdf learned about the challenges that one key user segment (admins) face when trying to convert PDFs into Word files, they improved their ‘PDF to Word’ conversion tool.

We won’t go into the details here because it involves a lot of technical jargon, but they made the entire process simpler and more straightforward for users. Plus, they made it so that their system recognized when you drop a PDF file into their ‘Word to PDF’ converter instead of the ‘PDF to Word’ converter, so users wouldn’t have to redo the task when they made that mistake. 

In other words: simple market segmentation for admins showed a business need that had to be accounted for, and customers are happier overall after Smallpdf implemented an informed change to their product.

Measure results

According to the Lean UX model, product and UX changes aren’t retained unless they achieve results.

Smallpdf’s changes produced:

A 75% reduction in error rate for the ‘PDF to Word’ converter

A 1% increase in NPS

Greater confidence in the team’s marketing efforts

"With all the changes said and done, we've cut our original error rate in four, which is huge. We increased our NPS by +1%, which isn't huge, but it means that of the users who received a file, they were still slightly happier than before, even if they didn't notice that anything special happened at all.”

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Market research (or marketing research) is any set of techniques used to gather information and better understand a company’s target market. This might include primary research on brand awareness and customer satisfaction or secondary market research on market size and competitive analysis. Businesses use this information to design better products, improve user experience, and craft a marketing strategy that attracts quality leads and improves conversion rates.

David Darmanin, one of Hotjar’s founders, launched two startups before Hotjar took off—but both companies crashed and burned. Each time, he and his team spent months trying to design an amazing new product and user experience, but they failed because they didn’t have a clear understanding of what the market demanded.

With Hotjar, they did things differently . Long story short, they conducted market research in the early stages to figure out what consumers really wanted, and the team made (and continues to make) constant improvements based on market and user research.

Without market research, it’s impossible to understand your users. Sure, you might have a general idea of who they are and what they need, but you have to dig deep if you want to win their loyalty.

Here’s why research matters:

Obsessing over your users is the only way to win. If you don’t care deeply about them, you’ll lose potential customers to someone who does.

Analytics gives you the ‘what’, while research gives you the ‘why’. Big data, user analytics , and dashboards can tell you what people do at scale, but only research can tell you what they’re thinking and why they do what they do. For example, analytics can tell you that customers leave when they reach your pricing page, but only research can explain why.

Research beats assumptions, trends, and so-called best practices. Have you ever watched your colleagues rally behind a terrible decision? Bad ideas are often the result of guesswork, emotional reasoning, death by best practices , and defaulting to the Highest Paid Person’s Opinion (HiPPO). By listening to your users and focusing on their customer experience , you’re less likely to get pulled in the wrong direction.

Research keeps you from planning in a vacuum. Your team might be amazing, but you and your colleagues simply can’t experience your product the way your customers do. Customers might use your product in a way that surprises you, and product features that seem obvious to you might confuse them. Over-planning and refusing to test your assumptions is a waste of time, money, and effort because you’ll likely need to make changes once your untested business plan gets put into practice.

Lean User Experience (UX) design is a model for continuous improvement that relies on quick, efficient research to understand customer needs and test new product features.

Lean market research can help you become more...

Efficient: it gets you closer to your customers, faster.

Cost-effective: no need to hire an expensive marketing firm to get things started.

Competitive: quick, powerful insights can place your products on the cutting edge.

As a small business or sole proprietor, conducting lean market research is an attractive option when investing in a full-blown research project might seem out of scope or budget.

There are lots of different ways you could conduct market research and collect customer data, but you don’t have to limit yourself to just one research method. Four common types of market research techniques include surveys, interviews, focus groups, and customer observation.

Which method you use may vary based on your business type: ecommerce business owners have different goals from SaaS businesses, so it’s typically prudent to mix and match these methods based on your particular goals and what you need to know.

1. Surveys: the most commonly used

Surveys are a form of qualitative research that ask respondents a short series of open- or closed-ended questions, which can be delivered as an on-screen questionnaire or via email. When we asked 2,000 Customer Experience (CX) professionals about their company’s approach to research , surveys proved to be the most commonly used market research technique.

What makes online surveys so popular?  

They’re easy and inexpensive to conduct, and you can do a lot of data collection quickly. Plus, the data is pretty straightforward to analyze, even when you have to analyze open-ended questions whose answers might initially appear difficult to categorize.

We've built a number of survey templates ready and waiting for you. Grab a template and share with your customers in just a few clicks.

💡 Pro tip: you can also get started with Hotjar AI for Surveys to create a survey in mere seconds . Just enter your market research goal and watch as the AI generates a survey and populates it with relevant questions. 

Once you’re ready for data analysis, the AI will prepare an automated research report that succinctly summarizes key findings, quotes, and suggested next steps.

An example research report generated by Hotjar AI for Surveys

2. Interviews: the most insightful

Interviews are one-on-one conversations with members of your target market. Nothing beats a face-to-face interview for diving deep (and reading non-verbal cues), but if an in-person meeting isn’t possible, video conferencing is a solid second choice.

Regardless of how you conduct it, any type of in-depth interview will produce big benefits in understanding your target customers.

What makes interviews so insightful?

By speaking directly with an ideal customer, you’ll gain greater empathy for their experience , and you can follow insightful threads that can produce plenty of 'Aha!' moments.

3. Focus groups: the most unreliable

Focus groups bring together a carefully selected group of people who fit a company’s target market. A trained moderator leads a conversation surrounding the product, user experience, or marketing message to gain deeper insights.

What makes focus groups so unreliable?

If you’re new to market research, we wouldn’t recommend starting with focus groups. Doing it right is expensive , and if you cut corners, your research could fall victim to all kinds of errors. Dominance bias (when a forceful participant influences the group) and moderator style bias (when different moderator personalities bring about different results in the same study) are two of the many ways your focus group data could get skewed.

4. Observation: the most powerful

During a customer observation session, someone from the company takes notes while they watch an ideal user engage with their product (or a similar product from a competitor).

What makes observation so clever and powerful?

‘Fly-on-the-wall’ observation is a great alternative to focus groups. It’s not only less expensive, but you’ll see people interact with your product in a natural setting without influencing each other. The only downside is that you can’t get inside their heads, so observation still isn't a recommended replacement for customer surveys and interviews.

The following questions will help you get to know your users on a deeper level when you interview them. They’re general questions, of course, so don’t be afraid to make them your own.

1. Who are you and what do you do?

How you ask this question, and what you want to know, will vary depending on your business model (e.g. business-to-business marketing is usually more focused on someone’s profession than business-to-consumer marketing).

It’s a great question to start with, and it’ll help you understand what’s relevant about your user demographics (age, race, gender, profession, education, etc.), but it’s not the be-all-end-all of market research. The more specific questions come later.

2. What does your day look like?

This question helps you understand your users’ day-to-day life and the challenges they face. It will help you gain empathy for them, and you may stumble across something relevant to their buying habits.

3. Do you ever purchase [product/service type]?

This is a ‘yes or no’ question. A ‘yes’ will lead you to the next question.

4. What problem were you trying to solve or what goal were you trying to achieve?

This question strikes to the core of what someone’s trying to accomplish and why they might be willing to pay for your solution.

5. Take me back to the day when you first decided you needed to solve this kind of problem or achieve this goal.

This is the golden question, and it comes from Adele Revella, Founder and CEO of Buyer Persona Institute . It helps you get in the heads of your users and figure out what they were thinking the day they decided to spend money to solve a problem.

If you take your time with this question, digging deeper where it makes sense, you should be able to answer all the relevant information you need to understand their perspective.

“The only scripted question I want you to ask them is this one: take me back to the day when you first decided that you needed to solve this kind of problem or achieve this kind of a goal. Not to buy my product, that’s not the day. We want to go back to the day that when you thought it was urgent and compelling to go spend money to solve a particular problem or achieve a goal. Just tell me what happened.”

— Adele Revella , Founder/CEO at Buyer Persona Institute

Bonus question: is there anything else you’d like to tell me?

This question isn’t just a nice way to wrap it up—it might just give participants the opportunity they need to tell you something you really need to know.

That’s why Sarah Doody, author of UX Notebook , adds it to the end of her written surveys.

“I always have a last question, which is just open-ended: “Is there anything else you would like to tell me?” And sometimes, that’s where you get four paragraphs of amazing content that you would never have gotten if it was just a Net Promoter Score [survey] or something like that.”

What is the difference between qualitative and quantitative research?

Qualitative research asks questions that can’t be reduced to a number, such as, “What is your job title?” or “What did you like most about your customer service experience?” 

Quantitative research asks questions that can be answered with a numeric value, such as, “What is your annual salary?” or “How was your customer service experience on a scale of 1-5?”

 → Read more about the differences between qualitative and quantitative user research .

How do I do my own market research?

You can do your own quick and effective market research by 

Surveying your customers

Building user personas

Studying your users through interviews and observation

Wrapping your head around your data with tools like flow models, affinity diagrams, and customer journey maps

What is the difference between market research and user research?

Market research takes a broad look at potential customers—what problems they’re trying to solve, their buying experience, and overall demand. User research, on the other hand, is more narrowly focused on the use (and usability ) of specific products.

What are the main criticisms of market research?

Many marketing professionals are critical of market research because it can be expensive and time-consuming. It’s often easier to convince your CEO or CMO to let you do lean market research rather than something more extensive because you can do it yourself. It also gives you quick answers so you can stay ahead of the competition.

Do I need a market research firm to get reliable data?

Absolutely not! In fact, we recommend that you start small and do it yourself in the beginning. By following a lean market research strategy, you can uncover some solid insights about your clients. Then you can make changes, test them out, and see whether the results are positive. This is an excellent strategy for making quick changes and remaining competitive.

Net Promoter, Net Promoter System, Net Promoter Score, NPS, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld, and Satmetrix Systems, Inc.

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What is Data Analysis? Definition, Tools, Examples

Appinio Research · 11.04.2024 · 35min read

Have you ever wondered how businesses make decisions, scientists uncover new discoveries, or governments tackle complex challenges? The answer often lies in data analysis. In today's data-driven world, organizations and individuals alike rely on data analysis to extract valuable insights from vast amounts of information. Whether it's understanding customer preferences, predicting future trends, or optimizing processes, data analysis plays a crucial role in driving informed decision-making and problem-solving. This guide will take you through the fundamentals of analyzing data, exploring various techniques and tools used in the process, and understanding the importance of data analysis in different domains. From understanding what data analysis is to delving into advanced techniques and best practices, this guide will equip you with the knowledge and skills to harness the power of data and unlock its potential to drive success and innovation.

What is Data Analysis?

Data analysis is the process of examining, cleaning, transforming, and interpreting data to uncover insights, identify patterns, and make informed decisions. It involves applying statistical, mathematical, and computational techniques to understand the underlying structure and relationships within the data and extract actionable information from it. Data analysis is used in various domains, including business, science, healthcare, finance, and government, to support decision-making, solve complex problems, and drive innovation.

Importance of Data Analysis

Data analysis is crucial in modern organizations and society, providing valuable insights and enabling informed decision-making across various domains. Here are some key reasons why data analysis is important:

• Informed Decision-Making:  Data analysis enables organizations to make evidence-based decisions by providing insights into past trends, current performance, and future predictions.
• Improved Efficiency:  By analyzing data, organizations can identify inefficiencies, streamline processes, and optimize resource allocation, leading to increased productivity and cost savings.
• Identification of Opportunities:  Data analysis helps organizations identify market trends, customer preferences, and emerging opportunities, allowing them to capitalize on new business prospects and stay ahead of competitors.
• Risk Management:  Data analysis enables organizations to assess and mitigate risks by identifying potential threats, vulnerabilities, and opportunities for improvement.
• Performance Evaluation:  Data analysis allows organizations to measure and evaluate their performance against key metrics and objectives, facilitating continuous improvement and accountability.
• Innovation and Growth:  By analyzing data, organizations can uncover new insights, discover innovative solutions, and drive growth through product development, process optimization, and strategic initiatives.
• Personalization and Customer Satisfaction:  Data analysis enables organizations to understand customer behavior, preferences, and needs, allowing them to deliver personalized products, services, and experiences that enhance customer satisfaction and loyalty .
• Regulatory Compliance:  Data analysis helps organizations ensure compliance with regulations and standards by monitoring and analyzing data for compliance-related issues, such as fraud, security breaches, and data privacy violations.

Overall, data analysis empowers organizations to harness the power of data to drive strategic decision-making, improve performance, and achieve their goals and objectives.

Understanding Data

Understanding the nature of data is fundamental to effective data analysis. It involves recognizing the types of data, their sources, methods of collection, and the crucial process of cleaning and preprocessing data before analysis.

Types of Data

Data can be broadly categorized into two main types: quantitative and qualitative data .

• Quantitative data:  This type of data represents quantities and is measurable. It deals with numbers and numerical values, allowing for mathematical calculations and statistical analysis. Examples include age, height, temperature, and income.
• Qualitative data:  Qualitative data describes qualities or characteristics and cannot be expressed numerically. It focuses on qualities, opinions, and descriptions that cannot be measured. Examples include colors, emotions, opinions, and preferences.

Understanding the distinction between these two types of data is essential as it influences the choice of analysis techniques and methods.

Data Sources

Data can be obtained from various sources, depending on the nature of the analysis and the project's specific requirements.

• Internal databases:  Many organizations maintain internal databases that store valuable information about their operations, customers, products, and more. These databases often contain structured data that is readily accessible for analysis.
• External sources:  External data sources provide access to a wealth of information beyond an organization's internal databases. This includes data from government agencies, research institutions, public repositories, and third-party vendors. Examples include census data, market research reports, and social media data.
• Sensor data:  With the proliferation of IoT (Internet of Things) devices, sensor data has become increasingly valuable for various applications. These devices collect data from the physical environment, such as temperature, humidity, motion, and location, providing real-time insights for analysis.

Understanding the available data sources is crucial for determining the scope and scale of the analysis and ensuring that the data collected is relevant and reliable.

Data Collection Methods

The process of collecting data can vary depending on the research objectives, the nature of the data, and the target population. Various data collection methods are employed to gather information effectively.

• Surveys :  Surveys involve collecting information from individuals or groups through questionnaires, interviews, or online forms. Surveys are versatile and can be conducted in various formats, including face-to-face interviews, telephone interviews, paper surveys, and online surveys.
• Observational studies:  Observational studies involve observing and recording behavior, events, or phenomena in their natural settings without intervention. This method is often used in fields such as anthropology, sociology, psychology, and ecology to gather qualitative data.
• Experiments:  Experiments are controlled investigations designed to test hypotheses and determine cause-and-effect relationships between variables. They involve manipulating one or more variables while keeping others constant to observe the effect on the dependent variable.

Understanding the strengths and limitations of different data collection methods is essential for designing robust research studies and ensuring the quality and validity of the data collected. For businesses seeking efficient and insightful data collection, Appinio offers a seamless solution.

With its user-friendly interface and comprehensive features, Appinio simplifies the process of gathering valuable insights from diverse audiences. Whether conducting surveys, observational studies, or experiments, Appinio provides the tools and support needed to collect, analyze, and interpret data effectively.

Ready to elevate your data collection efforts? Book a demo today and experience the power of real-time market research with Appinio!

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Data Cleaning and Preprocessing

Data cleaning and preprocessing are essential steps in the data analysis process aimed at improving data quality, consistency, and reliability.

• Handling missing values:  Missing values are common in datasets and can arise due to various reasons, such as data entry errors, equipment malfunction, or non-response. Techniques for handling missing values include deletion, imputation, and predictive modeling.
• Dealing with outliers:  Outliers are data points that deviate significantly from the rest of the data and may distort the analysis results. It's essential to identify and handle outliers appropriately using statistical methods, visualization techniques, or domain knowledge.
• Standardizing data:  Standardization involves scaling variables to a common scale to facilitate comparison and analysis. It ensures that variables with different units or scales contribute equally to the analysis results. Standardization techniques include z-score normalization, min-max scaling, and robust scaling.

By cleaning and preprocessing the data effectively, you can ensure that it is accurate, consistent, and suitable for analysis, leading to more reliable and actionable insights.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial phase in the data analysis process, where you explore and summarize the main characteristics of your dataset. This phase helps you gain insights into the data, identify patterns, and detect anomalies or outliers. Let's delve into the key components of EDA.

Descriptive Statistics

Descriptive statistics provide a summary of the main characteristics of your dataset, allowing you to understand its central tendency, variability, and distribution. Standard descriptive statistics include measures such as mean, median, mode, standard deviation, variance, and range.

• Mean: The average value of a dataset, calculated by summing all values and dividing by the number of observations. Mean = (Sum of all values) / (Number of observations)
• Median:  The middle value of a dataset when it is ordered from least to greatest.
• Mode:  The value that appears most frequently in a dataset.
• Standard deviation:  A measure of the dispersion or spread of values around the mean. Standard deviation = Square root of [(Sum of squared differences from the mean) / (Number of observations)]
• Variance: The average of the squared differences from the mean. Variance = Sum of squared differences from the mean / Number of observations
• Range:  The difference between the maximum and minimum values in a dataset.

Descriptive statistics provide initial insights into the central tendencies and variability of the data, helping you identify potential issues or areas for further exploration.

Data Visualization Techniques

Data visualization is a powerful tool for exploring and communicating insights from your data. By representing data visually, you can identify patterns, trends, and relationships that may not be apparent from raw numbers alone. Common data visualization techniques include:

• Histograms:  A graphical representation of the distribution of numerical data divided into bins or intervals.
• Scatter plots:  A plot of individual data points on a two-dimensional plane, useful for visualizing relationships between two variables.
• Box plots:  A graphical summary of the distribution of a dataset, showing the median, quartiles, and outliers.
• Bar charts:  A visual representation of categorical data using rectangular bars of varying heights or lengths.
• Heatmaps :  A visual representation of data in a matrix format, where values are represented using colors to indicate their magnitude.

Data visualization allows you to explore your data from different angles, uncover patterns, and communicate insights effectively to stakeholders.

Identifying Patterns and Trends

During EDA, you'll analyze your data to identify patterns, trends, and relationships that can provide valuable insights into the underlying processes or phenomena.

• Time series analysis:  Analyzing data collected over time to identify temporal patterns, seasonality, and trends.
• Correlation analysis:  Examining the relationships between variables to determine if they are positively, negatively, or not correlated.
• Cluster analysis:  Grouping similar data points together based on their characteristics to identify natural groupings or clusters within the data.
• Principal Component Analysis (PCA):  A dimensionality reduction technique used to identify the underlying structure in high-dimensional data and visualize it in lower-dimensional space.

By identifying patterns and trends in your data, you can uncover valuable insights that can inform decision-making and drive business outcomes.

Handling Missing Values and Outliers

Missing values and outliers can distort the results of your analysis, leading to biased conclusions or inaccurate predictions. It's essential to handle them appropriately during the EDA phase. Techniques for handling missing values include:

• Deletion:  Removing observations with missing values from the dataset.
• Imputation:  Filling in missing values using methods such as mean imputation, median imputation, or predictive modeling.
• Detection and treatment of outliers:  Identifying outliers using statistical methods or visualization techniques and either removing them or transforming them to mitigate their impact on the analysis.

By addressing missing values and outliers, you can ensure the reliability and validity of your analysis results, leading to more robust insights and conclusions.

Data Analysis Examples

Data analysis spans various industries and applications. Here are a few examples showcasing the versatility and power of data-driven insights.

Business and Marketing

Data analysis is used to understand customer behavior, optimize marketing strategies, and drive business growth. For instance, a retail company may analyze sales data to identify trends in customer purchasing behavior, allowing them to tailor their product offerings and promotional campaigns accordingly.

Similarly, marketing teams use data analysis techniques to measure the effectiveness of advertising campaigns, segment customers based on demographics or preferences, and personalize marketing messages to improve engagement and conversion rates.

Healthcare and Medicine

In healthcare, data analysis is vital in improving patient outcomes, optimizing treatment protocols, and advancing medical research. For example, healthcare providers may analyze electronic health records (EHRs) to identify patterns in patient symptoms, diagnoses, and treatment outcomes, helping to improve diagnostic accuracy and treatment effectiveness.

Pharmaceutical companies use data analysis techniques to analyze clinical trial data, identify potential drug candidates, and optimize drug development processes, ultimately leading to the discovery of new treatments and therapies for various diseases and conditions.

Finance and Economics

Data analysis is used to inform investment decisions, manage risk, and detect fraudulent activities. For instance, investment firms analyze financial market data to identify trends, assess market risk, and make informed investment decisions.

Banks and financial institutions use data analysis techniques to detect fraudulent transactions, identify suspicious activity patterns, and prevent financial crimes such as money laundering and fraud. Additionally, economists use data analysis to analyze economic indicators, forecast economic trends, and inform policy decisions at the national and global levels.

Science and Research

Data analysis is essential for generating insights, testing hypotheses, and advancing knowledge in various fields of scientific research. For example, astronomers analyze observational data from telescopes to study the properties and behavior of celestial objects such as stars, galaxies, and black holes.

Biologists use data analysis techniques to analyze genomic data, study gene expression patterns, and understand the molecular mechanisms underlying diseases. Environmental scientists use data analysis to monitor environmental changes, track pollution levels, and assess the impact of human activities on ecosystems and biodiversity.

These examples highlight the diverse applications of data analysis across different industries and domains, demonstrating its importance in driving innovation, solving complex problems, and improving decision-making processes.

Statistical Analysis

Statistical analysis is a fundamental aspect of data analysis, enabling you to draw conclusions, make predictions, and infer relationships from your data. Let's explore various statistical techniques commonly used in data analysis.

Hypothesis Testing

Hypothesis testing is a method used to make inferences about a population based on sample data. It involves formulating a hypothesis about the population parameter and using sample data to determine whether there is enough evidence to reject or fail to reject the null hypothesis.

Common types of hypothesis tests include:

• t-test:  Used to compare the means of two groups and determine if they are significantly different from each other.
• Chi-square test:  Used to determine whether there is a significant association between two categorical variables.
• ANOVA (Analysis of Variance):  Used to compare means across multiple groups to determine if there are significant differences.

Correlation Analysis

Correlation analysis is used to measure the strength and direction of the relationship between two variables. The correlation coefficient, typically denoted by "r," ranges from -1 to 1, where:

• r = 1:  Perfect positive correlation
• r = -1:  Perfect negative correlation
• r = 0:  No correlation

Common correlation coefficients include:

• Pearson correlation coefficient:  Measures the linear relationship between two continuous variables.
• Spearman rank correlation coefficient:  Measures the strength and direction of the monotonic relationship between two variables, particularly useful for ordinal data .

Correlation analysis helps you understand the degree to which changes in one variable are associated with changes in another variable.

Regression Analysis

Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It aims to predict the value of the dependent variable based on the values of the independent variables. Common types of regression analysis include:

• Linear regression:  Models the relationship between the dependent variable and one or more independent variables using a linear equation. It is suitable for predicting continuous outcomes.
• Logistic regression:  Models the relationship between a binary dependent variable and one or more independent variables. It is commonly used for classification tasks.

Regression analysis helps you understand how changes in one or more independent variables are associated with changes in the dependent variable.

ANOVA (Analysis of Variance)

ANOVA is a statistical technique used to analyze the differences among group means in a sample. It is often used to compare means across multiple groups and determine whether there are significant differences between them. ANOVA tests the null hypothesis that the means of all groups are equal against the alternative hypothesis that at least one group mean is different.

ANOVA can be performed in various forms, including:

• One-way ANOVA:  Used when there is one categorical independent variable with two or more levels and one continuous dependent variable.
• Two-way ANOVA:  Used when there are two categorical independent variables and one continuous dependent variable.
• Repeated measures ANOVA:  Used when measurements are taken on the same subjects at different time points or under different conditions.

ANOVA is a powerful tool for comparing means across multiple groups and identifying significant differences that may exist between them.

Machine Learning for Data Analysis

Machine learning is a powerful subset of artificial intelligence that focuses on developing algorithms capable of learning from data to make predictions or decisions.

Introduction to Machine Learning

Machine learning algorithms learn from historical data to identify patterns and make predictions or decisions without being explicitly programmed. The process involves training a model on labeled data (supervised learning) or unlabeled data (unsupervised learning) to learn the underlying patterns and relationships.

Key components of machine learning include:

• Features:  The input variables or attributes used to train the model.
• Labels:  The output variable that the model aims to predict in supervised learning.
• Training data:  The dataset used to train the model.
• Testing data:  The dataset used to evaluate the performance of the trained model.

Supervised Learning Techniques

Supervised learning involves training a model on labeled data, where the input features are paired with corresponding output labels. The goal is to learn a mapping from input features to output labels, enabling the model to make predictions on new, unseen data.

Supervised learning techniques include:

• Regression:  Used to predict a continuous target variable. Examples include linear regression for predicting house prices and logistic regression for binary classification tasks.
• Classification:  Used to predict a categorical target variable. Examples include decision trees, support vector machines, and neural networks.

Supervised learning is widely used in various domains, including finance, healthcare, and marketing, for tasks such as predicting customer churn, detecting fraudulent transactions, and diagnosing diseases.

Unsupervised Learning Techniques

Unsupervised learning involves training a model on unlabeled data, where the algorithm tries to learn the underlying structure or patterns in the data without explicit guidance.

Unsupervised learning techniques include:

• Clustering:  Grouping similar data points together based on their features. Examples include k-means clustering and hierarchical clustering.
• Dimensionality reduction:  Reducing the number of features in the dataset while preserving its essential information. Examples include principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE).

Unsupervised learning is used for tasks such as customer segmentation, anomaly detection, and data visualization.

Model Evaluation and Selection

Once a machine learning model has been trained, it's essential to evaluate its performance and select the best-performing model for deployment.

• Cross-validation:  Dividing the dataset into multiple subsets and training the model on different combinations of training and validation sets to assess its generalization performance.
• Performance metrics:  Using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve to evaluate the model's performance on the validation set.
• Hyperparameter tuning:  Adjusting the hyperparameters of the model, such as learning rate, regularization strength, and number of hidden layers, to optimize its performance.

Model evaluation and selection are critical steps in the machine learning pipeline to ensure that the deployed model performs well on new, unseen data.

Advanced Data Analysis Techniques

Advanced data analysis techniques go beyond traditional statistical methods and machine learning algorithms to uncover deeper insights from complex datasets.

Time Series Analysis

Time series analysis is a method for analyzing data collected at regular time intervals. It involves identifying patterns, trends, and seasonal variations in the data to make forecasts or predictions about future values. Time series analysis is commonly used in fields such as finance, economics, and meteorology for tasks such as forecasting stock prices, predicting sales, and analyzing weather patterns.

Key components of time series analysis include:

• Trend analysis :  Identifying long-term trends or patterns in the data, such as upward or downward movements over time.
• Seasonality analysis:  Identifying recurring patterns or cycles that occur at fixed intervals, such as daily, weekly, or monthly seasonality.
• Forecasting:  Using historical data to make predictions about future values of the time series.

Time series analysis techniques include:

• Autoregressive integrated moving average (ARIMA) models.
• Exponential smoothing methods.
• Seasonal decomposition of time series (STL).

Predictive Modeling

Predictive modeling involves using historical data to build a model that can make predictions about future events or outcomes. It is widely used in various industries for customer churn prediction, demand forecasting, and risk assessment. This involves involves:

• Data preparation:  Cleaning and preprocessing the data to ensure its quality and reliability.
• Feature selection:  Identifying the most relevant features or variables contributing to the predictive task.
• Model selection:  Choosing an appropriate machine learning algorithm or statistical technique to build the predictive model.
• Model training:  Training the model on historical data to learn the underlying patterns and relationships.
• Model evaluation:  Assessing the performance of the model on a separate validation dataset using appropriate metrics such as accuracy, precision, recall, and F1-score.

Common predictive modeling techniques include linear regression, decision trees, random forests, gradient boosting, and neural networks.

Text Mining and Sentiment Analysis

Text mining, also known as text analytics, involves extracting insights from unstructured text data. It encompasses techniques for processing, analyzing, and interpreting textual data to uncover patterns, trends, and sentiments. Text mining is used in various applications, including social media analysis, customer feedback analysis, and document classification.

Key components of text mining and sentiment analysis include:

• Text preprocessing:  Cleaning and transforming raw text data into a structured format suitable for analysis, including tasks such as tokenization, stemming, and lemmatization.
• Sentiment analysis:  Determining the sentiment or opinion expressed in text data, such as positive, negative, or neutral sentiment.
• Topic modeling:  Identifying the underlying themes or topics present in a collection of documents using techniques such as latent Dirichlet allocation (LDA).
• Named entity recognition:  Identifying and categorizing entities mentioned in text data, such as names of people, organizations, or locations.

Text mining and sentiment analysis techniques enable organizations to gain valuable insights from textual data sources and make data-driven decisions.

Network Analysis

Network analysis, also known as graph analysis, involves studying the structure and interactions of complex networks or graphs. It is used to analyze relationships and dependencies between entities in various domains, including social networks, biological networks, and transportation networks.

Key concepts in network analysis include:

• Nodes:  Represent entities or objects in the network, such as people, websites, or genes.
• Edges:  Represent relationships or connections between nodes, such as friendships, hyperlinks, or interactions.
• Centrality measures:  Quantify the importance or influence of nodes within the network, such as degree centrality, betweenness centrality, and eigenvector centrality.
• Community detection:  Identify groups or communities of nodes that are densely connected within themselves but sparsely connected to nodes in other communities.

Network analysis techniques enable researchers and analysts to uncover hidden patterns, identify key influencers, and understand the underlying structure of complex systems.

Data Analysis Software and Tools

Effective data analysis relies on the use of appropriate tools and software to process, analyze, and visualize data.

What Are Data Analysis Tools?

Data analysis tools encompass a wide range of software applications and platforms designed to assist in the process of exploring, transforming, and interpreting data. These tools provide features for data manipulation, statistical analysis, visualization, and more. Depending on the analysis requirements and user preferences, different tools may be chosen for specific tasks.

Popular Data Analysis Tools

Several software packages are widely used in data analysis due to their versatility, functionality, and community support. Some of the most popular data analysis software include:

• Python:  A versatile programming language with a rich ecosystem of libraries and frameworks for data analysis, including NumPy, pandas, Matplotlib, and scikit-learn.
• R:  A programming language and environment specifically designed for statistical computing and graphics, featuring a vast collection of packages for data analysis, such as ggplot2, dplyr, and caret.
• Excel:  A spreadsheet application that offers basic data analysis capabilities, including formulas, pivot tables, and charts. Excel is widely used for simple data analysis tasks and visualization.

These software packages cater to different user needs and skill levels, providing options for beginners and advanced users alike.

Data Collection Tools

Data collection tools are software applications or platforms that gather data from various sources, including surveys, forms, databases, and APIs. These tools provide features for designing data collection instruments, distributing surveys, and collecting responses.

Examples of data collection tools include:

• Google Forms:  A free online tool for creating surveys and forms, collecting responses, and analyzing the results.
• Appinio :  A real-time market research platform that simplifies data collection and analysis. With Appinio, businesses can easily create surveys, gather responses, and gain valuable insights to drive decision-making.

Data collection tools streamline the process of gathering and analyzing data, ensuring accuracy, consistency, and efficiency. Appinio stands out as a powerful tool for businesses seeking rapid and comprehensive data collection, empowering them to make informed decisions with ease.

Ready to experience the benefits of Appinio? Book a demo and get started today!

Data Visualization Tools

Data visualization tools enable users to create visual representations of data, such as charts, graphs, and maps, to communicate insights effectively. These tools provide features for creating interactive and dynamic visualizations that enhance understanding and facilitate decision-making.

Examples of data visualization tools include Power BI, a business analytics tool from Microsoft that enables users to visualize and analyze data from various sources, create interactive reports and dashboards, and share insights with stakeholders.

Data visualization tools play a crucial role in exploring and presenting data in a meaningful and visually appealing manner.

Data Management Platforms

Data management platforms (DMPs) are software solutions designed to centralize and manage data from various sources, including customer data, transaction data, and marketing data. These platforms provide features for data integration, cleansing, transformation, and storage, allowing organizations to maintain a single source of truth for their data.

Data management platforms help organizations streamline their data operations, improve data quality, and derive actionable insights from their data assets.

Data Analysis Best Practices

Effective data analysis requires adherence to best practices to ensure the accuracy, reliability, and validity of the results.

• Define Clear Objectives:  Clearly define the objectives and goals of your data analysis project to guide your efforts and ensure alignment with the desired outcomes.
• Understand the Data:  Thoroughly understand the characteristics and limitations of your data, including its sources, quality, structure, and any potential biases or anomalies.
• Preprocess Data:  Clean and preprocess the data to handle missing values, outliers, and inconsistencies, ensuring that the data is suitable for analysis.
• Use Appropriate Tools:  Select and use appropriate tools and software for data analysis, considering factors such as the complexity of the data, the analysis objectives, and the skills of the analysts.
• Document the Process:  Document the data analysis process, including data preprocessing steps, analysis techniques, assumptions, and decisions made, to ensure reproducibility and transparency.
• Validate Results:  Validate the results of your analysis using appropriate techniques such as cross-validation, sensitivity analysis, and hypothesis testing to ensure their accuracy and reliability.
• Visualize Data:  Use data visualization techniques to represent your findings visually, making complex patterns and relationships easier to understand and communicate to stakeholders.
• Iterate and Refine:  Iterate on your analysis process, incorporating feedback and refining your approach as needed to improve the quality and effectiveness of your analysis.
• Consider Ethical Implications:  Consider the ethical implications of your data analysis, including issues such as privacy, fairness, and bias, and take appropriate measures to mitigate any potential risks.
• Collaborate and Communicate:  Foster collaboration and communication among team members and stakeholders throughout the data analysis process to ensure alignment, shared understanding, and effective decision-making.

By following these best practices, you can enhance the rigor, reliability, and impact of your data analysis efforts, leading to more informed decision-making and actionable insights.

Data analysis is a powerful tool that empowers individuals and organizations to make sense of the vast amounts of data available to them. By applying various techniques and tools, data analysis allows us to uncover valuable insights, identify patterns, and make informed decisions across diverse fields such as business, science, healthcare, and government. From understanding customer behavior to predicting future trends, data analysis applications are virtually limitless. However, successful data analysis requires more than just technical skills—it also requires critical thinking, creativity, and a commitment to ethical practices. As we navigate the complexities of our data-rich world, it's essential to approach data analysis with curiosity, integrity, and a willingness to learn and adapt. By embracing best practices, collaborating with others, and continuously refining our approaches, we can harness the full potential of data analysis to drive innovation, solve complex problems, and create positive change in the world around us. So, whether you're just starting your journey in data analysis or looking to deepen your expertise, remember that the power of data lies not only in its quantity but also in our ability to analyze, interpret, and use it wisely.

How to Conduct Data Analysis in Minutes?

Introducing Appinio , the real-time market research platform that revolutionizes data analysis. With Appinio, companies can easily collect and analyze consumer insights in minutes, empowering them to make better, data-driven decisions swiftly. Appinio handles all the heavy lifting in research and technology, allowing clients to focus on what truly matters: leveraging real-time consumer insights for rapid decision-making.

• From questions to insights in minutes:  With Appinio, get answers to your burning questions in record time, enabling you to act swiftly on emerging trends and consumer preferences.
• No research PhD required:  Our platform is designed to be user-friendly and intuitive, ensuring that anyone, regardless of their research background, can navigate it effortlessly and extract valuable insights.
• Rapid data collection:  With an average field time of less than 23 minutes for 1,000 respondents, Appinio enables you to gather comprehensive data from a diverse range of target groups spanning over 90 countries. Plus, it offers over 1,200 characteristics to define your target audience, ensuring precise and actionable insights tailored to your needs.

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16.05.2024 | 30min read

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How To Do Market Research: Definition, Types, Methods

Jan 2, 2024

11 min. read

Market research isn’t just collecting data. It’s a strategic tool that allows businesses to gain a competitive advantage while making the best use of their resources. Research reveals valuable insights into your target audience about their preferences, buying habits, and emerging demands — all of which help you unlock new opportunities to grow your business.

When done correctly, market research can minimize risks and losses, spur growth, and position you as a leader in your industry. 

Let’s explore the basic building blocks of market research and how to collect and use data to move your company forward:

Table of Contents

What Is Market Research?

Why is market research important, market analysis example, 5 types of market research, what are common market research questions, what are the limitations of market research, how to do market research, improving your market research with radarly.

Market Research Definition: The process of gathering, analyzing, and interpreting information about a market or audience.

Market research studies consumer behavior to better understand how they perceive products or services. These insights help businesses identify ways to grow their current offering, create new products or services, and improve brand trust and brand recognition .

You might also hear market research referred to as market analysis or consumer research .

Traditionally, market research has taken the form of focus groups, surveys, interviews, and even competitor analysis . But with modern analytics and research tools, businesses can now capture deeper insights from a wider variety of sources, including social media, online reviews, and customer interactions. These extra layers of intel can help companies gain a more comprehensive understanding of their audience.

With consumer preferences and markets evolving at breakneck speeds, businesses need a way to stay in touch with what people need and want. That’s why the importance of market research cannot be overstated.

Market research offers a proactive way to identify these trends and make adjustments to product development, marketing strategies , and overall operations. This proactive approach can help businesses stay ahead of the curve and remain agile as markets shift.

Market research examples abound — given the number of ways companies can get inside the minds of their customers, simply skimming through your business’s social media comments can be a form of market research.

A restaurant chain might use market research methods to learn more about consumers’ evolving dining habits. These insights might be used to offer new menu items, re-examine their pricing strategies, or even open new locations in different markets, for example.

A consumer electronics company might use market research for similar purposes. For instance, market research may reveal how consumers are using their smart devices so they can develop innovative features.

Market research can be applied to a wide range of use cases, including:

• Testing new product ideas
• Improve existing products
• Entering new markets
• Right-sizing their physical footprints
• Improving brand image and awareness
• Gaining insights into competitors via competitive intelligence

Ultimately, companies can lean on market research techniques to stay ahead of trends and competitors while improving the lives of their customers.

Market research methods take different forms, and you don’t have to limit yourself to just one. Let’s review the most common market research techniques and the insights they deliver.

1. Interviews

3. Focus Groups

4. Observations

5. AI-Driven Market Research

One-on-one interviews are one of the most common market research techniques. Beyond asking direct questions, skilled interviewers can uncover deeper motivations and emotions that drive purchasing decisions. Researchers can elicit more detailed and nuanced responses they might not receive via other methods, such as self-guided surveys.

Interviews also create the opportunity to build rapport with customers and prospects. Establishing a connection with interviewees can encourage them to open up and share their candid thoughts, which can enrich your findings. Researchers also have the opportunity to ask clarifying questions and dig deeper based on individual responses.

Market research surveys provide an easy entry into the consumer psyche. They’re cost-effective to produce and allow researchers to reach lots of people in a short time. They’re also user-friendly for consumers, which allows companies to capture more responses from more people.

Big data and data analytics are making traditional surveys more valuable. Researchers can apply these tools to elicit a deeper understanding from responses and uncover hidden patterns and correlations within survey data that were previously undetectable.

The ways in which surveys are conducted are also changing. With the rise of social media and other online channels, brands and consumers alike have more ways to engage with each other, lending to a continuous approach to market research surveys.

3. Focus groups

Focus groups are “group interviews” designed to gain collective insights. This interactive setting allows participants to express their thoughts and feelings openly, giving researchers richer insights beyond yes-or-no responses.

One of the key benefits of using focus groups is the opportunity for participants to interact with one another. They spark discussions while sharing diverse viewpoints. These sessions can uncover underlying motivations and attitudes that may not be easily expressed through other research methods.

Observing your customers “in the wild” might feel informal, but it can be one of the most revealing market research techniques of all. That’s because you might not always know the right questions to ask. By simply observing, you can surface insights you might not have known to look for otherwise.

This method also delivers raw, authentic, unfiltered data. There’s no room for bias and no potential for participants to accidentally skew the data. Researchers can also pick up on non-verbal cues and gestures that other research methods may fail to capture.

5. AI-driven market research

One of the newer methods of market research is the use of AI-driven market research tools to collect and analyze insights on your behalf. AI customer intelligence tools and consumer insights software like Meltwater Radarly take an always-on approach by going wherever your audience is and continuously predicting behaviors based on current behaviors.

By leveraging advanced algorithms, machine learning, and big data analysis , AI enables companies to uncover deep-seated patterns and correlations within large datasets that would be near impossible for human researchers to identify. This not only leads to more accurate and reliable findings but also allows businesses to make informed decisions with greater confidence.

Tip: Learn how to use Meltwater as a research tool , how Meltwater uses AI , and learn more about consumer insights and about consumer insights in the fashion industry .

No matter the market research methods you use, market research’s effectiveness lies in the questions you ask. These questions should be designed to elicit honest responses that will help you reach your goals.

Examples of common market research questions include:

Demographic market research questions

• What is your age range?
• What is your occupation?
• What is your household income level?
• What is your educational background?
• What is your gender?

Product or service usage market research questions

• How long have you been using [product/service]?
• How frequently do you use [product/service]?
• What do you like most about [product/service]?
• Have you experienced any problems using [product/service]?
• How could we improve [product/service]?
• Why did you choose [product/service] over a competitor’s [product/service]?

Brand perception market research questions

• How familiar are you with our brand?
• What words do you associate with our brand?
• How do you feel about our brand?
• What makes you trust our brand?
• What sets our brand apart from competitors?
• What would make you recommend our brand to others?

Buying behavior market research questions

• What do you look for in a [product/service]?
• What features in a [product/service] are important to you?
• How much time do you need to choose a [product/service]?
• How do you discover new products like [product/service]?
• Do you prefer to purchase [product/service] online or in-store?
• How do you research [product/service] before making a purchase?
• How often do you buy [product/service]?
• How important is pricing when buying [product/service]?
• What would make you switch to another brand of [product/service]?

Customer satisfaction market research questions

• How happy have you been with [product/service]?
• What would make you more satisfied with [product/service]?
• How likely are you to continue using [product/service]?

Bonus Tip: Compiling these questions into a market research template can streamline your efforts.

Market research can offer powerful insights, but it also has some limitations. One key limitation is the potential for bias. Researchers may unconsciously skew results based on their own preconceptions or desires, which can make your findings inaccurate.

• Depending on your market research methods, your findings may be outdated by the time you sit down to analyze and act on them. Some methods struggle to account for rapidly changing consumer preferences and behaviors.
• There’s also the risk of self-reported data (common in online surveys). Consumers might not always accurately convey their true feelings or intentions. They might provide answers they think researchers are looking for or misunderstand the question altogether.
• There’s also the potential to miss emerging or untapped markets . Researchers are digging deeper into what (or who) they already know. This means you might be leaving out a key part of the story without realizing it.

Still, the benefits of market research cannot be understated, especially when you supplement traditional market research methods with modern tools and technology.

Let’s put it all together and explore how to do market research step-by-step to help you leverage all its benefits.

Step 1: Define your objectives

You’ll get more from your market research when you hone in on a specific goal : What do you want to know, and how will this knowledge help your business?

This step will also help you define your target audience. You’ll need to ask the right people the right questions to collect the information you want. Understand the characteristics of the audience and what gives them authority to answer your questions.

Step 2: Select your market research methods

Choose one or more of the market research methods (interviews, surveys, focus groups, observations, and/or AI-driven tools) to fuel your research strategy.

Certain methods might work better than others for specific goals . For example, if you want basic feedback from customers about a product, a simple survey might suffice. If you want to hone in on serious pain points to develop a new product, a focus group or interview might work best.

You can also source secondary research ( complementary research ) via secondary research companies , such as industry reports or analyses from large market research firms. These can help you gather preliminary information and inform your approach.

Step 3: Develop your research tools

Prior to working with participants, you’ll need to craft your survey or interview questions, interview guides, and other tools. These tools will help you capture the right information , weed out non-qualifying participants, and keep your information organized.

You should also have a system for recording responses to ensure data accuracy and privacy. Test your processes before speaking with participants so you can spot and fix inefficiencies or errors.

Step 4: Conduct the market research

With a system in place, you can start looking for candidates to contribute to your market research. This might include distributing surveys to current customers or recruiting participants who fit a specific profile, for example.

Set a time frame for conducting your research. You might collect responses over the course of a few days, weeks, or even months. If you’re using AI tools to gather data, choose a data range for your data to focus on the most relevant information.

Step 5: Analyze and apply your findings

Review your findings while looking for trends and patterns. AI tools can come in handy in this phase by analyzing large amounts of data on your behalf.

Compile your findings into an easy-to-read report and highlight key takeaways and next steps. Reports aren’t useful unless the reader can understand and act on them.

Tip: Learn more about trend forecasting , trend detection , and trendspotting .

Meltwater’s Radarly consumer intelligence suite helps you reap the benefits of market research on an ongoing basis. Using a combination of AI, data science, and market research expertise, Radarly scans multiple global data sources to learn what people are talking about, the actions they’re taking, and how they’re feeling about specific brands.

Our tools are created by market research experts and designed to help researchers uncover what they want to know (and what they don’t know they want to know). Get data-driven insights at scale with information that’s always relevant, always accurate, and always tailored to your organization’s needs.

Learn more when you request a demo by filling out the form below:

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98 Quantitative Research Questions & Examples

As researchers, we know how powerful quantitative research data can be in helping answer strategic questions. Here, I’ve detailed 23 use cases and curated 98 quantitative market research questions with examples – making this a post you should add to your bookmark list , so you can quickly refer back.

I’ve formatted this post to show you 10-15 questions for each use case. At the end of each section, I also share a quicker way to get similar insights using modern market research tools like Similarweb.

What is a quantitative research question?

Quantitative market research questions tell you the what, how, when, and where of a subject. From trendspotting to identifying patterns or establishing averages– using quantitative data is a clear and effective way to start solving business problems.

Types of quantitative research questions

Quantitative market research questions are divided into two main types: descriptive and causal.

• Descriptive research questions seek to quantify a phenomenon by focusing on a certain population or phenomenon to measure certain aspects of it, such as frequency, average, or relationship.
• Causal research questions explore the cause-and-effect relationship between two or more variables.

The ultimate list of questions for quantitative market research

Get clear explanations of the different applications and approaches to quantitative research–with the added bonus of seeing what questions to ask and how they can impact your business.

Examples of quantitative research questions for competitive analysis

A powerful example of quantitative research in play is when it’s used to inform a competitive analysis . A process that’s used to analyze and understand how industry leaders and companies of interest are performing.

Pro Tip: Collect data systematically, and use a competitive analysis framework to record your findings. You can refer back to it when you repeat the process later in the year.

• What is the market share of our major competitors?
• What is the average purchase price of our competitors’ products?
• How often do our competitors release new products?
• What is the total number of customer reviews for our competitors’ products?
• What is the average rating of our competitors’ products?
• What is the average customer satisfaction score for our competitors?
• What is the average return rate of our competitors’ products?
• What is the average shipping time for our competitors’ products?
• What is the average price discount offered by our competitors?
• What is the average lifespan of our competitors’ products?

With this data, you can determine your position in the market and benchmark your performance against rival companies. It can then be used to improve offerings, service standards, pricing, positioning, and operational effectiveness. Notice that all questions can be answered with a numerical response , a key component of all successful examples of quantitative market research questions.

Quantitative research question example: market analysis

‍♀️ Question: What is the market share of our major competitors?

Insight sought: Industry market share of leaders and key competitors.

Challenges with traditional quantitative research methods: Outdated data is a major consideration; data freshness remains critical, yet is often tricky to obtain using traditional research methods. Markets shift fast, so being able to obtain and track market share in real time is a challenge many face.

A new approach: Similarweb enables you to track this key business KPI in real-time using digital data directly from the platform. On any day, you can see what your market share is, along with any players in your market. Plus, you get to see rising stars showing significant growth, who may pose a threat through market disruption or new tactics.

⏰ Time to insight: 30 seconds

✅ How it’s done: Using Similarweb’s Web Industry Analysis, two digital metrics give you the intel needed to decipher the market share in any industry. I’m using the Banking, Credit, and Lending market throughout these examples. I’ve selected the US market, analyzing the performance of the previous 3 months.

• Share of visits 

Here, I can see the top players in my market based on the number of unique visitors to their sites. On top of the raw data that shows me the volume of visitors as a figure, I can quickly see the two players ( Capital One and Chase ) that have grown and by what percentage. On the side, you can see rising players in the industry. Now, while my initial question was to establish the market share of my major competitors, I can see there are a few disruptive players in my market who I’d want to track too; Synchrony.com being one of particular interest, given their substantial growth and traffic numbers.

• Share of search 

Viewing the overall market size based on total search volumes, you can explore industry leaders in more detail. The top websites are the top five players, ranking by traffic share . You can also view the month-over-month change in visits, which shows you who is performing best at any given time . It’s the same five names, with Paypal and Chase leading the pack. However, I see Wells Fargo is better at attracting repeat visitors, while Capital One and Bank of America perform better at drawing in unique visitors.

In answer to my question, what is the market share of my major competitors, I can quickly use Similarweb’s quantitative data to get my answer.

This traffic share visual can be downloaded from the platform. It plots the ten industry leader’s market share and allocates the remaining share to the rest of the market.

I can also download a market quadrant analysis, which takes two key data points, traffic share and unique visitors, and plots the industry leaders. All supporting raw data can be downloaded in .xls format or connected to other business intelligence platforms via the API.

Quantitative research questions for consumer behavior studies

These studies measure and analyze consumer behavior, preferences, and habits . Any type of audience analysis helps companies better understand customer intent, and adjust offerings, messaging, campaigns, SEO, and ultimately offer more relevant products and services within a market.

• What is the average amount consumers spend on a certain product each month?
• What percentage of consumers are likely to purchase a product based on its price?
• How do the demographics of the target audience affect their purchasing behavior?
• What type of incentive is most likely to increase the likelihood of purchase?
• How does the store’s location impact product sales and turnover?
• What are the key drivers of product loyalty among consumers?
• What are the most commonly cited reasons for not buying a product?
• How does the availability of product information impact purchasing decisions?
• What is the average time consumers spend researching a product before buying it?
• How often do consumers use social media when making a purchase decision?

While applying a qualitative approach to such studies is also possible, it’s a great example of quantitative market research in action. For larger corporations, studies that involve a large, relevant sample size of a target market deliver vital consumer insights at scale .

Read More: 83 Qualitative Research Questions & Examples

Quantitative research question and answer: content strategy and analysis

‍♀️ Question: What type of content performed best in the market this past month?

Insight sought: Establish high-performing campaigns and promotions in a market.

Challenges with traditional quantitative research methods: Whether you consider putting together a panel yourself, or paying a company to do it for you, quantitative research at scale is costly and time-consuming. What’s more, you have to ensure that sampling is done right and represents your target audience.

A new approach: Data analysis is the foundation of our entire business. For over 10 years, Similarweb has developed a unique , multi-dimensional approach to understanding the digital world. To see the specific campaigns that resonate most with a target audience, use Similarweb’s Popular Pages feature. Key metrics show which campaigns achieve the best results for any site (including rival firms), campaign take-up, and periodic changes in performance and interest.

✅ How it’s done: I’ve chosen Capital One and Wells Fargo to review. Using the Popular Pages campaign filter, I can view all pages identified by a URL parameter UTM. For clarity, I’ve highlighted specific campaigns showing high-growth and increasing popularity. I can view any site’s trending, new, or best-performing pages using a different filter.

In this example, I have highlighted three campaigns showing healthy growth, covering teen checking accounts, performance savings accounts, and add-cash-in-store. Next, I will perform the same check for another key competitor in my market.

Here, I can see financial health tools campaigns with over 300% month-over-month growth and smarter credit and FICO campaigns showing strong performance. This tells me that campaigns focussing on education and tools are growing in popularity within this market. 

Examples of quantitative research questions for brand tracking

These studies are designed to measure customers’ awareness, perceptions, behaviors, and attitudes toward a brand over time. Different applications include measuring brand awareness , brand equity, customer satisfaction, and purchase or usage intent.

These types of research surveys ask questions about brand knowledge, brand attributes, brand perceptions, and brand loyalty . The data collected can then be used to understand the current state of a brand’s performance, identify improvements, and track the success of marketing initiatives.

• To what extent is Brand Z associated with innovation?
• How do consumers rate the quality of Brand Z’s products and services?
• How has the awareness of Brand Z changed over the past 6 months?
• How does Brand Z compare to its competitors in terms of customer satisfaction?
• To what extent do consumers trust Brand Z?
• How likely are consumers to recommend Brand Z?
• What factors influence consumers’ purchase decisions when considering Brand Z?
• What is the average customer satisfaction score for equity?
• How does equity’s customer service compare to its competitors?
• How do customer perceptions of equity’s brand values compare to its competitors?

Quantitative research question example and answer: brand tracking

‍♀️ Question: How has the awareness of Brand Z changed over the past 6 months?

Insight sought: How has brand awareness changed for my business and competitors over time.

⏰ Time to insight: 2 minutes

✅ How it’s done: Using Similarweb’s search overview, I can quickly identify which brands in my chosen market have the highest brand awareness over any time period or location. I can view these stats as a custom market or examine brands individually.

Here, I’ve chosen a custom view that shows me five companies side-by-side. In the top right-hand corner, under branded traffic, you get a quick snapshot of the share of website visits that were generated by branded keywords. A branded keyword is when a consumer types the brand name + a search term.

Below that, you will see the search traffic and engagement section. Here, I’ve filtered the results to show me branded traffic as a percentage of total traffic. Similarweb shows me how branded search volumes grow or decline monthly. Helping me answer the question of how brand awareness has changed over time.

Quantitative research questions for consumer ad testing

Another example of using quantitative research to impact change and improve results is ad testing. It measures the effectiveness of different advertising campaigns. It’s often known as A/B testing , where different visuals, content, calls-to-action, and design elements are experimented with to see which works best. It can show the impact of different ads on engagement and conversions.

A range of quantitative market research questions can be asked and analyzed to determine the optimal approach.

• How does changing the ad’s headline affect the number of people who click on the ad?
• How does varying the ad’s design affect its click-through rate?
• How does altering the ad’s call-to-action affect the number of conversions?
• How does adjusting the ad’s color scheme influence the number of people who view the ad?
• How does manipulating the ad’s text length affect the average amount of time a user spends on the landing page?
• How does changing the ad’s placement on the page affect the amount of money spent on the ad?
• How does varying the ad’s targeting parameters affect the number of impressions?
• How does altering the ad’s call-to-action language impact the click-through rate?

Quantitative question examples for social media monitoring

Quantitative market research can be applied to measure and analyze the impact of social media on a brand’s awareness, engagement, and reputation . By tracking key metrics such as the number of followers, impressions, and shares, brands can:

• Assess the success of their social media campaigns
• Understand what content resonates with customers
• Spot potential areas for improvement
• How often are people talking about our brand on social media channels?
• How many times has our brand been mentioned in the past month?
• What are the most popular topics related to our brand on social media?
• What is the sentiment associated with our brand across social media channels?
• How do our competitors compare in terms of social media presence?
• What is the average response time for customer inquiries on social media?
• What percentage of followers are actively engaging with our brand?
• What are the most popular hashtags associated with our brand?
• What types of content generate the most engagement on social media?
• How does our brand compare to our competitors in terms of reach and engagement on social media?

Example of quantitative research question and answer: social media monitoring

‍♀️ Question: How does our brand compare to our competitors in terms of reach and engagement on social media?

Insight sought: The social channels that most effectively drive traffic and engagement in my market

✅ How it’s done: Similarweb Digital Research Intelligence shows you a marketing channels overview at both an industry and market level. With it, you can view the most effective social media channels in any industry and drill down to compare social performance across a custom group of competitors or an individual company.

Here, I’ve taken the five closest rivals in my market and clicked to expand social media channel data. Wells Fargo and Bank of America have generated the highest traffic volume from social media, with over 6.6 million referrals this year. Next, I can see the exact percentage of traffic generated by each channel and its relative share of traffic for each competitor. This shows me the most effective channels are YouTube, Facebook, LinkedIn, and Reddit – in that order.

In 30-seconds, I’ve discovered the following:

• YouTube is the most popular social network in my market.
• Facebook and LinkedIn are the second and third most popular channels.
• Wells Fargo is my primary target for a more in-depth review, with the highest performance on the top two channels.
• Bank of America is outperforming all key players significantly on LinkedIn.
• American Express has found a high referral opportunity on Reddit that others have been unable to match.

Power-up Your Market Research with Similarweb Today

Examples of quantitative research questions for online polls.

This is one of the oldest known uses of quantitative market research. It dates back to the 19th century when they were first used in America to try and predict the outcome of the presidential elections.

Polls are just short versions of surveys but provide a point-in-time perspective across a large group of people. You can add a poll to your website as a widget, to an email, or if you’ve got a budget to spend, you might use a company like YouGov to add questions to one of their online polls and distribute it to an audience en-masse.

• What is your annual income?
• In what age group do you fall?
• On average, how much do you spend on our products per month?
• How likely are you to recommend our products to others?
• How satisfied are you with our customer service?
• How likely are you to purchase our products in the future?
• On a scale of 1 to 10, how important is price when it comes to buying our products?
• How likely are you to use our products in the next six months?
• What other brands of products do you purchase?
• How would you rate our products compared to our competitors?

Quantitative research questions for eye tracking studies

These research studies measure how people look and respond to different websites or ad elements. It’s traditionally an example of quantitative research used by enterprise firms but is becoming more common in the SMB space due to easier access to such technologies.

• How much time do participants spend looking at each visual element of the product or ad?
• How does the order of presentation affect the impact of time spent looking at each visual element?
• How does the size of the visual elements affect the amount of time spent looking at them?
• What is the average time participants spend looking at the product or ad as a whole?
• What is the average number of fixations participants make when looking at the product or ad?
• Are there any visual elements that participants consistently ignore?
• How does the product’s design or advertising affect the average number of fixations?
• How do different types of participants (age, gender, etc.) interact with the product or ad differently?
• Is there a correlation between the amount of time spent looking at the product or ad and the participants’ purchase decision?
• How does the user’s experience with similar products or ads affect the amount of time spent looking at the current product or ad?

Quantitative question examples for customer segmentation

Segmentation is becoming more important as organizations large and small seek to offer more personalized experiences. Effective segmentation helps businesses understand their customer’s needs–which can result in more targeted marketing, increased conversions, higher levels of loyalty, and better brand awareness.

If you’re just starting to segment your market, and want to know the best quantitative research questions to ask to help you do this, here are 20 to choose from.

Examples of quantitative research questions to segment customers

• What is your age range?
• What is your annual household income?
• What is your preferred online shopping method?
• What is your occupation?
• What types of products do you typically purchase?
• Are you a frequent shopper?
• How often do you purchase products online?
• What is your typical budget for online purchases?
• What is your primary motivation for purchasing products online?
• What factors influence your decision to purchase a product online?
• What device do you use most often when shopping online?
• What type of product categories are you most interested in?
• Do you prefer to shop online for convenience or for a better price?
• What type of discounts or promotions do you look for when making online purchases?
• How do you prefer to receive notifications about product promotions or discounts?
• What type of payment methods do you prefer when shopping online?
• What methods do you use to compare different products and prices when shopping online?
• What type of customer service do you expect when shopping online?
• What type of product reviews do you consider when making online purchases?
• How do you prefer to interact with a brand when shopping online?

Examples of quantitative research questions for analyzing customer segments

• What is the average age of customers in each segment?
• How do spending habits vary across customer segments?
• What is the average length of time customers spend in each segment?
• How does loyalty vary across customer segments?
• What is the average purchase size in each segment?
• What is the average frequency of purchases in each segment?
• What is the average customer lifetime value in each segment?
• How does customer satisfaction vary across customer segments?
• What is the average response rate to campaigns in each segment?
• How does customer engagement vary across customer segments?

These questions are ideal to ask once you’ve already defined your segments. We’ve written a useful post that covers the ins and outs of what market segmentation is and how to do it.

Additional applications of quantitative research questions

I’ve covered ten use cases for quantitative questions in detail. Still, there are other instances where you can put quantitative research to good use.

Product usage studies: Measure how customers use a product or service.

Preference testing: Testing of customer preferences for different products or services.

Sales analysis: Analysis of sales data to identify trends and patterns.

Distribution analysis: Analyzing distribution channels to determine the most efficient and effective way to reach customers.

Focus groups: Groups of consumers brought together to discuss and provide feedback on a particular product, service, or marketing campaign.

Consumer interviews: Conducted with customers to understand their behavior and preferences better.

Mystery shopping: Mystery shoppers are sent to stores to measure customer service levels and product availability.

Conjoint analysis: Analysis of how consumers value different attributes of a product or service.

Regression analysis: Statistical analysis used to identify relationships between different variables.

A/B testing: Testing two or more different versions of a product or service to determine which one performs better.

Brand equity studies: Measure, compare and analyze brand recognition, loyalty, and consumer perception.

Exit surveys: Collect numerical data to analyze employee experience and reasons for leaving, providing insight into how to improve the work environment and retain employees.

Price sensitivity testing: Measuring responses to different pricing models to find the optimal pricing model, and identify areas if and where discounts or incentives might be beneficial.

Quantitative market research survey examples

A recent GreenBook study shows that 89% of people in the market research industry use online surveys frequently–and for good reason. They’re quick and easy to set up, the cost is minimal, and they’re highly scalable too.

Questions are always formatted to provide close-ended answers that can be quantified. If you wish to collect free-text responses, this ventures into the realm of qualitative research . Here are a few examples.

Brand Loyalty Surveys: Companies use online surveys to measure customers’ loyalty to their brand. They include questions about how long an individual has been a customer, their overall satisfaction with the service or product, and the likelihood of them recommending the brand to others.

Customer Satisfaction Surveys: These surveys may include questions about the customer’s experience, their overall satisfaction, and the likelihood they will recommend a product or service to others.

Pricing Studies: This type of research reveals how customers value their products or services. These surveys may include questions about the customer’s willingness to pay for the product, the customer’s perception of the price and value, and their comparison of the price to other similar items.

Product/Service Usage Studies: These surveys measure how customers use their products or services. They can include questions about how often customers use a product, their preferred features, and overall satisfaction.

Here’s an example of a typical survey we’ve used when testing out potential features with groups of clients. After they’ve had the chance to use the feature for a period, we send a short survey, then use the feedback to determine the viability of the feature for future release.

Employee Experience Surveys: Another great example of quantitative data in action, and one we use at Similarweb to measure employee satisfaction. Many online platforms are available to help you conduct them; here, we use Culture AMP . The ability to manipulate the data, spot patterns or trends, then identify the core successes and development areas are astounding.

How to answer quantitative research questions with Similarweb

For the vast majority of applications I’ve covered in this post, there’s a more modern, quicker, and more efficient way to obtain similar insights online. Gone are the days when companies need to use expensive outdated data or pay hefty sums of money to market research firms to conduct broad studies to get the answers they need.

By this point, I hope you’ve seen how quick and easy it is to use Similarweb to do market research the modern way. But I’ve only scratched the surface of its capabilities.

Take two to watch this introductory video and see what else you can uncover.

Added bonus: Similarweb API

If you need to crunch large volumes of data and already use tools like Tableau or PowerBI, you can seamlessly connect Similarweb via the API and pipe in the data. So for faster analysis of big data, you can leverage Similarweb data to use alongside the visualization tools you already know and love.

Similarweb’s suite of market intelligence solutions offers unbiased, accurate, honest insights you can trust. With a world of data at your fingertips, use Similarweb Research Intelligence to uncover facts that help inform your research and strengthen your position.

Take a look at:

• Our Market Research suite
• Our Benchmarking tools
• Our Audience Insights tool
• Our Company Research module
• Our Consumer Journey Tracker
• Our Competitive Analysis Tool

Wrapping up

Today’s markets change at lightning speed. To keep up and succeed, companies need access to insights and intel they can depend on to be timely and on-point. While quantitative market research questions can and should always be asked, it’s important to leverage technology to increase your speed to insight, and thus improve reaction times and response to market shifts.

What is quantitative market research?

Quantitative market research is a form of research that uses numerical data to gain insights into the behavior and preferences of customers. It is used to measure and track the performance of products, services, and campaigns.

How does quantitative market research help businesses?

Quantitative market research can help businesses identify customer trends, measure customer satisfaction, and develop effective marketing strategies. It can also provide valuable insights into customer behavior, preferences, and attitudes.

What types of questions should be included in a quantitative market research survey?

Questions in a quantitative market research survey should be focused, clear, and specific. Questions should be structured to collect quantitative data, such as numbers, percentages, or frequency of responses.

What methods can be used to collect quantitative market research data?

Common methods used to collect quantitative market research data include surveys, interviews, focus groups, polls, and online questionnaires.

What are the advantages and disadvantages of using quantitative market research?

The advantages of using quantitative market research include the ability to collect data quickly, the ability to analyze data in a structured way, and the ability to identify trends. Disadvantages include the potential for bias, the cost of collecting data, and the difficulty in interpreting results.

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What Is Market Research?

• How It Works
• Primary vs. Secondary
• How to Conduct Research

The Bottom Line

• Marketing Essentials

How to Do Market Research, Types, and Example

Joules Garcia / Investopedia

Market research examines consumer behavior and trends in the economy to help a business develop and fine-tune its business idea and strategy. It helps a business understand its target market by gathering and analyzing data.

Market research is the process of evaluating the viability of a new service or product through research conducted directly with potential customers. It allows a company to define its target market and get opinions and other feedback from consumers about their interest in a product or service.

Research may be conducted in-house or by a third party that specializes in market research. It can be done through surveys and focus groups, among other ways. Test subjects are usually compensated with product samples or a small stipend for their time.

Key Takeaways

• Companies conduct market research before introducing new products to determine their appeal to potential customers.
• Tools include focus groups, telephone interviews, and questionnaires.
• The results of market research inform the final design of the product and determine how it will be positioned in the marketplace.
• Market research usually combines primary information, gathered directly from consumers, and secondary information, which is data available from external sources.

Market Research

How market research works.

Market research is used to determine the viability of a new product or service. The results may be used to revise the product design and fine-tune the strategy for introducing it to the public. This can include information gathered for the purpose of determining market segmentation . It also informs product differentiation , which is used to tailor advertising.

A business engages in various tasks to complete the market research process. It gathers information based on the market sector being targeted by the product. This information is then analyzed and relevant data points are interpreted to draw conclusions about how the product may be optimally designed and marketed to the market segment for which it is intended.

It is a critical component in the research and development (R&D) phase of a new product or service introduction. Market research can be conducted in many different ways, including surveys, product testing, interviews, and focus groups.

Market research is a critical tool that companies use to understand what consumers want, develop products that those consumers will use, and maintain a competitive advantage over other companies in their industry.

Primary Market Research vs. Secondary Market Research

Market research usually consists of a combination of:

• Primary research, gathered by the company or by an outside company that it hires
• Secondary research, which draws on external sources of data

Primary Market Research

Primary research generally falls into two categories: exploratory and specific research.

• Exploratory research is less structured and functions via open-ended questions. The questions may be posed in a focus group setting, telephone interviews, or questionnaires. It results in questions or issues that the company needs to address about a product that it has under development.
• Specific research delves more deeply into the problems or issues identified in exploratory research.

Secondary Market Research

All market research is informed by the findings of other researchers about the needs and wants of consumers. Today, much of this research can be found online.

Secondary research can include population information from government census data , trade association research reports , polling results, and research from other businesses operating in the same market sector.

History of Market Research

Formal market research began in Germany during the 1920s. In the United States, it soon took off with the advent of the Golden Age of Radio.

Companies that created advertisements for this new entertainment medium began to look at the demographics of the audiences who listened to each of the radio plays, music programs, and comedy skits that were presented.

They had once tried to reach the widest possible audience by placing their messages on billboards or in the most popular magazines. With radio programming, they had the chance to target rural or urban consumers, teenagers or families, and judge the results by the sales numbers that followed.

Types of Market Research

Face-to-face interviews.

From their earliest days, market research companies would interview people on the street about the newspapers and magazines that they read regularly and ask whether they recalled any of the ads or brands that were published in them. Data collected from these interviews were compared to the circulation of the publication to determine the effectiveness of those ads.

Market research and surveys were adapted from these early techniques.

To get a strong understanding of your market, it’s essential to understand demand, market size, economic indicators, location, market saturation, and pricing.

Focus Groups

A focus group is a small number of representative consumers chosen to try a product or watch an advertisement.

Afterward, the group is asked for feedback on their perceptions of the product, the company’s brand, or competing products. The company then takes that information and makes decisions about what to do with the product or service, whether that's releasing it, making changes, or abandoning it altogether.

Phone Research

The man-on-the-street interview technique soon gave way to the telephone interview. A telephone interviewer could collect information in a more efficient and cost-effective fashion.

Telephone research was a preferred tactic of market researchers for many years. It has become much more difficult in recent years as landline phone service dwindles and is replaced by less accessible mobile phones.

Survey Research

As an alternative to focus groups, surveys represent a cost-effective way to determine consumer attitudes without having to interview anyone in person. Consumers are sent surveys in the mail, usually with a coupon or voucher to incentivize participation. These surveys help determine how consumers feel about the product, brand, and price point.

Online Market Research

With people spending more time online, market research activities have shifted online as well. Data collection still uses a survey-style form. But instead of companies actively seeking participants by finding them on the street or cold calling them on the phone, people can choose to sign up, take surveys, and offer opinions when they have time.

This makes the process far less intrusive and less rushed, since people can participate on their own time and of their own volition.

How to Conduct Market Research

The first step to effective market research is to determine the goals of the study. Each study should seek to answer a clear, well-defined problem. For example, a company might seek to identify consumer preferences, brand recognition, or the comparative effectiveness of different types of ad campaigns.

After that, the next step is to determine who will be included in the research. Market research is an expensive process, and a company cannot waste resources collecting unnecessary data. The firm should decide in advance which types of consumers will be included in the research, and how the data will be collected. They should also account for the probability of statistical errors or sampling bias .

The next step is to collect the data and analyze the results. If the two previous steps have been completed accurately, this should be straightforward. The researchers will collect the results of their study, keeping track of the ages, gender, and other relevant data of each respondent. This is then analyzed in a marketing report that explains the results of their research.

The last step is for company executives to use their market research to make business decisions. Depending on the results of their research, they may choose to target a different group of consumers, or they may change their price point or some product features.

The results of these changes may eventually be measured in further market research, and the process will begin all over again.

Benefits of Market Research

Market research is essential for developing brand loyalty and customer satisfaction. Since it is unlikely for a product to appeal equally to every consumer, a strong market research program can help identify the key demographics and market segments that are most likely to use a given product.

Market research is also important for developing a company’s advertising efforts. For example, if a company’s market research determines that its consumers are more likely to use Facebook than X (formerly Twitter), it can then target its advertisements to one platform instead of another. Or, if they determine that their target market is value-sensitive rather than price-sensitive, they can work on improving the product rather than reducing their prices.

Market research only works when subjects are honest and open to participating.

Example of Market Research

Many companies use market research to test new products or get information from consumers about what kinds of products or services they need and don’t currently have.

For example, a company that’s considering starting a business might conduct market research to test the viability of its product or service. If the market research confirms consumer interest, the business can proceed confidently with its business plan . If not, the company can use the results of the market research to make adjustments to the product to bring it in line with customer desires.

What Are the Main Types of Market Research?

The main types of market research are primary research and secondary research. Primary research includes focus groups, polls, and surveys. Secondary research includes academic articles, infographics, and white papers.

Qualitative research gives insights into how customers feel and think. Quantitative research uses data and statistics such as website views, social media engagement, and subscriber numbers.

What Is Online Market Research?

Online market research uses the same strategies and techniques as traditional primary and secondary market research, but it is conducted on the Internet. Potential customers may be asked to participate in a survey or give feedback on a product. The responses may help the researchers create a profile of the likely customer for a new product.

What Are Paid Market Research Surveys?

Paid market research involves rewarding individuals who agree to participate in a study. They may be offered a small payment for their time or a discount coupon in return for filling out a questionnaire or participating in a focus group.

What Is a Market Study?

A market study is an analysis of consumer demand for a product or service. It looks at all of the factors that influence demand for a product or service. These include the product’s price, location, competition, and substitutes as well as general economic factors that could influence the new product’s adoption, for better or worse.

Market research is a key component of a company’s research and development (R&D) stage. It helps companies understand in advance the viability of a new product that they have in development and to see how it might perform in the real world.

Britannica Money. “ Market Research .”

U.S. Small Business Administration. “ Market Research and Competitive Analysis .”

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What is data analysis? Examples and how to get started

Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Why is data analysis important?

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

Types of data analysis (with examples)

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Text analysis: What is happening?

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

Data analysis process: How to get started

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Automate your data collection, frequently asked questions.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea Stevens

Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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15 Data Analysis Examples for Beginners in 2024

Data analysis is a multifaceted process that involves inspecting, cleaning, transforming, and modeling data to uncover valuable insights. It encompasses a wide array of techniques and methodologies, enabling organizations to interpret complex data structures and extract meaningful patterns.

Data Analysis Examples foe Beginners

In this article, we will explore about 15 Data Analysis Examples for Beginners in 2024.

Real-world Examples of Data Analysis for Beginners

The Applications of Data Analysis are vast and far-reaching, permeating various sectors and industries. Let’s explore 15 illuminating examples that highlight the versatility and impact of this powerful discipline.

Sales Trend Analysis

Businesses often leverage data analysis to assess sales data over different periods, identifying trends and patterns that can inform their strategies. For instance, a retail company might monitor quarterly sales data to pinpoint peak buying times, popular products, and emerging customer preferences. By doing so, they can adjust inventory management, marketing efforts, and sales strategies to align with consumer demands and seasonal fluctuations, ultimately enhancing profitability and operational efficiency.

Customer Segmentation

In this data analysis application, companies categorize their customer base into distinct groups based on specific criteria, such as purchasing behavior, demographics, or preferences. An online shopping platform, for example, might segment its customers into categories like frequent buyers, seasonal shoppers, or budget-conscious consumers. This analysis enables businesses to tailor marketing campaigns, product offerings, and customer experiences to appeal to each group’s unique needs, fostering improved engagement and driving business growth.

Social Media Sentiment Analysis

In the digital age, companies harness the power of data analysis to gauge public sentiment towards their products or brands by analyzing social media interactions. By examining comments, likes, shares, and other engagement metrics, they can assess overall customer satisfaction and identify areas for improvement. This type of analysis significantly impacts online reputation management, influencing marketing and public relations strategies.

Forecasting and Predictive Analysis

Data analysis plays a crucial role in predicting future trends or outcomes. An airline company, for instance, might analyze past data on seat bookings, flight timings, and passenger preferences to forecast future travel trends. This predictive analysis allows the airline to optimize flight schedules, plan for peak travel periods, and set competitive ticket prices, ultimately contributing to improved customer satisfaction and increased revenues.

Operational Efficiency Analysis

This form of data analysis focuses on optimizing internal processes within an organization. A manufacturing company might analyze data regarding machine performance, maintenance schedules, and production output to identify bottlenecks or inefficiencies. By addressing these issues, the company can streamline operations, improve productivity, and reduce costs, underscoring the importance of data analysis in achieving operational excellence.

Risk Assessment Analysis

Data analysis helps businesses identify potential risks that could adversely impact their operations or profits. An insurance company, for instance, might analyze customer data and historical claim information to estimate future claim risks. This analysis supports accurate premium setting and proactive risk management, mitigating potential financial hazards and highlighting the role of data analysis in sound risk management practices.

Recruitment and Talent Management Analysis

In this example, human resources departments scrutinize data concerning employee performance, retention rates, and skill sets. A technology firm might conduct an analysis to identify the skills and experience most prevalent among its top-performing employees. This analysis enables companies to attract and retain high-caliber talent, tailor training programs, and improve overall workforce effectiveness.

Supply Chain Optimization Analysis

This form of data analysis aims to enhance the efficiency of a business’s supply chain. A grocery store, for example, might examine sales data, warehouse inventory levels, and supplier delivery times to ensure the right products are in stock at the right time. This analysis can reduce warehousing costs, minimize stockouts or overstocks, and increase customer satisfaction, underscoring the role of data analysis in streamlining supply chains.

Web Analytics

In the digital era, businesses invest in data analysis to optimize their online presence and functionality. An e-commerce business might analyze website traffic data, bounce rates, conversion rates, and user engagement metrics. This analysis can guide website redesign, enhance user experience, and boost conversion rates, reflecting the importance of data analysis in digital marketing and web optimization.

Medical and Healthcare Analysis

Data analysis plays a crucial role in the healthcare sector. A hospital might analyze patient data, disease patterns, treatment outcomes, and more. This analysis can support evidence-based treatment plans, inform research on healthcare trends, and contribute to policy development. It can also enhance patient care by identifying efficient treatment paths and reducing hospitalization time, underscoring the significance of data analysis in the medical field.

Fraud Detection Analysis

In the financial and banking sector, data analysis is paramount in identifying and mitigating fraudulent activities. Banks might analyze transaction data, account activity, and user behavior trends to detect abnormal patterns indicative of fraud. By alerting the concerned authorities about suspicious activity, such analysis can prevent financial losses and protect customer assets, illustrating the importance of data analysis in ensuring financial security.

Energy Consumption Analysis

Utilities and energy companies often utilize data analysis to optimize their energy distribution and consumption. By evaluating data on customer usage patterns, peak demand times, and grid performance, companies can enhance energy efficiency, optimize grid operations, and develop more customer-centric services. This example showcases how data analysis can contribute to more sustainable and efficient resource utilization.

Market Research Analysis

Many businesses rely on data analysis to gauge market dynamics and consumer behaviors. A cosmetic brand, for instance, might analyze sales data, consumer feedback, and competitor information. Such analysis can provide useful insights into consumer preferences, popular trends, and competitive strategies, facilitating the development of products that align with market demands and showcasing how data analysis can drive business innovation.

Quality Control Analysis

Manufacturing industries often employ data analysis in their quality control processes. They may monitor operational data, machine performance, and product fault reports. By identifying causes of defects or inefficiencies, these industries can improve product quality, enhance manufacturing processes, and reduce waste, demonstrating the decisive role of data analysis in maintaining high-quality standards.

Economic and Policy Analysis

Government agencies and think tanks utilize data analysis to inform policy decisions and societal strategies. They might analyze data relating to employment rates, GDP, public health, or educational attainment. These insights can inform policy development, assess the impact of existing policies, and guide strategies for societal improvement, revealing that data analysis is a key tool in managing social and economic progression.

Analysis Techniques and Insights

The examples above highlight the diverse applications of data analysis, but it’s essential to delve deeper into the techniques and methodologies that enable these insights. From exploratory analysis to predictive modeling, each approach serves a unique purpose and provides distinct perspectives.

Exploratory Analysis

• Exploratory analysis is often the starting point in a data analysis process, allowing researchers to understand the main characteristics of a dataset. This technique involves visual methods such as scatter plots, histograms, and box plots, enabling analysts to summarize the data’s primary aspects, check for missing values, and test assumptions.

Regression Analysis

• Regression analysis is a statistical method used to understand the relationship between a dependent variable and one or more independent variables. It is commonly employed for forecasting, time series modeling, and identifying causal effect relationships between variables. This technique is widely used in areas such as machine learning and business intelligence.

Factor Analysis

• Factor analysis is a technique used to reduce a large number of variables into fewer factors, capturing the maximum possible information from the original variables. This approach is often utilized in market research, customer segmentation, and image recognition, enabling analysts to identify underlying patterns and relationships within complex datasets.

Monte Carlo Simulation

• Monte Carlo simulation is a technique that uses probability distributions and random sampling to estimate numerical results. It is frequently employed in risk analysis and decision-making scenarios where there is significant uncertainty, providing a powerful tool for exploring potential outcomes and informing strategic decisions.

Key Lessons from Implementing Data Analysis in Various Industries

As we delve into the various examples and techniques of data analysis, several valuable lessons emerge:

• Embrace a Data-Driven Mindset: Successful organizations recognize the value of data-driven decision-making and actively incorporate data analysis into their strategic planning and operations.
• Foster Cross-Functional Collaboration: Effective data analysis often requires collaboration between different departments and stakeholders, enabling a holistic understanding of the problem at hand and facilitating comprehensive solutions.
• Invest in Talent and Technology: Developing a skilled workforce proficient in data analysis techniques and leveraging cutting-edge tools and technologies are crucial for extracting meaningful insights from complex datasets.
• Prioritize Data Quality: The accuracy and reliability of data analysis outcomes are heavily dependent on the quality of the input data. Implementing robust data governance practices and ensuring data integrity is essential.
• Continuously Adapt and Evolve: The field of data analysis is constantly evolving, with new techniques and methodologies emerging regularly. Embracing a culture of continuous learning and adaptation is vital to staying ahead of the curve.

Best Practices from Real-World Data Analysis

To maximize the benefits of data analysis and ensure its successful implementation, it is essential to adopt best practices. These include:

• Clearly Define Objectives: Before embarking on a data analysis project, clearly define the objectives, questions, and metrics to be addressed, ensuring alignment with organizational goals.
• Establish Data Governance Frameworks: Implement robust data governance frameworks to ensure data quality, security, and compliance with relevant regulations and policies.
• Leverage Automation: Explore opportunities to automate repetitive tasks and processes within the data analysis workflow, improving efficiency and reducing the risk of human error.
• Encourage Collaboration and Knowledge Sharing: Foster an environment that promotes collaboration, knowledge sharing, and cross-functional communication, enabling a holistic approach to data analysis.
• Continuously Monitor and Iterate: Regularly monitor and evaluate the effectiveness of data analysis initiatives, iterating and refining processes as needed to ensure ongoing relevance and alignment with evolving business needs.

In the digital era, data analysis has become a vital tool for organizations, enabling them to unleash the full potential of their data. The examples presented demonstrate the wide-ranging impact of data analysis, from operational optimization to driving innovation. Embracing a data-driven approach and staying current with emerging technologies will be key to unlocking future growth and success.

Data Analysis Examples for Beginners in 2024 – FAQs

What are some common examples of data analysis.

Common examples include sales data analysis for identifying trends, customer behavior analysis for marketing, and financial data analysis for predicting market trends.

Can you provide an example of data analysis in business?

Using sales data to identify top-selling products and make informed decisions about inventory and marketing strategies is a common example of data analysis in business.

How is data analysis used in healthcare?

Data analysis in healthcare involves studying patient records to identify disease patterns and treatment outcomes, aiding in improved patient care and resource allocation.

What are some examples of data visualization in data analysis?

Examples of data visualization include bar charts and line graphs for representing trends, and heat maps for spatial data analysis and identifying patterns.

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Sales Data: How to Perform a Sales Data Analysis

Discover how to perform a sales data analysis to boost your business. Learn to collect, analyse, and apply insights to drive sales and improve strategies.

Salesforce Staff

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Sales data is a powerful tool that can help you understand your business, make informed decisions, and improve your sales performance. Regularly analysing sales data is crucial for gaining real-time insights into the sales cycle, driving improvement, and setting the team up for success. By doing so, you can track your progress, identify trends, and forecast future sales growth. You can also segment your customers, evaluate your marketing campaigns, and make data-driven decisions about your business. In this blog post, we will show you how to collect, analyse, and use sales data to improve your business. We will also discuss some of the key sales data metrics that you should track, and how to present your sales data in a way that is easy to understand.

What is sales data?

Sales data is a valuable asset for businesses of all sizes. It provides insights into customer behaviour, sales performance, and market trends. By analysing sales data, businesses can make informed decisions about product development, marketing, and sales strategies.

Sales data can be collected from various sources, including point-of-sale systems, customer relationship management (CRM) software, and e-commerce platforms. Once collected, the data can be analysed using various tools and techniques, such as business intelligence (BI) software and data visualisation tools.

Sales data can be used to track key performance indicators (KPIs), such as revenue, profit, customer acquisition cost, and customer lifetime value. By tracking these metrics, businesses can measure their progress and identify areas for improvement. Additionally, sales data can be used to identify trends and patterns, such as seasonal fluctuations in demand or changes in customer preferences. This information can be used to make informed decisions about product development, marketing, and sales strategies.

For example, a business might use sales data to identify which products are most popular with customers, or which marketing campaigns are most effective. This information can then be used to make decisions about which products to invest in, or which marketing campaigns to continue.

In summary, sales data is a valuable tool that can help businesses improve their performance. By collecting and analysing sales data, businesses can gain insights into customer behaviour, sales performance metrics, and market trends. This information can be used to make informed decisions about product development, marketing, and sales strategies.

Key sales data metrics

Businesses need to track a variety of sales metrics to measure their performance and make informed decisions. Some of the most important sales data metrics include:

Total Revenue:  This is the total amount of money that a business brings in from sales over a given period of time. It is calculated by multiplying the number of units sold by the price per unit. Total revenue is a key metric for measuring the overall success of a business and can be used to track growth over time.

Profit Margin: This is the percentage of revenue that a business keeps after subtracting all costs associated with producing and selling its products or services. It is calculated by dividing the gross profit (total revenue minus the cost of goods sold) by the total revenue. Profit margin is a key metric for measuring the profitability of a business and can be used to identify areas where costs can be reduced.

Customer Acquisition Cost: This is the average amount of money that a business spends to acquire a new customer. It is calculated by dividing the total marketing and sales expenses by the number of new customers acquired over a given period of time. Customer acquisition cost is a key metric for measuring the efficiency of a business’s marketing and sales efforts and can be used to identify ways to reduce costs.

Customer Lifetime Value:  This is the total amount of money that a business can expect to earn from a customer over their lifetime. It is calculated by multiplying the average customer lifespan by the average revenue per customer. Customer lifetime value is a key metric for measuring the profitability of a business’s customer relationships and can be used to identify ways to increase customer retention and loyalty.

Average Deal Size:  This metric is crucial for calculating Customer Lifetime Value (CLV) and monitoring upsell performance. It reflects the average revenue generated from each deal, helping businesses to determine pipeline velocity and provide targeted training to sales reps to maximise deal values.

Average Order Value: This is the average amount of money that a customer spends on a single purchase. It is calculated by dividing the total revenue by the number of orders over a given period of time. Average order value is a key metric for measuring the effectiveness of a business’s pricing strategy and can be used to identify ways to increase sales.

These are just a few of the key sales data metrics that businesses should track. By understanding these metrics, businesses can make informed decisions about their product development, marketing, and sales strategies to improve their sales teams overall performance.

How to collect sales data  

There are several methods for collecting sales data, each with its own advantages and disadvantages. Some common methods include:

Tracking website analytics:  Website analytics tools, such as Google Analytics, can provide valuable insights into how customers interact with your website. This data can include information such as the number of visitors to your site, the pages they visit, and the amount of time they spend on each page. By analysing this data, you can gain insights into customer behaviour and identify areas where you can improve your website to increase sales.

Sending surveys to customers:  Customer surveys can provide valuable feedback about your products, services, and customer experience. By sending surveys to your customers, you can gather information about their satisfaction levels, identify areas for improvement, and collect suggestions for new products or services. Surveys can be conducted online, via email, or over the phone.

Using a CRM system: A customer relationship management (CRM) system can help you track customer interactions and manage your sales pipeline. CRM systems can store customer contact information, track sales activities, and provide insights into customer behaviour. By using a CRM system, you can improve your sales efficiency and effectiveness.

Monitoring social media mentions and online reviews: Social media and online reviews can provide valuable insights into customer sentiment and brand reputation. By monitoring social media mentions and online reviews, you can identify areas where you can improve your products or services and address customer concerns. You can also use social media and online reviews to generate leads and build relationships with potential customers.

By collecting and analysing sales data, you can gain valuable insights into your business and make informed decisions to improve your sales performance.

The importance of sales data analysis

Sales data analysis is important because it can help businesses make more informed decisions, understand customer behaviour, identify their most profitable products and services, track their progress, and stay ahead of the competition.

Informed Decisions

With accurate and timely sales data, businesses can make more informed decisions about their product development, marketing, and sales strategies. For instance, by analysing historical sales data, businesses can identify seasonal trends, customer preferences, and market demands. This information can then be used to develop new products or services, target specific customer segments, and optimise marketing campaigns. Additionally, analysing sales per region helps in determining where products or services are selling the best, enhancing sales and marketing efforts through intelligent performance insights and actionable suggestions for improving these efforts.

Understanding Customer Behaviour

Sales data analysis provides valuable insights into customer behaviour, including their buying patterns, preferences, and pain points. By using sales analytics and understanding customer behaviour, businesses can develop targeted marketing campaigns, improve customer service, and create products and services that better meet customer needs.

Identifying Profitable Products and Services

Sales data analysis helps businesses identify their most profitable products and services. This information can then be used to allocate resources more effectively, focus on high-potential opportunities, and discontinue underperforming products or services.

Tracking Progress

Sales data analysis allows businesses to track their progress over time and measure the effectiveness of their sales and marketing strategies. By using predictive sales analysis and comparing current sales data to historical data, businesses can identify areas of improvement and make necessary adjustments to their strategies.

Staying Ahead of the Competition

In today’s competitive business environment, it is crucial for businesses to stay ahead of the competition. Sales data analysis provides businesses with the insights they need to make informed decisions, identify new opportunities, and develop strategies that give them a competitive edge.

You’ve recorded your sales data — now what? Understanding the sales funnel

After collecting your sales data, the next step is to analyse it to gain valuable insights into your business. By identifying trends and patterns through sales analysis, you can make informed decisions about your sales strategy and improve your overall performance.

One way to analyse your sales data is to look for trends over time. This can help you identify seasonal fluctuations, changes in customer behaviour, and the impact of marketing campaigns. For example, you might see a spike in sales during the holiday season or a decrease in sales during the summer months. By understanding these trends, you can adjust your sales strategy accordingly.

Another way to analyse your sales data is to segment your customers. This involves dividing your customers into different groups based on shared characteristics, such as demographics, purchase history, or location. By segmenting your customers, you can target your marketing and sales efforts more effectively and increase your chances of success.

For a sales cycle for instance, if you have a group of customers who frequently purchase high-priced items, you could create a targeted marketing campaign specifically for them. Or, if you have a group of customers who live in a particular region, you could hold a local sales event.

Finally, you can use your sales data to evaluate your marketing campaigns. By using sales targets and tracking the results of your marketing campaigns, you can see what is working and what is not. This information can help you fine-tune your marketing efforts and get the most out of your marketing budget.

For example, if you run a paid advertising campaign, you can track the number of leads generated by the campaign and the conversion rate of those leads. This information can help you determine the effectiveness of your campaign and make adjustments as needed.

By analysing your sales data, you can gain valuable insights into your business and make informed decisions to improve your sales performance. So, what are you waiting for? Start analysing your sales data today!

Perfecting your sales team performance and sales process

Sales data can also be used to perfect your sales process. By analysing your sales data, you can identify bottlenecks and inefficiencies in your sales process and take steps to streamline it. For example, you may find that your sales team is spending too much time on administrative tasks or that they are not following up with leads quickly enough. By identifying these inefficiencies, you can take steps to improve your sales process and increase your sales. Understanding the sales funnel is crucial for evaluating the health of your sales process and the team’s ability to move prospects through the funnel to turn them into customers.

In addition to identifying bottlenecks, you can also use sales data to automate your sales process. By automating tasks such as lead generation, qualification, sales pipeline analysis and nurturing, you can free up your sales team to focus on more important tasks. This can lead to increased sales and improved customer service.

Finally, you can use sales data to train your sales team and develop targeted marketing campaigns. By understanding your sales data, you can identify the needs of your customers and develop marketing campaigns that reach your ideal customers. You can also use sales data to track the performance of your sales team and provide them with feedback to help them improve their sales team performance further.

By following these tips, you can use sales data to improve your sales process and increase your sales. Sales data is a valuable tool that can help you make informed decisions about your business and achieve your sales goals.

How to present your sales data with dashboards

You’ve collected and analysed your sales data, and now it’s time to present your findings in a way that’s easy to understand and actionable. Dashboards are a great way to do this, as they allow you to visualise your data and track your progress over time.

When creating a sales data dashboard, it’s important to focus on creating a data-driven narrative. This means telling a story with your data, and highlighting the key insights that you want your audience to take away. For example, you might want to show how your sales have increased over time, or how your conversion rate has improved.

To do this, you’ll need to use the right charts and visualisations. Bar charts and line graphs are a good way to show trends over time, while pie charts and scatter plots can be used to show relationships between different variables. It’s also important to consider your audience when choosing your charts and visualisations. If your audience is not familiar with data analysis, you’ll need to use simple charts and visualisations that are easy to understand.

Finally, keep your dashboard simple. Don’t try to cram too much information onto one dashboard, as this will only make it difficult to read and understand. Instead, focus on presenting the most important information in a clear and concise way.

By following these tips, you can create sales reports and data dashboards that are informative, actionable, and easy to understand. This will help you make better decisions about your business and improve your sales performance.

Sales data analysis with Salesforce

Salesforce is a powerful customer relationship management (CRM) platform that can be used to analyse your sales data and gain valuable insights into your business. With Salesforce, you can combine data from various sources, such as your CRM, marketing automation platform, and website analytics, to get a complete view of your sales performance. You can then use Salesforce’s analytics cloud to create reports and dashboards that visualise your data and make it easy to understand.

One of the most powerful features of Salesforce for sales data analysis is Einstein Analytics. Einstein Analytics is a predictive analytics tool that uses artificial intelligence to identify trends and patterns in your data. This information can be used to forecast future sales, identify at-risk customers, and develop targeted marketing campaigns.

In addition to its analytics capabilities, Salesforce can also be used to automate repetitive sales tasks, such as your sales reps sending follow-up emails and creating sales orders. This can free up your sales team to focus on more strategic tasks, such as building relationships with customers and closing deals.

Finally, Salesforce can be used to centralise all of your sales data in one place, making it easier to access and to analyse sales further. This can be especially helpful for businesses that have multiple sales channels or locations.

By using Salesforce for sales data analysis, you can gain valuable insights into your business and improve your overall sales performance.

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Not all data are created equal; some are structured, but most of them are unstructured. Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

In this article, we will take a deep dive into both types so that you can get the most out of your data.

Structured data—typically categorized as quantitative data—is highly organized and easily decipherable by  machine learning algorithms .  Developed by IBM® in 1974 , structured query language (SQL) is the programming language used to manage structured data. By using a  relational (SQL) database , business users can quickly input, search and manipulate structured data.

Examples of structured data include dates, names, addresses, credit card numbers, among others. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

• Easily used by machine learning (ML) algorithms:  The specific and organized architecture of structured data eases the manipulation and querying of ML data.
• Easily used by business users:  Structured data do not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
• Accessible by more tools:  Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.
• Limited usage:  Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
• Limited storage options:  Structured data are usually stored in data storage systems with rigid schemas (for example, “ data warehouses ”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.
• OLAP :  Performs high-speed, multidimensional data analysis from unified, centralized data stores.
• SQLite : (link resides outside ibm.com)  Implements a self-contained,  serverless , zero-configuration, transactional relational database engine.
• MySQL :  Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
• PostgreSQL :  Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java,  Python , among others.).
• Customer relationship management (CRM):  CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking:  Hotel and ticket reservation data (for example, dates, prices, destinations, among others.) fits the “rows and columns” format indicative of the pre-defined data model.
• Accounting:  Accounting firms or departments use structured data to process and record financial transactions.

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in  non-relational (NoSQL) databases . Another way to manage unstructured data is to use  data lakes  to preserve it in raw form.

The importance of unstructured data is rapidly increasing.  Recent projections  (link resides outside ibm.com) indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, among others. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

• Native format:  Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
• Fast accumulation rates:  Since there is no need to predefine the data, it can be collected quickly and easily.
• Data lake storage:  Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
• Requires expertise:  Due to its undefined or non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who might not fully understand specialized data topics or how to utilize their data.
• Specialized tools:  Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.
• MongoDB :  Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB :  (link resides outside ibm.com) Delivers single-digit millisecond performance at any scale through built-in security, in-memory caching and backup and restore.
• Hadoop :  Provides distributed processing of large data sets using simple programming models and no formatting requirements.
• Azure :  Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.
• Data mining :  Enables businesses to use unstructured data to identify consumer behavior, product sentiment and purchasing patterns to better accommodate their customer base.
• Predictive data analytics :  Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots :  Perform text analysis to route customer questions to the appropriate answer sources.

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

• Sources:  Structured data is sourced from GPS sensors, online forms, network logs, web server logs,  OLTP systems , among others; whereas unstructured data sources include email messages, word-processing documents, PDF files, and others.
• Forms:  Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, among others.
• Models:  Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (for example, schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (for example, schema-on-read).
• Storage:  Structured data is stored in tabular formats (for example, excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes, which makes it difficult to scale.
• Uses:  Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in  natural language processing  (NLP) and text mining.

Semi-structured data (for example, JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage:  An online article displays a headline, a snippet, a featured image, image alt-text, slug, among others, which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data:  A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data:  A tab-delimited file versus a list of comments from a customer’s Instagram.

Recent developments in  artificial intelligence  (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models that are covered in this article are helping business users to do the following:

• Analyze digital communications for compliance:  Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
• Track high-volume customer conversations in social media:  Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
• Gain new marketing intelligence:  ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

• Understand customer needs at a deeper level to better serve them
• Create more focused and targeted marketing campaigns
• Track current metrics and create new ones
• Create better product opportunities and offerings
• Reduce operational costs

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By using structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

• 1.1 AP Statistics Topics for Project
• 1.2 Statistics Project Topics for High School Students
• 1.3 Statistical Survey Topics
• 1.4 Statistical Experiment Ideas
• 1.5 Easy Stats Project Ideas
• 1.6 Business Ideas for Statistics Project
• 1.7 Socio-Economic Easy Statistics Project Ideas
• 1.8 Experiment Ideas for Statistics and Analysis
• 2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

• Challenging concepts suitable for advanced placement courses.
• Accessible ideas that are engaging and educational for younger students.
• Ideas for conducting surveys and analyzing the results.
• Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

Need help with statistics project? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

AP Statistics Topics for Project

• Analyzing Variance in Climate Data Over Decades.
• The Correlation Between Economic Indicators and Standard of Living.
• Statistical Analysis of Voter Behavior Patterns.
• Probability Models in Sports: Predicting Outcomes.
• The Effectiveness of Different Teaching Methods: A Statistical Study.
• Analysis of Demographic Data in Public Health.
• Time Series Analysis of Stock Market Trends.
• Investigating the Impact of Social Media on Academic Performance.
• Survival Analysis in Clinical Trial Data.
• Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

• The Mathematics of Personal Finance: Budgeting and Spending Habits.
• Analysis of Class Performance: Test Scores and Study Habits.
• A Statistical Comparison of Local Public Transportation Options.
• Survey on Dietary Habits and Physical Health Among Teenagers.
• Analyzing the Popularity of Various Music Genres in School.
• The Impact of Sleep on Academic Performance: A Statistical Approach.
• Statistical Study on the Use of Technology in Education.
• Comparing Athletic Performance Across Different Sports.
• Trends in Social Media Usage Among High School Students.
• The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

• Public Opinion on Environmental Conservation Efforts.
• Consumer Preferences in the Fast Food Industry.
• Attitudes Towards Online Learning vs. Traditional Classroom Learning.
• Survey on Workplace Satisfaction and Productivity.
• Public Health: Attitudes Towards Vaccination.
• Trends in Mobile Phone Usage and Preferences.
• Community Response to Local Government Policies.
• Consumer Behavior in Online vs. Offline Shopping.
• Perceptions of Public Safety and Law Enforcement.
• Social Media Influence on Political Opinions.

Statistical Experiment Ideas

• The Effect of Light on Plant Growth.
• Memory Retention: Visual vs. Auditory Information.
• Caffeine Consumption and Cognitive Performance.
• The Impact of Exercise on Stress Levels.
• Testing the Efficacy of Natural vs. Chemical Fertilizers.
• The Influence of Color on Mood and Perception.
• Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
• The Effectiveness of Different Types of Water Filters.
• Analyzing the Impact of Room Temperature on Concentration.
• Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

• Average Daily Screen Time Among Students.
• Analyzing the Most Common Birth Months.
• Favorite School Subjects Among Peers.
• Average Time Spent on Homework Weekly.
• Frequency of Public Transport Usage.
• Comparison of Pet Ownership in the Community.
• Favorite Types of Movies or TV Shows.
• Daily Water Consumption Habits.
• Common Breakfast Choices and Their Nutritional Value.
• Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

• Analyzing Customer Satisfaction in Retail Stores.
• Market Analysis of a New Product Launch.
• Employee Performance Metrics and Organizational Success.
• Sales Data Analysis for E-commerce Websites.
• Impact of Advertising on Consumer Buying Behavior.
• Analysis of Supply Chain Efficiency.
• Customer Loyalty and Retention Strategies.
• Trend Analysis in Social Media Marketing.
• Financial Risk Assessment in Investment Decisions.
• Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

• Income Inequality and Its Impact on Education.
• The Correlation Between Unemployment Rates and Crime Levels.
• Analyzing the Effects of Minimum Wage Changes.
• The Relationship Between Public Health Expenditure and Population Health.
• Demographic Analysis of Housing Affordability.
• The Impact of Immigration on Local Economies.
• Analysis of Gender Pay Gap in Different Industries.
• Statistical Study of Homelessness Causes and Solutions.
• Education Levels and Their Impact on Job Opportunities.
• Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

• Multivariate Analysis of Global Climate Change Data.
• Time-Series Analysis in Predicting Economic Recessions.
• Logistic Regression in Medical Outcome Prediction.
• Machine Learning Applications in Statistical Modeling.
• Network Analysis in Social Media Data.
• Bayesian Analysis of Scientific Research Data.
• The Use of Factor Analysis in Psychology Studies.
• Spatial Data Analysis in Geographic Information Systems (GIS).
• Predictive Analysis in Customer Relationship Management (CRM).
• Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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Female labor force participation

Across the globe, women face inferior income opportunities compared with men. Women are less likely to work for income or actively seek work. The global labor force participation rate for women is just over 50% compared to 80% for men. Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression. When women do work, they earn less. Emerging evidence from recent household survey data suggests that these gender gaps are heightened due to the COVID-19 pandemic.

Women’s work and GDP

Women’s work is posited to be related to development through the process of economic transformation.

Levels of female labor force participation are high for the poorest economies generally, where agriculture is the dominant sector and women often participate in small-holder agricultural work. Women’s participation in the workforce is lower in middle-income economies which have much smaller shares of agricultural activities. Finally, among high-income economies, female labor force participation is again higher, accompanied by a shift towards a service sector-based economy and higher education levels among women.

This describes the posited  U-shaped relationship  between development (proxied by GDP per capita) and female labor force participation where women’s work participation is high for the poorest economies, lower for middle income economies, and then rises again among high income economies.

This theory of the U-shape is observed globally across economies of different income levels. But this global picture may be misleading. As more recent studies have found, this pattern does not hold within regions or when looking within a specific economy over time as their income levels rise.

In no region do we observe a U-shape pattern in female participation and GDP per capita over the past three decades.

Structural transformation, declining fertility, and increasing female education in many parts of the world have not resulted in significant increases in women’s participation as was theorized. Rather, rigid historic, economic, and social structures and norms factor into stagnant female labor force participation.

Historical view of women’s participation and GDP

Taking a historical view of female participation and GDP, we ask another question: Do lower income economies today have levels of participation that mirror levels that high-income economies had decades earlier?

The answer is no.

This suggests that the relationship of female labor force participation to GDP for lower-income economies today is different than was the case decades past. This could be driven by numerous factors -- changing social norms, demographics, technology, urbanization, to name a few possible drivers.

Gendered patterns in type of employment

Gender equality is not just about equal access to jobs but also equal access for men and women to good jobs. The type of work that women do can be very different from the type of work that men do. Here we divide work into two broad categories: vulnerable work and wage work.

The Gender gap in vulnerable and wage work by GDP per capita

Vulnerable employment is closely related to GDP per capita. Economies with high rates of vulnerable employment are low-income contexts with a large agricultural sector. In these economies, women tend to make up the higher share of the vulnerably employed. As economy income levels rise, the gender gap also flips, with men being more likely to be in vulnerable work when they have a job than women.

From COVID-19 crisis to recovery

The COVID-19 crisis has exacerbated these gender gaps in employment. Although comprehensive official statistics from labor force surveys are not yet available for all economies,  emerging studies  have consistently documented that working women are taking a harder hit from the crisis. Different patterns by sector and vulnerable work do not explain this. That is, this result is not driven by the sectors in which women work or their higher rates of vulnerable work—within specific work categories, women fared worse than men in terms of COVID-19 impacts on jobs.

Among other explanations is that women have borne the brunt of the increase in the demand for care work (especially for children). A strong and inclusive recovery will require efforts which address this and other underlying drivers of gender gaps in employment opportunities.

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