sampling plan in marketing research

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• The Online Researcher’s Guide To Sampling

How to Build a Sampling Process for Marketing Research

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When is it necessary to use sampling for market research, defining your target population, questions to ask when building a sampling strategy, how easy is it to reach your target audience, how much money do you have available for your project, how quickly do you need the data, what kind of information are you seeking from participants, calculating and justifying required sample size, selecting a method for sourcing participants.

By Cheskie Rosenzweig, MS, Aaron Moss, PhD, & Leib Litman, PhD

Online Researcher’s Sampling Guide, Part 3: How to Build a Sampling Process for Marketing Research

Most businesses can’t survive without conducting some research. What is our market share? Are our customers happy? Who is likely to buy this product? Questions like these are what lead businesses around the world to spend tens of billions of dollars per year on market research.

Regardless of whether you have a significant market research budget or one with very limited resources, it is of paramount importance for your business that your funds are spent efficiently and effectively. How do you do that? The first step might be recognizing when you do and do not need to gather your own data.

Not all market research requires a team of people to go out and gather data. Sometimes, your business has internal data, or you can use data other people have collected (known as secondary data) to answer your research questions. Internal data can help companies understand consumer behavior, and secondary data might help a company understand the market or its competitors.

But there are some questions no amount of internal or secondary data can answer. How do customers feel about our brand compared to others? How can we improve our product or service? Finding answers to questions like these requires talking to your customers or potential customers, and that means sampling people for the purpose of primary research.

As an example, imagine we lead the research team at a young company based in Minneapolis, Minnesota. Our company, aptly named SunVac, developed a new vacuum that runs on solar energy and never needs to be plugged in. As you might guess, we are excited that our hard work has come to fruition. We did it! We created an environmentally friendly vacuum with no more pesky wires to get tangled!

The problem we have now is that we aren’t sure how much our vacuum is worth on the open market. Although we have some secondary data on how much people will pay for wireless vacuums, we decide our product is sufficiently different from other models that we need to gather data to determine pricing sensitivity and the best way to market our product. The first step is determining who we need to sample.

Before embarking on any research project, it’s important to spend time clearly defining your objectives. Defining what you want to learn will guide your decisions about which source of data is best, how you should sample, and who you should sample.

Consider our company, SunVac. Our research team knows that we should conduct some studies investigating how much people will pay for our product and what kind of messages will convince people to buy it. From here, we need to define a target population for our studies, and while doing so, it is a good time to think about potential sources of sampling bias.

Is it important that our study represent certain demographic groups or people from various regions of the country? Should we make sure men and women are equally represented in the study? Does how much money people make influence whether they will buy our vacuum? Thinking about potential sources of bias can help us clarify who to sample.

Based on intuition and some secondary data, the research team at SunVac has a sense of who may have an interest in our product, who buy the product at different price points, and who respond to different marketing campaigns.

We decide we should sample people who may be in the market for a vacuum cleaner. We also decide it is important to collect data from people in various regions of the country to account for regional differences in environmental attitudes. If we limited our sampling to people in Minneapolis, we might end up with biased results, because Minneapolis is a city ranked cleanest in the U.S. and 6 th -most eco-friendly in the world , meaning people in Minneapolis may value our product more than potential customers elsewhere. Finally, we consider data we have seen that married people vacuum more than single adults. We decide we should sample more married people than singles. So, our target sample is adults from various regions of the US who may be interested in buying a vacuum. Let us next consider where we could collect our sample.

Once you identify a target population, you need to form a plan to reach them and to gather your data. There are several related issues to consider.

Some people are harder to find as research participants than others. CEOs and managers are less plentiful than entry-level employees. There are fewer older adults online than younger adults. When forming a sampling plan, it is important to consider how hard it is to reach your target audience.

The amount of money budgeted for your project will affect your decisions about how to reach your target audience. For example, gathering a nationally representative sample based on probability sampling is often quite expensive. If it isn’t essential that your project be based on probability sampling, many researchers find it more affordable to collect a controlled sample that uses quotas to match to the U.S. census.

The amount of money you have budgeted for your project can also affect other considerations, such as where to find participants. Some online platforms allow researchers to do more of the work in data collection, which lowers overall costs. Other online platforms manage data collection for researchers, which adds to overall costs. How much money you have will influence the decisions you make.

How quickly you need your data will affect not only the total cost of your study, but also your decisions of how to sample. If you need the data quickly, then it doesn’t make sense to adopt a slow strategy like voluntary sampling or face-to-face interviewing.

When researchers need data quickly, they often turn to online sampling sources. The internet makes it possible to run faster and more affordable studies than many other methods of data collection.

The information you’re asking participants to provide may influence how and where you decide to gather data. Specifically, if you are looking for participants to engage in an hour-long task, during which they rate several products and provide detailed responses about each one, then you will probably get the best results from a crowdsourcing platform like Mechanical Turk. Crowdsourcing platforms allow you to control participant compensation, and by paying participants adequately for their time, it is possible to get data from crowdsourcing sites that participants from most online panels would never take the time to provide.

On the other hand, if you are gathering simple survey responses from participants, then there are many platforms that are suited to the type of data you seek to collect.

How might the questions above affect the research decisions we make at SunVac?

First, we know it’s relatively easy to reach our target audience. Any sizeable online panel should have access to adults from around the U.S. and allow us to target married couples.

Second, as a small company, we don’t have a massive budget for research. Because a random sample isn’t necessary for our research questions, we will gather a non-random sample and aim to control for potential sources of bias. For example, we will use quotas in our data collection to ensure we gather data from people of various ethnic and age groups.

Third, we want the data quickly. We know our competitors are close to developing a similar product, and we want to make sure our product hits the market first. As a result, we want to conduct our project within the next two weeks, meaning we should choose a sampling method and source that yield quick data.

Finally, our study asks participants to answer some questions about our product and to tell us which features of different marketing messages are most persuasive. Because our study isn’t too long or too demanding, we can consider a wide range of online panels with which to run our study.

To summarize, we know that most online panels will allow us to sample the people we are interested in, but we need our data quickly and we have a tight budget to stick to. The ideal platform for our project may be something like CloudResearch’s Prime Panels, or if we want to do some of the work ourselves, we might run the study on Mechanical Turk using CloudResearch’s MTurk Toolkit.

Now that we’ve built a sampling plan, we have to decide how many people to sample.

How many people you recruit into your study depends on your goals, the type of study you’re conducting, and how you plan to use your data.

If you’re conducting a survey, as our company, SunVac, is, then you need to consider a few factors when determining sample size. First, how large is the population you’re studying? As the size of the population you seek to understand grows, so does the number of people you need to sample. Our population for the SunVac project is quite large, encompassing nearly all adults in the U.S.

Second, how much inaccuracy are you willing to accept in the results? While your initial reaction may be “none,” it’s important to keep in mind that all sampling entails some margin of error. The question you have to answer is how important it is for your project to minimize the margin of error while balancing the increased costs of gathering a larger sample.

At SunVac, someone on our team has a background in statistical methods. She informs us it would be wise to run a conjoint analysis project asking people to rate the attractiveness of a series of descriptions of vacuum cleaners at different price points and with different features. She explains to us that it will take some time to design the survey itself, but she estimates that for appropriate statistical power to analyze the results among the different market segments we are interested in (region, relationship status, age groups), we will need data from 2,000 potential customers.

Now, you’re ready to find participants. The problem is that there is an overwhelming number of online options to choose from.

Depending on who you want to sample and what you want them to do within your study, online panels and crowdsourcing platforms both offer options for obtaining the sample you are interested in.

Online panels offer access to tens of millions of participants worldwide. When using online panels, researchers can easily target participants based on demographic characteristics, geographic location, psychographics and more. At SunVac, we could easily run our study using an online panel.

In addition to online panels, crowdsourcing platforms like Amazon’s Mechanical Turk are increasingly popular among market researchers. Crowdsourcing platforms give researchers more control over how their study is setup, how communication with participants takes place, and how much participants are compensated. Each of these features can be used to elicit more participant engagement than is typical in online panels.

If we decide at SunVac to conduct our study with an online panel, we will need the ability to collect high-quality data from a diverse sample of 2,000 adults, with a quota for a particular number of men and women who come from different age groups and regions of the country, and are either married or single. This means we will need a platform that allows us to selectively recruit 2,000 vacuum cleaner users for a 15—20 minute survey, and we want to make sure we collect good data from participants who are paying attention.

Ideally, what might happen next for SunVac, and hopefully to you, our reader, is that, in the process of researching how to find the best sample for your needs, you come to this website, read this page, and realize that CloudResearch has what you need. At CloudResearch, we have the ability to connect researchers with samples for nearly any project. In addition, we can provide advice for your data collection or gather the sample for you . Our solutions are tailored to your needs.

Why wait? Reach out today and see how we can help you achieve your research goals. Collect participants via Prime Panels or our MTurk Toolkit by signing up for a CloudResearch account , or ask for our assistance in designing your survey or sampling approach or for help with data collection or analysis today.

Continue Reading: The Online Researcher’s Guide to Sampling

Part 4: Pros and Cons of Different Sampling Methods

Part 1: What Is the Purpose of Sampling in Research?

Part 2: How to Reduce Sampling Bias in Research

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Types of sampling for market research

Market research is crucial for any business that wants to understand the people it is selling its goods and services to. With preliminary research, businesses of all kinds can gain useful insights, identify new selling opportunities, and find ways to allocate their resources efficiently and equitably.

One of the most effective ways to conduct market research is sampling . Sampling utilizes data from a small group, such as a simple random sample, and allows marketers to draw conclusions about a much larger target population.

By ensuring that your representative group is actually representative of the population, and that the questions you are asking are effectively worded, you can pave the way for impactful and productive research. Without sampling, you will inevitably be forced to guess how to reach your audience. Not only will this be inefficient and cause you to miss out on valuable opportunities, but lack of sampling can also cause significant damage to your brand.

Fortunately, you can gain critical insights into your target audience by using the right types of sampling and strategically employing various sampling techniques. In this article, we will answer some of the most common questions that market researchers and business owners have about sampling. By taking the time to understand what sampling actually is, along with the different types of sampling that are currently in place, you can decide if committing to a broader sampling campaign makes sense for your particular organization.  

What is sampling?

Sampling is a term used to describe the process of obtaining data from a small group (or subgroups). Once this data has been gathered, it can then be applied to a larger audience, such as a company’s target market.

Suppose a restaurant is targeting people between the age of 25 and 35 living in an urban area. The restaurant wants to decide what color it should make its logo. Rather than asking everyone in that age group what color makes them most likely to visit the restaurant, the company might take a sample of 100 people from that group and gather their opinions. If more than half of the people say blue is the most appealing color, the company can draw conclusions about 25 to 35-year-olds in general, and adapt their marketing approach in response.

Of course, the conclusions that can be drawn from sampling will only be as good as the sampling frame itself. In this instance, if the restaurant were to just ask random people about their favorite color, rather than those within its target audience, the conclusions it made might not be as reliable. In other instances, creating a pure, simple random sample (SRS) might be more beneficial. Before conducting sampling research, it is important to identify what conclusions you hope to draw and who you are hoping to survey. Once these things have been adequately identified, you’ll be able to use small samples to help you draw big conclusions about almost any topic.  

Why are samples used in survey research?

Researchers use sampling because it helps them efficiently learn about a group in general, without needing to survey the entire group. During an election, for example, it would be impossible to survey every likely voter about who they plan to vote for. Instead, a researcher would ask a specific group of voters about their preferences and attempt to draw broader conclusions from the responses they receive. While this sort of polling certainly presents its own unique challenges, it can still provide valuable—and actionable—insights for all involved.

Sampled surveys can be used to answer many different questions. Learning about how people typically live their lives, how people view the world, and how people use a product or service can help businesses develop better strategies and methods for reaching their target audience. There are many different types of sampling and each of these methods can be effectively applied in different situations, for different market research needs.

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Representative sampling vs. random sampling

The various types of sampling methods will generally fall into one of two categories. The first category is random sampling while the second category is representative sampling .

A random sample, as the name suggests, is a sample of randomly selected individuals, designed to represent the population as a whole. Simple random samples can help companies and other organizations draw broad conclusions about people in general. If a company is trying to sell a product that essentially everyone might use, such as toothpaste, a simple random sample can help them draw broad conclusions. What flavors of toothpaste do people typically prefer? When do people typically brush their teeth? What type of toothbrush do most people use? These are questions that can be effectively answered by asking a wide range of people for their opinion, rather than limiting the survey to a deliberately narrow group.

In contrast, researchers using representative sampling don’t want a random sample of all people. Instead, they want a random sample of people that are representative of a specific group. For example, if a company is selling a product that only some people use, such as skiing equipment, they’d want a sample of individuals that actually use that particular product.

Representative samples can be broken down in myriad different ways. In the example above, “people who ski” could be a distinctive group that helps filter the broader population. In other instances, you might consider breaking the population down by age, demographics, location, income, hobbies, profession, or other traits. As long as you can find enough survey takers to generate statistically significant conclusions, you will have a considerable amount of flexibility when creating a representative group.

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Probability sampling vs. non-probability sampling methods

The different types of sampling can also be distinguished as either probability sampling or non-probability sampling. Essentially, with probability sampling, every single individual within the target group (which can be either random or representative) has an equal chance of being selected for the survey.

With non-probability sampling, on the other hand, some people within this group will be more likely to be selected than others. For example, if the group you hope to draw conclusions about is American adults, but you conduct a survey at a mall in Missouri, you are using a non-probability sampling method for your survey. In this case, you are not randomly sampling American adults because your broad group has been filtered down to “mall shoppers in Missouri.” This particular type of survey is known as a convenience survey (more info below). While it is indeed possible that these mall shoppers might happen to produce results that are similar to the opinions of the American adult population as a whole, it is important to recognize which portions of the broad group are systematically excluded by your sampling methods.  

Probability sampling methods

As suggested, probability sampling is a type of sampling in which every single member of a group has an equal probability of being selected for the survey. Probability sampling can still exist within a filtered group (such as American adults), as long as every representative of this subgroup has an equal chance of being selected.

There are four primary types of probability sampling methods.  

Simple random sampling

Simple random sampling is both simple and random. That means that within a group or subgroup, each member of the population has an equal chance of being selected as a respondent. There are many ways in which a simple random sample can be created. For example, every person within the group might be given a number and then a specific portion of these numbers is selected entirely at random (using a random number generator, drawing from a hat, etc.). Simple random sampling offers the benefit of a “pure” random data set, enabling researchers to draw sweeping conclusions. However, simple random sampling is also criticized for being relatively inefficient.  

Systematic sampling

Systematic sampling is a type of sampling that involves selecting a random starting point in the overall population and choosing sample members at regular intervals. For example, if a researcher has a list of every resident of a city with a population of 300,000, they might choose to generate a random sample of people by surveying every 100 th person featured on the list. In this instance, 3,000 people will be surveyed. 

As long as there is no hidden pattern in the list that might skew the selection process, systematic sampling creates a sample where members of the selected population don’t appear to have anything in common. Systematic sampling still provides most of the benefits of random sampling because, when properly applied, the population essentially is randomly selected. At the same time, this straightforward method requires considerably less effort than other sampling methods.  

Stratified random sampling

Stratified random sampling randomly selects from several subgroups in order to create the final sample. Suppose the researcher wants to gain insight about the opinions of American adults. Rather than simply selecting 500 random adults, the researcher might select 10 adults from each of the 50 states to create the “random” sample population. If each of the subgroups has a lower standard deviation (possibility of error) than the total group, then the margin of error can be systematically decreased.  

Cluster sampling

Cluster sampling creates a sample by pulling people from multiple (but not necessarily all) subgroups of a population. Ideally, each of these subgroups, or clusters, will be a diverse representation of the population as a whole and will also be structurally similar to the other subgroups. Cluster sampling is one of the least expensive forms of probability sampling and is also ideal for sampling relatively large populations. To successfully use this particular type of sampling, it is crucial for the clusters to be consistently structured and for the selections within each cluster to remain random.  

Non-probability sampling methods

While probability sampling can be used to draw conclusions from random (though sometimes slightly modified) groups, non-probability sampling uses groups that are a bit more deliberately structured. Non-probability sampling can help reduce random biases and, in many instances, ensure that key portions of a broader population are included within the sampled population.  

Quota sampling

Quota sampling is a sampling method in which the researcher manipulates the sampling population in order to represent the population as a whole. This type of sampling is especially useful when the broader population includes many different types of people. 

For example, suppose the survey is designed to draw conclusions about American adults. Rather than risking a random sample in which one group (race, gender, age, geographic location, etc.) is either overrepresented or underrepresented, the researcher might deliberately select a proportioned number of individuals from each of the conceivable subgroups. So if Black Americans represent 13% of the population, the researcher would deliberately ensure that the sampling population is actually 13% Black—and adjust other populations to be proportionally representative as well. By doing this, they would avoid a less accurate simple random sample, which might only be between 5-20% Black. Quota sampling is typically used for large, clustered populations, such as the population of the United States.  

Convenience sampling

Convenience sampling, as you might guess, is a type of sampling that is done by surveying a group of people that is easiest to reach. This sampling is often the easiest to conduct and is often very affordable. During a convenience sample, a researcher might go to a crowded public area and ask people if they are willing to be surveyed. This population is by no means randomly selected, but depending on the type of data the researcher is hoping to gather, that might not really matter. Convenience sampling is often used during a pilot study in which a company is trying to learn about the feasibility or popularity of a proposed product.  

Snowball sampling

Snowball sampling is a non-probability sampling method that is designed to help reveal information about populations that are difficult to reach or are “hidden.” With snowball sampling, researchers will encourage their already existing population to reach out to additional members of the population in order to help bolster the underlying data set. While this does create systematic biases, it is one of the best methods for reaching populations that tend to avoid answering random surveys, such as individuals engaging in illegal activity. Snowball sampling is only occasionally used by market researchers, but though it might be problematic, it has helped deliver data where other sampling methods were proven to be ineffective.  

Purposive sampling

Purposive sampling is a type of sampling in which researchers will directly (rather than randomly) select a subpopulation that is supposed to be representative of the population as a whole. This type of sampling is often called “judgment sampling” or “expert sampling” because it involves the judgment of someone who is familiar with the group and its basic characteristics. Purposive sampling is often characteristic of other non-probability sampling, such as quota sampling, but involves an additional layer of human intervention.

Want to learn more about sampling best practices? Read our Ultimate Guide to Market Research.

Survey sampling with a market research panel

Survey sampling with a market research panel, such as SurveyMonkey’s integrated global panel , can help researchers and organizations quickly access a large, random population. When using these sorts of panels, surveyors will have the freedom to control the questions they are asking, the populations they are drawing from, and the types of surveying they choose to use.

Populations can be divided in many different ways. Demographics, geography, professional profile, and more might all be actively considered. These panels can be used for valuable insights, including basic market research, product development, brand tracking, and consumer behavior. By using a panel to look at a specific group of people, businesses can draw crucial conclusions about their broader target audience.  

Which sampling method should you use?

Every type of sampling method will have both pros and cons that come with it. For example, while a simple random sample can decrease bias and help you draw broad conclusions, generating a truly random sample can often be very inefficient. Furthermore, you might want to learn about a specific subgroup, rather than the population as a whole. At the same time, while convenience sampling can help you quickly generate data, these sample populations can be extremely biased and may cloud your final conclusions.

Clearly, there is no universally “best” type of sampling. To determine which type of sampling makes sense for your campaign, you will need to begin by determining what—exactly—you are hoping to learn by conducting the survey. From there, you will need to consider other relevant variables, such as time and cost constraints, the ways in which survey questions will be worded, and whether the population you want to survey can be accessed with ease.

By making an effort to better plan your survey, it will be easier to determine which type of sampling will be most useful for you. With a firm understanding of the different types of sampling and access to valuable resources, such as SurveyMonkey’s audience of more than 80 million people, you can learn a lot about a population and conduct better market research.

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6.3 Steps in a Successful Marketing Research Plan

Learning outcomes.

By the end of this section, you will be able to:

• 1 Identify and describe the steps in a marketing research plan.
• 2 Discuss the different types of data research.
• 3 Explain how data is analyzed.
• 4 Discuss the importance of effective research reports.

Define the Problem

There are seven steps to a successful marketing research project (see Figure 6.3 ). Each step will be explained as we investigate how a marketing research project is conducted.

The first step, defining the problem, is often a realization that more information is needed in order to make a data-driven decision. Problem definition is the realization that there is an issue that needs to be addressed. An entrepreneur may be interested in opening a small business but must first define the problem that is to be investigated. A marketing research problem in this example is to discover the needs of the community and also to identify a potentially successful business venture.

Many times, researchers define a research question or objectives in this first step. Objectives of this research study could include: identify a new business that would be successful in the community in question, determine the size and composition of a target market for the business venture, and collect any relevant primary and secondary data that would support such a venture. At this point, the definition of the problem may be “Why are cat owners not buying our new cat toy subscription service?”

Additionally, during this first step we would want to investigate our target population for research. This is similar to a target market, as it is the group that comprises the population of interest for the study. In order to have a successful research outcome, the researcher should start with an understanding of the problem in the current situational environment.

Develop the Research Plan

Step two is to develop the research plan. What type of research is necessary to meet the established objectives of the first step? How will this data be collected? Additionally, what is the time frame of the research and budget to consider? If you must have information in the next week, a different plan would be implemented than in a situation where several months were allowed. These are issues that a researcher should address in order to meet the needs identified.

Research is often classified as coming from one of two types of data: primary and secondary. Primary data is unique information that is collected by the specific researcher with the current project in mind. This type of research doesn’t currently exist until it is pulled together for the project. Examples of primary data collection include survey, observation, experiment, or focus group data that is gathered for the current project.

Secondary data is any research that was completed for another purpose but can be used to help inform the research process. Secondary data comes in many forms and includes census data, journal articles, previously collected survey or focus group data of related topics, and compiled company data. Secondary data may be internal, such as the company’s sales records for a previous quarter, or external, such as an industry report of all related product sales. Syndicated data , a type of external secondary data, is available through subscription services and is utilized by many marketers. As you can see in Table 6.1 , primary and secondary data features are often opposite—the positive aspects of primary data are the negative side of secondary data.

There are four research types that can be used: exploratory, descriptive, experimental, and ethnographic research designs (see Figure 6.4 ). Each type has specific formats of data that can be collected. Qualitative research can be shared through words, descriptions, and open-ended comments. Qualitative data gives context but cannot be reduced to a statistic. Qualitative data examples are categorical and include case studies, diary accounts, interviews, focus groups, and open-ended surveys. By comparison, quantitative data is data that can be reduced to number of responses. The number of responses to each answer on a multiple-choice question is quantitative data. Quantitative data is numerical and includes things like age, income, group size, and height.

Exploratory research is usually used when additional general information in desired about a topic. When in the initial steps of a new project, understanding the landscape is essential, so exploratory research helps the researcher to learn more about the general nature of the industry. Exploratory research can be collected through focus groups, interviews, and review of secondary data. When examining an exploratory research design, the best use is when your company hopes to collect data that is generally qualitative in nature. 7

For instance, if a company is considering a new service for registered users but is not quite sure how well the new service will be received or wants to gain clarity of exactly how customers may use a future service, the company can host a focus group. Focus groups and interviews will be examined later in the chapter. The insights collected during the focus group can assist the company when designing the service, help to inform promotional campaign options, and verify that the service is going to be a viable option for the company.

Descriptive research design takes a bigger step into collection of data through primary research complemented by secondary data. Descriptive research helps explain the market situation and define an “opinion, attitude, or behavior” of a group of consumers, employees, or other interested groups. 8 The most common method of deploying a descriptive research design is through the use of a survey. Several types of surveys will be defined later in this chapter. Descriptive data is quantitative in nature, meaning the data can be distilled into a statistic, such as in a table or chart.

Again, descriptive data is helpful in explaining the current situation. In the opening example of LEGO , the company wanted to describe the situation regarding children’s use of its product. In order to gather a large group of opinions, a survey was created. The data that was collected through this survey allowed the company to measure the existing perceptions of parents so that alterations could be made to future plans for the company.

Experimental research , also known as causal research , helps to define a cause-and-effect relationship between two or more factors. This type of research goes beyond a correlation to determine which feature caused the reaction. Researchers generally use some type of experimental design to determine a causal relationship. An example is A/B testing, a situation where one group of research participants, group A, is exposed to one treatment and then compared to the group B participants, who experience a different situation. An example might be showing two different television commercials to a panel of consumers and then measuring the difference in perception of the product. Another example would be to have two separate packaging options available in different markets. This research would answer the question “Does one design sell better than the other?” Comparing that to the sales in each market would be part of a causal research study. 9

The final method of collecting data is through an ethnographic design. Ethnographic research is conducted in the field by watching people interact in their natural environment. For marketing research, ethnographic designs help to identify how a product is used, what actions are included in a selection, or how the consumer interacts with the product. 10

Examples of ethnographic research would be to observe how a consumer uses a particular product, such as baking soda. Although many people buy baking soda, its uses are vast. So are they using it as a refrigerator deodorizer, a toothpaste, to polish a belt buckle, or to use in baking a cake?

Select the Data Collection Method

Data collection is the systematic gathering of information that addresses the identified problem. What is the best method to do that? Picking the right method of collecting data requires that the researcher understand the target population and the design picked in the previous step. There is no perfect method; each method has both advantages and disadvantages, so it’s essential that the researcher understand the target population of the research and the research objectives in order to pick the best option.

Sometimes the data desired is best collected by watching the actions of consumers. For instance, how many cars pass a specific billboard in a day? What website led a potential customer to the company’s website? When are consumers most likely to use the snack vending machines at work? What time of day has the highest traffic on a social media post? What is the most streamed television program this week? Observational research is the collecting of data based on actions taken by those observed. Many data observations do not require the researched individuals to participate in the data collection effort to be highly valuable. Some observation requires an individual to watch and record the activities of the target population through personal observations .

Unobtrusive observation happens when those being observed aren’t aware that they are being watched. An example of an unobtrusive observation would be to watch how shoppers interact with a new stuffed animal display by using a one-way mirror. Marketers can identify which products were handled more often while also determining which were ignored.

Other methods can use technology to collect the data instead. Instances of mechanical observation include the use of vehicle recorders, which count the number of vehicles that pass a specific location. Computers can also assess the number of shoppers who enter a store, the most popular entry point for train station commuters, or the peak time for cars to park in a parking garage.

When you want to get a more in-depth response from research participants, one method is to complete a one-on-one interview . One-on-one interviews allow the researcher to ask specific questions that match the respondent’s unique perspective as well as follow-up questions that piggyback on responses already completed. An interview allows the researcher to have a deeper understanding of the needs of the respondent, which is another strength of this type of data collection. The downside of personal interviews it that a discussion can be very time-consuming and results in only one respondent’s answers. Therefore, in order to get a large sample of respondents, the interview method may not be the most efficient method.

Taking the benefits of an interview and applying them to a small group of people is the design of a focus group . A focus group is a small number of people, usually 8 to 12, who meet the sample requirements. These individuals together are asked a series of questions where they are encouraged to build upon each other’s responses, either by agreeing or disagreeing with the other group members. Focus groups are similar to interviews in that they allow the researcher, through a moderator, to get more detailed information from a small group of potential customers (see Figure 6.5 ).

Link to Learning

Focus groups.

Focus groups are a common method for gathering insights into consumer thinking and habits. Companies will use this information to develop or shift their initiatives. The best way to understand a focus group is to watch a few examples or explanations. TED-Ed has this video that explains how focus groups work.

You might be asking when it is best to use a focus group or a survey. Learn the differences, the pros and cons of each, and the specific types of questions you ask in both situations in this article .

Preparing for a focus group is critical to success. It requires knowing the material and questions while also managing the group of people. Watch this video to learn more about how to prepare for a focus group and the types of things to be aware of.

One of the benefits of a focus group over individual interviews is that synergy can be generated when a participant builds on another’s ideas. Additionally, for the same amount of time, a researcher can hear from multiple respondents instead of just one. 11 Of course, as with every method of data collection, there are downsides to a focus group as well. Focus groups have the potential to be overwhelmed by one or two aggressive personalities, and the format can discourage more reserved individuals from speaking up. Finally, like interviews, the responses in a focus group are qualitative in nature and are difficult to distill into an easy statistic or two.

Combining a variety of questions on one instrument is called a survey or questionnaire . Collecting primary data is commonly done through surveys due to their versatility. A survey allows the researcher to ask the same set of questions of a large group of respondents. Response rates of surveys are calculated by dividing the number of surveys completed by the total number attempted. Surveys are flexible and can collect a variety of quantitative and qualitative data. Questions can include simplified yes or no questions, select all that apply, questions that are on a scale, or a variety of open-ended types of questions. There are four types of surveys (see Table 6.2 ) we will cover, each with strengths and weaknesses defined.

Let’s start off with mailed surveys —surveys that are sent to potential respondents through a mail service. Mailed surveys used to be more commonly used due to the ability to reach every household. In some instances, a mailed survey is still the best way to collect data. For example, every 10 years the United States conducts a census of its population (see Figure 6.6 ). The first step in that data collection is to send every household a survey through the US Postal Service (USPS). The benefit is that respondents can complete and return the survey at their convenience. The downside of mailed surveys are expense and timeliness of responses. A mailed survey requires postage, both when it is sent to the recipient and when it is returned. That, along with the cost of printing, paper, and both sending and return envelopes, adds up quickly. Additionally, physically mailing surveys takes time. One method of reducing cost is to send with bulk-rate postage, but that slows down the delivery of the survey. Also, because of the convenience to the respondent, completed surveys may be returned several weeks after being sent. Finally, some mailed survey data must be manually entered into the analysis software, which can cause delays or issues due to entry errors.

Phone surveys are completed during a phone conversation with the respondent. Although the traditional phone survey requires a data collector to talk with the participant, current technology allows for computer-assisted voice surveys or surveys to be completed by asking the respondent to push a specific button for each potential answer. Phone surveys are time intensive but allow the respondent to ask questions and the surveyor to request additional information or clarification on a question if warranted. Phone surveys require the respondent to complete the survey simultaneously with the collector, which is a limitation as there are restrictions for when phone calls are allowed. According to Telephone Consumer Protection Act , approved by Congress in 1991, no calls can be made prior to 8:00 a.m. or after 9:00 p.m. in the recipient’s time zone. 12 Many restrictions are outlined in this original legislation and have been added to since due to ever-changing technology.

In-person surveys are when the respondent and data collector are physically in the same location. In-person surveys allow the respondent to share specific information, ask questions of the surveyor, and follow up on previous answers. Surveys collected through this method can take place in a variety of ways: through door-to-door collection, in a public location, or at a person’s workplace. Although in-person surveys are time intensive and require more labor to collect data than some other methods, in some cases it’s the best way to collect the required data. In-person surveys conducted through a door-to-door method is the follow-up used for the census if respondents do not complete the mailed survey. One of the downsides of in-person surveys is the reluctance of potential respondents to stop their current activity and answer questions. Furthermore, people may not feel comfortable sharing private or personal information during a face-to-face conversation.

Electronic surveys are sent or collected through digital means and is an opportunity that can be added to any of the above methods as well as some new delivery options. Surveys can be sent through email, and respondents can either reply to the email or open a hyperlink to an online survey (see Figure 6.7 ). Additionally, a letter can be mailed that asks members of the survey sample to log in to a website rather than to return a mailed response. Many marketers now use links, QR codes, or electronic devices to easily connect to a survey. Digitally collected data has the benefit of being less time intensive and is often a more economical way to gather and input responses than more manual methods. A survey that could take months to collect through the mail can be completed within a week through digital means.

Design the Sample

Although you might want to include every possible person who matches your target market in your research, it’s often not a feasible option, nor is it of value. If you did decide to include everyone, you would be completing a census of the population. Getting everyone to participate would be time-consuming and highly expensive, so instead marketers use a sample , whereby a portion of the whole is included in the research. It’s similar to the samples you might receive at the grocery store or ice cream shop; it isn’t a full serving, but it does give you a good taste of what the whole would be like.

So how do you know who should be included in the sample? Researchers identify parameters for their studies, called sample frames . A sample frame for one study may be college students who live on campus; for another study, it may be retired people in Dallas, Texas, or small-business owners who have fewer than 10 employees. The individual entities within the sampling frame would be considered a sampling unit . A sampling unit is each individual respondent that would be considered as matching the sample frame established by the research. If a researcher wants businesses to participate in a study, then businesses would be the sampling unit in that case.

The number of sampling units included in the research is the sample size . Many calculations can be conducted to indicate what the correct size of the sample should be. Issues to consider are the size of the population, the confidence level that the data represents the entire population, the ease of accessing the units in the frame, and the budget allocated for the research.

There are two main categories of samples: probability and nonprobability (see Figure 6.8 ). Probability samples are those in which every member of the sample has an identified likelihood of being selected. Several probability sample methods can be utilized. One probability sampling technique is called a simple random sample , where not only does every person have an identified likelihood of being selected to be in the sample, but every person also has an equal chance of exclusion. An example of a simple random sample would be to put the names of all members of a group into a hat and simply draw out a specific number to be included. You could say a raffle would be a good example of a simple random sample.

Another probability sample type is a stratified random sample , where the population is divided into groups by category and then a random sample of each category is selected to participate. For instance, if you were conducting a study of college students from your school and wanted to make sure you had all grade levels included, you might take the names of all students and split them into different groups by grade level—freshman, sophomore, junior, and senior. Then, from those categories, you would draw names out of each of the pools, or strata.

A nonprobability sample is a situation in which each potential member of the sample has an unknown likelihood of being selected in the sample. Research findings that are from a nonprobability sample cannot be applied beyond the sample. Several examples of nonprobability sampling are available to researchers and include two that we will look at more closely: convenience sampling and judgment sampling.

The first nonprobability sampling technique is a convenience sample . Just like it sounds, a convenience sample is when the researcher finds a group through a nonscientific method by picking potential research participants in a convenient manner. An example might be to ask other students in a class you are taking to complete a survey that you are doing for a class assignment or passing out surveys at a basketball game or theater performance.

A judgment sample is a type of nonprobability sample that allows the researcher to determine if they believe the individual meets the criteria set for the sample frame to complete the research. For instance, you may be interested in researching mothers, so you sit outside a toy store and ask an individual who is carrying a baby to participate.

Collect the Data

Now that all the plans have been established, the instrument has been created, and the group of participants has been identified, it is time to start collecting data. As explained earlier in this chapter, data collection is the process of gathering information from a variety of sources that will satisfy the research objectives defined in step one. Data collection can be as simple as sending out an email with a survey link enclosed or as complex as an experiment with hundreds of consumers. The method of collection directly influences the length of this process. Conducting personal interviews or completing an experiment, as previously mentioned, can add weeks or months to the research process, whereas sending out an electronic survey may allow a researcher to collect the necessary data in a few days. 13

Analyze and Interpret the Data

Once the data has been collected, the process of analyzing it may begin. Data analysis is the distillation of the information into a more understandable and actionable format. The analysis itself can take many forms, from the use of basic statistics to a more comprehensive data visualization process. First, let’s discuss some basic statistics that can be used to represent data.

The first is the mean of quantitative data. A mean is often defined as the arithmetic average of values. The formula is:

A common use of the mean calculation is with exam scores. Say, for example, you have earned the following scores on your marketing exams: 72, 85, 68, and 77. To find the mean, you would add up the four scores for a total of 302. Then, in order to generate a mean, that number needs to be divided by the number of exam scores included, which is 4. The mean would be 302 divided by 4, for a mean test score of 75.5. Understanding the mean can help to determine, with one number, the weight of a particular value.

Another commonly used statistic is median. The median is often referred to as the middle number. To generate a median, all the numeric answers are placed in order, and the middle number is the median. Median is a common statistic when identifying the income level of a specific geographic region. 14 For instance, the median household income for Albuquerque, New Mexico, between 2015 and 2019 was $52,911. 15 In this case, there are just as many people with an income above the amount as there are below.

Mode is another statistic that is used to represent data of all types, as it can be used with quantitative or qualitative data and represents the most frequent answer. Eye color, hair color, and vehicle color can all be presented with a mode statistic. Additionally, some researchers expand on the concept of mode and present the frequency of all responses, not just identifying the most common response. Data such as this can easily be presented in a frequency graph, 16 such as the one in Figure 6.9 .

Additionally, researchers use other analyses to represent the data rather than to present the entirety of each response. For example, maybe the relationship between two values is important to understand. In this case, the researcher may share the data as a cross tabulation (see Figure 6.10 ). Below is the same data as above regarding social media use cross tabulated with gender—as you can see, the data is more descriptive when you can distinguish between the gender identifiers and how much time is spent per day on social media.

Not all data can be presented in a graphical format due to the nature of the information. Sometimes with qualitative methods of data collection, the responses cannot be distilled into a simple statistic or graph. In that case, the use of quotations, otherwise known as verbatims , can be used. These are direct statements presented by the respondents. Often you will see a verbatim statement when reading a movie or book review. The critic’s statements are used in part or in whole to represent their feelings about the newly released item.

Infographics

As they say, a picture is worth a thousand words. For this reason, research results are often shown in a graphical format in which data can be taken in quickly, called an infographic .

Check out this infographic on what components make for a good infographic. As you can see, a good infographic needs four components: data, design, a story, and the ability to share it with others. Without all four pieces, it is not as valuable a resource as it could be. The ultimate infographic is represented as the intersection of all four.

Infographics are particularly advantageous online. Refer to this infographic on why they are beneficial to use online .

Prepare the Research Report

The marketing research process concludes by sharing the generated data and makes recommendations for future actions. What starts as simple data must be interpreted into an analysis. All information gathered should be conveyed in order to make decisions for future marketing actions. One item that is often part of the final step is to discuss areas that may have been missed with the current project or any area of further study identified while completing it. Without the final step of the marketing research project, the first six steps are without value. It is only after the information is shared, through a formal presentation or report, that those recommendations can be implemented and improvements made. The first six steps are used to generate information, while the last is to initiate action. During this last step is also when an evaluation of the process is conducted. If this research were to be completed again, how would we do it differently? Did the right questions get answered with the survey questions posed to the respondents? Follow-up on some of these key questions can lead to additional research, a different study, or further analysis of data collected.

Methods of Quantifying Marketing Research

One of the ways of sharing information gained through marketing research is to quantify the research . Quantifying the research means to take a variety of data and compile into a quantity that is more easily understood. This is a simple process if you want to know how many people attended a basketball game, but if you want to quantify the number of students who made a positive comment on a questionnaire, it can be a little more complicated. Researchers have a variety of methods to collect and then share these different scores. Below are some of the most common types used in business.

Is a customer aware of a product, brand, or company? What is meant by awareness? Awareness in the context of marketing research is when a consumer is familiar with the product, brand, or company. It does not assume that the consumer has tried the product or has purchased it. Consumers are just aware. That is a measure that many businesses find valuable. There are several ways to measure awareness. For instance, the first type of awareness is unaided awareness . This type of awareness is when no prompts for a product, brand, or company are given. If you were collecting information on fast-food restaurants, you might ask a respondent to list all the fast-food restaurants that serve a chicken sandwich. Aided awareness would be providing a list of products, brands, or companies and the respondent selects from the list. For instance, if you give a respondent a list of fast-food restaurants and ask them to mark all the locations with a chicken sandwich, you are collecting data through an aided method. Collecting these answers helps a company determine how the business location compares to those of its competitors. 17

Customer Satisfaction (CSAT)

Have you ever been asked to complete a survey at the end of a purchase? Many businesses complete research on buying, returning, or other customer service processes. A customer satisfaction score , also known as CSAT, is a measure of how satisfied customers are with the product, brand, or service. A CSAT score is usually on a scale of 0 to 100 percent. 18 But what constitutes a “good” CSAT score? Although what is identified as good can vary by industry, normally anything in the range from 75 to 85 would be considered good. Of course, a number higher than 85 would be considered exceptional. 19

Customer Acquisition Cost (CAC) and Customer Effort Score (CES)

Other metrics often used are a customer acquisition cost (CAC) and customer effort score (CES). How much does it cost a company to gain customers? That’s the purpose of calculating the customer acquisition cost. To calculate the customer acquisition cost , a company would need to total all expenses that were accrued to gain new customers. This would include any advertising, public relations, social media postings, etc. When a total cost is determined, it is divided by the number of new customers gained through this campaign.

The final score to discuss is the customer effort score , also known as a CES. The CES is a “survey used to measure the ease of service experience with an organization.” 20 Companies that are easy to work with have a better CES than a company that is notorious for being difficult. An example would be to ask a consumer about the ease of making a purchase online by incorporating a one-question survey after a purchase is confirmed. If a number of responses come back negative or slightly negative, the company will realize that it needs to investigate and develop a more user-friendly process.

Knowledge Check

It’s time to check your knowledge on the concepts presented in this section. Refer to the Answer Key at the end of the book for feedback.

• Defining the problem
• Developing the research plan
• Selecting a data collection method
• Designing the sample
• you are able to send it to all households in an area
• it is inexpensive
• responses are automatically loaded into the software
• the data comes in quickly
• Primary data
• Secondary data
• Secondary and primary data
• Professional data
• It shows how respondents answered two variables in relation to each other and can help determine patterns by different groups of respondents.
• By presenting the data in the form of a picture, the information is easier for the reader to understand.
• It is an easy way to see how often one answer is selected by the respondents.
• This analysis can used to present interview or focus group data.

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How to Write a Marketing Sampling Plan

• Small Business
• Advertising & Marketing
• Marketing Plans
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The Basic Steps of the Marketing Research Process

Quantitative data interpretation, how to use social media for qualitative market research.

• Uses of Quantitative & Qualitative Advertising in the Creative Process
• What Must You Discover About the Target Audience Prior to Graphic Design?

A marketing sampling plan maps out how your company intends on gathering data to fulfill its short- and long-term marketing objectives. Methods for collecting market data include polling, surveys and focus groups. Because of its significance, the creation of a marketing sampling plan should be consistent with your company's overall business strategy.

Understanding the Market

It is important to identify your target market, or the type of consumers that your company wants to attract. Key items to focus on include demographic and socioeconomic trends. Take time to understand the size of the target market and whether it is a truly representative sample. This is paramount to formulating a relevant sampling plan. The information you obtain forms the basis for the company's overall marketing strategy for such expenses as advertising and promotion, branding and product positioning.

Data Collection

Decide how, where and when you intend to collect information about your target consumers. Secondary data uses already existing information, such as government census reports or trade publications. Secondary data may also include internal company information like sales invoices. Primary data supplements secondary data and focuses on obtaining first-hand information. Decide on a combination of secondary and primary data collection that satisfies your company's overall marketing research objective.

Research Methodology

Choose which market research methodologies you want to include in the marketing sampling plan. Quantitative market research methods rely on numerical measurement, such as the use of surveys and statistics. Qualitative market research uses in-person interviews, focus groups and similar methods to gather information. Focus on assessment of findings and how the company intends on using the information it gathers. It is important to define the market research within the framework of the company's marketing objectives.

Consideration

Your marketing sampling plan will evolve. You may find that you have to update it, particularly if the company changes strategies or enters new markets. Secondary data, while useful, has its limits but is a good building block because it is inexpensive. Primary data is expensive but often necessary. Therefore, craft a marketing sampling plan with your company's budget in mind.

• FAO: Chapter 7: Sampling in Market Research
• QuickMBA: Marketing Research
• DJS Research: Quantitative Market Research Methods
• Inc.: How to Conduct Qualitative Market Research

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Marketing Research - Sampling

Last updated 22 Mar 2021

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What is sampling? In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. Let's look at sampling in more detail and discuss the most popular types of sampling used in market research.

It would be expensive and time-consuming to collect data from the whole population of a market. Therefore, market researchers make extensive of sampling from which, through careful design and analysis, marketers can draw information about their chosen market.

Sample Design

Sample design covers:

• Method of selection
• Sample structure
• Plans for analysing and interpreting the results.

Sample designs can vary from simple to complex. They depend on the type of information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out; in simple terms the more precision the market researcher requires, the more complex the design and larger the sample size will be.

The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative . It may be necessary to draw a larger sample than would be expected from some parts of the population: for example, to select more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection . This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.

Defining the Population

The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible. This is to ensure that all elements within the population are represented.

The target population is sampled using a sampling frame .

Often, the units in the population can be identified by existing information such as pay-rolls, company lists, government registers etc.

A sampling frame could also be geographical. For example, postcodes have become a well-used means of selecting a sample.

Sample Size

For any sample design, deciding upon the appropriate sample size will depend on several key factors:

• No estimate taken from a sample is expected to be exact: assumptions about the overall population based on the results of a sample will have an attached margin of error
• To lower the margin of error usually requires a larger sample size: the amount of variability in the population, ie the range of values or opinions, will also affect accuracy and therefore size of the sample
• The confidence level is the likelihood that the results obtained from the sample lie within a required precision: the higher the confidence level, the more certain you wish to be that the results are not atypical. Statisticians often use a 95% confidence level to provide strong conclusions
• Population size does not normally affect sample size: in fact the larger the population size, the lower the proportion of that population needs to be sampled to be representative. It's only when the proposed sample size is more than 5% of the population that the population size becomes part of the formulae to calculate the sample size

Types of Sampling

There are many different types of sampling methods, here's a summary of the most common:

Cluster sampling

Units in the population can often be found in certain geographic groups or "clusters" for example, primary school children in Derbyshire.

A random sample of clusters is taken, then all units within the cluster are examined.

• Quick and easy
• Doesn't need complete population information
• Good for face-to-face surveys

Disadvantages

• Expensive if the clusters are large
• Greater risk of sampling error

Convenience sampling

Uses those who are willing to volunteer and easiest to involve in the study.

• Subjects are readily available
• Large amounts of information can be gathered quickly
• The sample is not representative of the entire population, so results can't speak for them - inferences are limited. future data
• Prone to volunteer bias

Judgement sampling

A deliberate choice of a sample - the opposite of random

• Good for providing illustrative examples or case studies
• Very prone to bias
• Samples often small
• Cannot extrapolate from sample

Quota sampling

The aim is to obtain a sample that is "representative" of the overall population.

The population is divided ("stratified") by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.

• Quick and easy way of obtaining a sample
• Not random, so some risk of bias
• Need to understand the population to be able to identify the basis of stratification

Simply random sampling

This makes sure that every member of the population has an equal chance of selection.

• Simple to design and interpret
• Can calculate both estimate of the population and sampling error
• Need a complete and accurate population listing
• May not be practical if the sample requires lots of small visits over the country

Systematic sampling

After randomly selecting a starting point from the population between 1 and * n , every nth unit is selected.

* n equals the population size divided by the sample size.

• Easier to extract the sample than via simple random
• Ensures sample is spread across the population
• Can be costly and time-consuming if the sample is not conveniently located
• Secondary research
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Essential Market Research Tips: Documenting Your Sampling Plan

• News for Rockstars
• September 15, 2021

For professional researchers, properly documenting your sampling plan is critical to ensuring a high-quality market research process.

What is sampling and why is it important?

If you are new to the term, sampling is about deciding who is going to participate in your research and how you will ensure that they truly represent your population of interest.

It is essential to determine your population of interest for your research. The population of interest might include current customers, prospective customers, competitors’ customers, employees, or other groups—all depending on the specific project’s objectives.

Sampling is a classic example of the saying, “garbage in, garbage out.” If you don’t gather data from the right population, then your research results will be meaningless. For an obvious example, if you are doing a study to inform product roadmap planning, you don’t want to survey people who would never buy the product. That would be a sampling problem.

What types of market research projects involve sampling?

What types of market research projects involve sampling? The answer might surprise you.

All of them!

Whether you are conducting surveys, focus groups, in-depth interviews (phone or webcam), online communities, ethnographic research, or any other kind of primary research , you are going to be making decisions about sampling.

Sampling decisions can be complex, and some projects are going to have more rigorous sampling plans than others. However, all studies require decisions about who is going to participate.

Is documenting your sampling decisions really necessary?

Documenting your sampling decisions might seem like an extra step, but it is an important part of the process. Documentation is critical because you are likely going to collaborate with other people who are going to ask questions (or worse, make assumptions).

Documentation is especially handy when you have market research results that are controversial or unexpected. The first thing that the research sponsor/client is going to do is question the validity of the research. If the results are “bad news”, they’ll likely say you did the research with the “wrong” population.

To avoid any misunderstandings, you want to be able to state and demonstrate that you’ve completed the research with the correct population. While the population’s views might not be expected or ideal, careful sampling decisions (and correct data analysis) will ensure that your results reflect reality.

What should you include in your sampling plan documentation?

So now you’re ready to document your sampling plan, but what does that involve? What you document can vary depending on the project requirements, but a good plan will include at least the following:

• What methodology the sampling is for (is this for survey research, ethnography , focus groups, in-depth interviews, etc.)
• How research participants ( sample ) are selected from the population of interest ( target market ). This typically involves specifying the variables and counts/ratios that will be used in screeners and quotas, such as gender quotas.
• How the sample is obtained (the “frame” or “source”). You may use a company or multiple sources for this.
• What incentives or response rate boosters are used, if applicable. In some industries, compensation is very common but in others, it is unethical or banned, so it is important to document this.
• What is known about the expected sample quality and known limitations.
• How many participants total will be included, written as “n=x”.
• What the expected margin of error is for the data.

Current Research Rockstar students: you have related templates in both the Market Research 101 and Sampling Methods for Market Research courses.

To learn more about this and other market research topics, check out our Research Rockstar eLearning course, “Sampling Methods for Market Research” .

And don’t miss our video podcast series, Conversations for Research Rockstars , with new episodes each week. Be sure to see our video podcast episode on today’s topic, Essential Market Research Tips: Documenting Your Sampling Plan . Subscribe and share this video series on YouTube or listen to the audio-only version on Apple Podcasts so you never miss an episode!

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Seeding the Insights Harvest: Understanding Sampling Techniques in Market Research

In the expansive landscape of market research, sampling techniques serve as the compass, guiding researchers through the complex task of understanding diverse populations. The choice of a sampling method is pivotal, as it directly influences the representativeness and reliability of research findings.

This exploration delves into the significance of sampling techniques in market research , examining various methodologies, their strengths, limitations, and the strategic considerations that shape the selection process.

Significance of Sampling Techniques in Market Research

• Representation of Diversity: Sampling techniques are fundamental to achieving a representative sample. A well-designed sample mirrors the diversity of the target population, ensuring that research findings can be generalized confidently.
• Resource Optimization: Effective sampling allows for the optimization of resources. Rather than attempting to survey an entire population, which can be impractical and costly, researchers can strategically select a subset that encapsulates the characteristics of the larger group.
• Statistical Inference: Sampling techniques underpin statistical inference. By concluding a carefully selected sample, researchers can make informed inferences about the broader population, providing valuable insights for decision-making.
• Efficient Data Collection: Sampling facilitates efficient data collection. Researchers can gather insights from a population subset, streamlining the research process and enabling focused analysis without the overwhelming challenge of studying the entire population.

Common Sampling Techniques in Market Research

• Random Sampling: Methodology: Every member of the population has an equal chance of being selected. Application: Random sampling is ideal when the population is homogeneous, and each member is equally likely to represent the entire group. Strengths: Ensures representativeness and minimizes bias. Limitations: This may be impractical for large or dispersed populations.
• Stratified Sampling: Methodology: Divide the population into subgroups (strata) based on certain characteristics, then randomly sample from each stratum. Application: Useful when the population is heterogeneous, researchers want to ensure representation from different subgroups. Strengths: Guarantees representation from all strata, leading to more accurate insights. Limitations: Requires knowledge of the population’s characteristics to create meaningful strata.
• Systematic Sampling: Methodology: Select every kth element from a list after randomly choosing a starting point. Application: Useful when the population is ordered, and researchers want a systematic representation. Strengths: Simplicity and efficiency in selecting a representative sample. Limitations: Susceptible to periodic patterns that may exist in the population list.
• Cluster Sampling: Methodology: Divides the population into clusters, randomly selects clusters, and then includes all members within the chosen clusters. Application: Suitable when the population is naturally grouped, and it is impractical to sample individuals independently. Strengths: Cost-effective and logistically efficient. Limitations: It may introduce intra-cluster homogeneity and inter-cluster heterogeneity.
• Convenience Sampling: Methodology: Involves selecting participants based on ease of access or availability Application: Common in exploratory research or when resources are limited. Strengths: Quick and cost-effective. Limitations: Prone to selection bias, as the sample may not represent the broader population.

Advantages of Effective Sampling Techniques in Market Research

• Increased Generalizability: Effective sampling techniques enhance the generalizability of research findings. Researchers can confidently extrapolate insights to the broader population by selecting a representative sample.
• Resource Optimization: Well-chosen sampling methods optimize resource utilization. Researchers can achieve meaningful results with a manageable sample size, avoiding the impracticality of studying an entire population.
• Minimized Bias: Rigorous sampling techniques minimize bias. Through randomization or careful stratification, researchers reduce the risk of selecting a sample that does not accurately reflect the population.
• Statistical Rigor: Statistical analyses rely on the foundations laid by effective sampling techniques. Researchers can confidently apply statistical tests and inferential methods when the sample is representative and well-designed.
• Efficient Data Collection: Well-structured sampling leads to efficient data collection. Researchers can focus on a population subset, streamlining the research process and making the most available resources.

Potential Pitfalls and Challenges in Sampling Techniques

• Sampling Bias: Sampling bias occurs when the chosen sample is not representative of the population. This can lead to inaccurate conclusions and compromise the external validity of the study.
• Undercoverage: Undercoverage happens when certain population segments are systematically excluded from the sampling process. It can result in a skewed representation and limit the generalizability of findings.
• Nonresponse Bias: Nonresponse bias occurs when individuals selected for the sample do not participate in the study. If nonrespondents differ systematically from respondents, the sample may not accurately reflect the population.
• Sampling Frame Issues: A sampling frame is the list from which the sample is drawn, and issues with the frame can impact the validity of the sample. Inaccurate or outdated sampling frames may introduce biases.
• Logistical Challenges: Certain sampling methods, such as random or stratified sampling, can pose logistical challenges, especially with large or dispersed populations. These challenges may affect the feasibility and cost-effectiveness of the study.

Best Practices for Effective Sampling in Market Research

• Clearly Defined Objectives: Define the research objectives before selecting a sampling method. The choice of sampling technique should align with the study’s goals, ensuring relevance and accuracy.
• Understand Population Characteristics: Gain a thorough understanding of the population characteristics. This knowledge is essential for choosing appropriate sampling methods, especially in stratified sampling or when creating clusters.
• Randomization: Embrace randomization to minimize bias. Random sampling or random assignment within strata enhances the representativeness of the sample.
• Consider Logistics and Resources: Consider logistical constraints and available resources. The chosen sampling method should be practical and feasible within the limitations of time, budget, and access.
• Pilot Testing: Conduct pilot testing to assess the effectiveness of the sampling method. Piloting helps identify potential issues, refine procedures, and ensure the reliability of the selected sampling technique.

Strategic Considerations in Sampling Techniques

• Population Homogeneity vs. Heterogeneity: The level of heterogeneity within the population influences the choice of sampling method. Homogeneous populations may benefit from simpler methods, while heterogeneous populations may require more sophisticated techniques like stratified sampling.
• Research Objectives and Study Design: The objectives of the research and the overall study design play a crucial role in selecting the appropriate sampling method. Exploratory studies may tolerate convenience sampling, while rigorous scientific investigations may demand more stringent methods.
• Resource Allocation: The allocation of resources, both in terms of time and budget, affects the choice of sampling method. Cluster sampling might be more cost-effective in certain situations, while random sampling may be justifiable when resources allow.
• Logistical Feasibility: The logistical feasibility of implementing a sampling method is a practical consideration. Alternative techniques should be explored if certain methods are impractical due to geographical constraints or resource limitations.
• Ethical Considerations: Ethical considerations, such as ensuring informed consent and respecting participant autonomy, should guide the choice of sampling methods. Ethical practices contribute to the credibility and integrity of the research.

Sampling techniques are the cornerstone of market research, providing the scaffolding for insightful conclusions. Carefully selecting a sampling method is not merely a technical exercise but a strategic decision that shapes the entire research endeavor.

By understanding the nuances of different sampling techniques, acknowledging their strengths and limitations, and aligning choices with research objectives, businesses can navigate the intricacies of diverse populations, ensuring that the insights gained are meaningful and representative of the dynamic landscapes they seek to understand.

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Exploring the Types of Sampling in Marketing Research

Introduction to sampling methods in marketing research.

At the core of every successful business lies a deep understanding of its customers, market trends, and opportunities. Market research is the key to unlocking these insights, and it all starts with sampling.

In today’s fiercely competitive landscape, gathering feedback from every existing and potential consumer is a daunting and often unfeasible task. Savvy businesses, however, know the power of sampling. Sampling is the linchpin of effective market research, allowing you to capture a representative subset of your target audience. This select group serves as a microcosm of the larger population, paving the way for invaluable insights and informed decision-making that resonates with your customers. Sampling is your bridge to success in the realm of market research, providing the knowledge and tools you need to thrive in today’s competitive marketplace.

What Is Sampling?

Market research sampling efficiently identifies a small group to represent a larger population, saving time and resources. Within the market research industry, results are typically expected to come from a carefully chosen sample. For the sample to be effective, it must closely match the characteristics of the larger population it seeks to measure.

At its core, sampling is the method of choice for market researchers seeking to understand and draw meaningful insights from a diverse and expansive population, without the constraints of interviewing or accounting for every individual within that population. It’s the bridge between businesses and a deeper understanding of their target audience, enabling well-informed and resonant decision-making.

Importance of Sampling in Market Research

Sampling is the backbone of effective market research, and its significance cannot be overstated. There are several reasons that underscore its importance.

Understanding Your Target Audience

Sampling is vital because it lies at the heart of understanding your target audience. Obtaining the right sample for your research project opens the door to a deeper comprehension of your audience’s needs, attitudes, behaviors, and preferences. It provides not only insights into your product and service but also valuable feedback that can be used to refine and tailor your offerings. The data gleaned from the responses of your carefully selected sample empowers your organization to make not just decisions but better, more informed decisions that resonate with your audience and drive success in a dynamic marketplace.

Efficiency and Cost-Effectiveness

Market research sampling offers a route to efficient learning about a group, without the need to survey the entire group. Take, for example, a national election. Surveying every likely voter about their voting preferences would be an insurmountable task. Instead, researchers ask a specific group of voters about their preferences and draw broader conclusions from the responses they receive. This approach presents its own unique challenges but provides valuable and actionable insights for all involved.

But the importance of sampling isn’t confined to massive populations. It makes sense to use sampling methods in studies focused on populations as small as 500 people. Why? Because it reduces the effort and cost of conducting a study while dramatically expanding the possibilities of research. Imagine you’re exploring a local community’s preferences for a new park design. Surveying every single resident individually would be time-consuming and expensive. Instead, you select a representative sample, just as a chef tastes a spoonful from a pot of soup to adjust the seasoning. That spoonful, or sample, provides the insights needed to enhance the park’s design. It’s a cost-effective approach that streamlines decision-making while delivering valuable insights.

Unlocking Research Possibilities

Sampling doesn’t just streamline research, it also unlocks new research possibilities. It enables us to carry out exit polls during elections, map the spread of epidemics across geographical areas, and conduct nationwide census research that provides a snapshot of society and culture. The flexibility and power of sampling extend the horizons of what’s achievable in market research.

Sampling isn’t merely a tool; it’s the cornerstone of market research, enabling businesses to connect with their target audience, understand their needs, and make informed, resonant decisions. It’s a method that transcends the confines of population size and budget, making meaningful insights accessible to all.

Main Principles of Sampling in Market Research

The principles of sampling in market research are critical to ensuring the validity and reliability of research findings while producing accurate and actionable insights. Here are some of the main principles:

Random Selection : The selection of the sample should be random to ensure that every element in the population has an equal chance of being included. This minimizes bias and allows for generalization for the entire population.

Representativeness : The sample should be representative of the population in terms of key characteristics, such as demographics or behaviors. It should mirror the population’s diversity to draw accurate conclusions.

Sample Size : Determining an appropriate sample size is essential. It should be large enough to provide statistically significant results but not so large that it becomes impractical and expensive.

Sampling Frame : A clear and comprehensive list of the entire population, or sampling frame, is crucial. The sampling frame forms the basis for random selection and ensures that no elements are omitted.

Sampling Methods : Various sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, are available. Choosing the most appropriate method depends on the research objectives and population characteristics.

Sampling Error : Researchers should be aware of the potential for sampling error, which is the variation that occurs between the sample and the entire population due to chance. Minimizing sampling error enhances the reliability of results.

Bias Reduction : Researchers should strive to minimize bias in the sample selection process. Bias can skew results and lead to inaccurate conclusions. Careful planning and execution can help reduce bias.

Data Collection : Data collection methods should be standardized and consistent across the sample to ensure data quality and comparability.

Statistical Analysis : Appropriate statistical techniques should be used to analyze the data collected from the sample. This includes calculating confidence intervals and margins of error.

Ethical Considerations : Researchers must adhere to ethical guidelines and seek informed consent when collecting data from respondents. Privacy and confidentiality of respondents should be maintained.

Nonresponse Management : Strategies should be in place to address nonresponse, as not all selected individuals may participate. High response rates are essential for accurate results.

Post-Stratification and Weighting : In some cases, post-stratification and weighting may be necessary to account for underrepresented groups in the sample, ensuring that the results are reflective of the overall population.

Continuous Monitoring : Ongoing monitoring of the sampling process is important to detect and address any deviations from the intended sampling design.

Types of Sampling Techniques in Marketing Research

In market research sampling, a diverse array of techniques and methodologies empowers researchers to create a representative sample from a given population, thereby unlocking valuable insights. Sampling methods are fundamentally categorized into two main branches: probability-based and non-probability sampling.

Probability Sample

Probability sampling is a method in which each member of the target population has a known, non-zero chance of being selected for the sample. This means that every element in the population has a quantifiable likelihood of inclusion. Probability sampling methods are designed to be objective and free from bias, providing a solid foundation for generalizing research findings to the entire population. Some common probability sampling techniques used in market research include simple random sampling, stratified sampling, systematic sampling, and cluster sampling. These methods ensure that every element in the population has an equal or known probability of being part of the sample, making it possible to draw statistically valid inferences and make accurate generalizations about the population as a whole. Probability sampling is highly regarded for its ability to produce results that are representative and reliable.

Simple Random Sampling

Systematic sampling.

Systematic sampling in market research is a structured and efficient method of selecting a sample from a larger population, where sample members are chosen at regular intervals. This technique involves defining a starting point in the population and a fixed interval, which is used to select every nth member from the list or population frame. Systematic sampling provides a representative cross-section of the population, maintaining a balanced and coherent distribution. It strikes a balance between simplicity and representativeness, making it a valuable tool for researchers seeking to generate robust insights. This method is particularly useful when the population is sorted in a random manner and patterns that could skew the selection process are absent. Systematic sampling is a cost-effective alternative to simple random sampling, especially in cases where a large pool of willing participants is not readily available.

An example of sampling methodology would be if you wanted to survey a population of 20,000 people, you would select every 200th person to be part of your pool of respondents .

Stratified Random Sampling

Cluster Sampling

Cluster sampling and stratified sampling share some common principles. In cluster sampling, the population is divided into clusters, such as geographic regions or natural groupings. Rather than selecting individual respondents, researchers randomly choose specific clusters to form their sample. The objective is to ensure that each selected cluster serves as a microcosm, accurately reflecting the characteristics of the broader population. For instance, when studying school districts, researchers might randomly select a few districts, recognizing that these clusters should ideally provide insights that generalize to the entire student population. Cluster sampling offers an efficient way to capture the diversity within a population while managing the logistical complexities that can arise when dealing with large and dispersed groups. This method simplifies data collection, especially when surveying a widely dispersed or geographically diverse population, making it a valuable tool in large-scale market research studies.

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Non-probability sampling.

Non-probability sampling is a method where the likelihood of any particular member of the target population being included in the sample is unknown and not quantifiable. Non-probability sampling methods are typically used when it’s challenging or impractical to establish a precise probability of selection for each element in the population. These methods are often more subjective and may involve the researcher’s judgment or convenience in selecting sample members.

Non-probability sampling methods are characterized by their potential for bias, as they do not ensure equal or known probabilities of selection for all population elements. Some common non-probability sampling techniques in market research include convenience sampling, judgmental or purposive sampling, quota sampling, and snowball sampling.

Non-probability samples are generally easier and more cost-effective to obtain, but their findings are typically less generalizable to the entire population. Researchers using non-probability sampling must exercise caution in drawing conclusions and be aware of the limitations associated with potential bias and lack of representation. Despite these limitations, non-probability sampling can still provide valuable insights, particularly in situations where probability sampling is impractical.

Convenience Sampling

Judgment/purposive sampling.

Judgment or purposive sampling is a non-probability sampling method that involves the deliberate selection of sample members based on the researcher’s judgment or specific criteria. Researchers handpick individuals or elements from the population who possess certain characteristics or meet criteria relevant to the research objectives. This method is often used when the researcher seeks to capture specific expertise, experiences, or unique perspectives. Judgment sampling is valuable when representativeness is not the primary concern, and researchers are interested in in-depth understanding or insight from participants who possess specialized knowledge or characteristics pertinent to the study. It is a purposeful approach to sampling, allowing researchers to target the individuals who can provide the most valuable and relevant information for their research.

Quota Sampling

Quota sampling is a non-random sampling method that involves dividing a target population into subgroups based on specific characteristics or criteria. Researchers establish quotas for each subgroup and then select elements from each group using various sampling techniques like convenience or judgment sampling. Quota sampling aims to create a sample that represents the broader population by ensuring that the specified quotas within each subgroup are met. It is like stratified random sampling in its attempt to achieve a spread across the population. For example, quotas may be set for different age groups, genders, ethnic backgrounds, etc. However, it is important to note that bias may be introduced if the quotas do not accurately reflect the population, potentially impacting the sample’s representativeness.

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Snowball sampling.

Snowball sampling is a non-probability sampling method employed when researchers encounter difficulty reaching or identifying subjects, especially those belonging to hard-to-reach or hidden populations. This method is particularly useful in situations where participants are challenging to trace or where the topic under investigation is sensitive and not openly discussed. Researchers typically initiate the process by identifying an initial group of participants who are more accessible or willing to participate. These participants are then asked to recruit more individuals from the target population, creating a network that progressively expands like a snowball rolling downhill, which gives this technique its name. While snowball sampling can be effective for reaching populations that tend to avoid traditional random surveys, it introduces systematic biases, making it essential to acknowledge its limitations when interpreting the results.

Choosing the Right Sampling Technique for Your Market Research

Choosing the right sampling technique for your market research project is a vital but multifaceted decision. Several key considerations must be considered to make an informed choice:

Research Goal : Begin by determining whether you require statistically generalizable results. If you do, probability sampling methods are your best choice. If your research focuses on exploratory or qualitative insights, non-probability methods may be more suitable.

Resource Availability : Evaluate your available resources, including time, budget, and expertise. Keep in mind that some sampling methods are more labor-intensive or costly than others.

Population Characteristics : Consider the specific attributes and characteristics of your target population. Are there distinct subgroups within the population that warrant individual study? Assess whether you have access to the entire population or only a part of it.

The Relevance of Sample Size and Sample Selection Errors

Sample size and sample selection error are crucial considerations when selecting a sampling technique for market research, and they play a significant role in the reliability and validity of research findings.

Sample Size:

Statistical Significance : The sample size directly impacts the statistical significance of the results. A larger sample size tends to provide more precise and reliable estimates, making it easier to detect meaningful differences or patterns in the data.

Margin of Error : Sample size is inversely related to the margin of error. A smaller sample size is associated with a larger margin of error, which means the estimates derived from the sample are less precise and may not accurately represent the population.

Cost and Resources : While a larger sample size is desirable for improved accuracy, it often requires more time and resources. Research projects need to strike a balance between sample size and available resources.

Sample Selection Error:

Biased Representations : Sample selection error occurs when there are systematic biases in how the sample is chosen, leading to a sample that doesn’t accurately represent the target population. This can introduce significant errors and distort the research findings.

Random vs. Non-Random Errors : Sample selection errors can be categorized into random and non-random errors. Random errors are due to chance and can often be reduced by increasing the sample size. Non-random errors are systematic and can result from flawed sampling methods or selection bias.

Minimizing Errors : To minimize sample selection errors, researchers must carefully design their sampling methods, ensuring they accurately reflect the population characteristics. Random sampling techniques, like simple random sampling or stratified random sampling, can help reduce these errors.

A balance between sample size and available resources to employ sound sampling techniques to minimize selection errors and enhance the quality of research.

Selecting the Right Sample Provider

Selecting the right sample provider for your market research project is no easy task. Here are some things to consider when you are looking to pick the right sample provider for your research project.

• Your Research Objectives
• Provider’s Reputation and Credibility
• Sample Quality
• Sample Size and Composition Needed
• Transparency of Sample Sources Used
• Data Privacy and Compliance
• Service & Delivery
• Legal Agreements

EMI’s Approach to Sampling

EMI is pretty unique in the online market research sample industry. As a leading sample consultancy, we understand the entire sample landscape, not just a single panel. Our goal is to create a strategic blend of sample based on your project, rather than make your project fit any specific panel. Unlike most market research sample panels, EMI doesn’t own a panel. That means we are not biased toward a specific panel asset. Instead, we create strategic sample blends that best fit the goals of your research, not what best fits any panel we might own.

Over the last 20 years, we have developed a knowledge of the online market research sample industry that is unrivaled when combined with our unbiased sample blending approach. We have built this knowledge by not only working with panel partners throughout the industry. Additionally, we have conducted research-on-research into the online sample industry for more than a decade. This has allowed us to understand the differences between consumer panels, and how they change over time. 

This unparalleled industry knowledge is our driver to provide unbiased, transparent sample consulting and advice to our clients that puts the emphasis on what is right for their research, and not what is right for any specific panel.

Strategic Sample Blending

Strategic sample blending takes traditional blending to the next level and is the best sample design to ensure confident business decisions. It is blending three or more sample providers, but the selection and blending of the selected providers is done in an intentional and controlled manner. Providers are selected to complement one another while reducing the overall sample bias and any potential behavioral or attitudinal impacts a panel can have. This method ensures that sample blending isn’t done simply for blending’s sake. Utilizing our strategic methodology, we build customized blends that best meet clients’ needs while ensuring the best results possible.

Additionally, by strategically selecting providers and managing their allocation, you increase overall feasibility while avoiding “top-up” situations and panel bias, both of which can skew your data.

EMI’s Online Market Research Sample Panel Network

EMI has built a global network of sample partners that gives you access to one of the highest quality pools of respondents of varying demographic, socio-economic, geographical, behavioral, and psychographic characteristics. This gives EMI the ability to create strategic sample blends that best fit your study and provide you with high-quality, deep insights that you need to make better business decisions.

Every market research sample panel in our network has passed our rigorous Partner Assessment Process so we can best understand the recruiting methods, validation process, and other data quality measures they have in place, as well as the ins and outs of their panel. Our strict vetting process ensures we only allow the best sample providers into our network, and that we maintain a high-level of data quality for our clients .

Dedication to Data Quality

EMI has been dedicated to the pursuit and delivery of high-quality, actionable data on behalf of our clients for more than 20 years. This commitment to quality comes from our extensive industry knowledge and our drive to deliver unbiased, actionable quantitative data tailored to the needs of our clients. To do that, we have built a multi-faceted suite of quality measures, including both technological and human elements, to provide the highest quality data possible.

Human Elements

• Partner Assessment Process

• Dedicated Quality Committee

• Response Red Flagging System

• Screener and Questionnaire Design Expertise

• Research-on-Research

Technology Elements

• SWIFT (Proprietary Digital Fingerprinting and De-Duplication)

• Research Defender’s Advanced Bot and Fraud Detection

• AI-Powered Data Scrubbing

• Geo-IP Blocking

• Quality Optimization Rating (QOR)

World-Class Project Management

We don’t just provide you with custom consumer sample solutions, we back it up with our world-class project management services. The team provides 24/7/365 coverage. We work closely with you as an extension of your team. Our goal is to offer a single point of contact, utilizing responsiveness, creativity, and flexibility to help you navigate any issues.

The backbone of all projects is SWIFT, EMI’s proprietary, cloud-based sample management platform. By connecting your survey to our sample network, we are enabled to:

• Launch your projects. • Field and close projects with more speed and accuracy than other panels. • Manage multiple partners where we can ensure there are no duplicates across panels.

EMI’s experience in identifying, vetting, and managing a network of high-quality market research sample panels, combined with our industry-leading approach of strategically blending sample makes us your best option for conducting market research. We will offer your company unique insights that will allow you to uncover exactly who your customer is, how they buy, and why their behavior changes over time. These insights will help you develop your company’s strategies and grow your business.

EMI’s unique combination of expertise, white-glove service, and reach allows us to be your one-stop sample provider — getting it done without compromise.

You can view the full capabilities of SWIFT

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Do you like free samples? I do too! Unfortunately, this is not an explanation of free samples, but it's an article about something that sounds quite similar - a sampling plan.

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• Cell Biology

Define sampling plan.

The sampling plan is a part of the _________ phase.

During a sampling plan in research, _____________, ___________, and the sampling procedure are decided. 

The ___________    involves deciding the target population.  

The   sample size

What are the two types of sampling plans?

Select the probability sampling methods:

C onvenience sampling and judgemental sampling are types of ____________ sampling.

Quota sampling and stratified sampling are examples of probability sampling plan methods.

__________  depends on the ease of accessing a person of interest.   

In stratified sampling, r esearchers divide the group into smaller subgroups called ________ based on their characteristics.  

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This might not be a term you are very familiar with, but it is a significant part of marketing. We know how important research is for marketing. We need to know the target audience to plan a successful marketing campaign, and a sampling plan is essential to make it successful. Wondering how? Keep reading to find out!

Sampling Plan Definition

Knowing the target audience is vital to understanding their needs and wants. Researchers need to study the population to draw conclusions. These conclusions will serve as a basis for constructing a suitable marketing campaign. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan is an outline based on which research is conducted.

A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes.

It is crucial to verify that the sampling plan is representative of all kinds of people to draw accurate conclusions.

Sampling Plan Research

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research.

Check out our explanation of market research to find out more.

Researchers decide the sampling unit, size, and procedure when creating a sampling plan.

Deciding the sampling unit involves defining the target population. The area of interest for the research may contain people that may be out of the scope of the research. Therefore, the researcher must first identify the type of people within the research's parameters.

The sample size will specify how many people from the sampling unit will be surveyed or studied. Usually, in realistic cases, the target population is colossal. Analyzing every single individual is an arduous task. Therefore, the researcher must decide which individuals should be considered and how many people to survey.

The sampling procedure decides how the sample size is chosen. Researchers can do this based on both probability sampling methods and non-probability sampling methods. We will talk about this in more detail in the following sections.

Sampling Plan Types

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods .

In the probability sampling method, the researcher lists a few criteria and then chooses people randomly from the population. In this method, all people of the population have an equal chance to be selected. The probability methods are further classified into:

1. Simple Random Sampling - as the name suggests, this type of sampling picks individuals randomly from the selection.

2. Cluster Sampling - the whole population gets divided into groups or clusters. Researchers then survey people from the selected clusters.

3. Systematic Sampling - researchers select individuals at a regular interval; for example, the researcher will select every 15th person on the list for interviews.

4. Stratified Sampling - researchers divide the group into smaller subgroups called strata based on their characteristics. Researchers then pick individuals at random from the strata.

Difference between cluster sampling and stratified sampling

In cluster sampling, all individuals are put into different groups, and all people in the selected groups are studied.

In stratified sampling, all the individuals are put into different groups, and some people from all groups are surveyed.

A non-probability method involves choosing people at random without any defined criteria. This means that not everybody has an equal chance of being selected for the survey. N on-probability techniques can be further classified into:

1. Convenience Sampling - this depends on the ease of accessing a person of interest.

2. Judgemental Sampling - also known as purposive sampling, includes selecting people with a particular characteristic that supports the scope of the research.

3. Snowball Sampling - used when trying to find people with traits that are difficult to trace. In such cases, the researcher would find one or two people with the traits and then ask them to refer to people with similar characteristics.

4. Quota Sampling - this involves collecting information from a homogenous group.

Steps of a Sample Plan

A sampling plan helps researchers collect data and get results quicker, as only a group of individuals is selected to be studied instead of the whole population. But how is a sampling plan conducted? What are the steps of a sample plan?

A sampling plan study consists of 5 main steps:

1. Sample Definition - this step involves identifying the research goals or what the research is trying to achieve. Defining the sample will help the researcher identify what they have to look for in the sample.

2. Sample Selection - after the sample definition, researchers now have to obtain a sample frame. The sample frame will give the researchers a list of the population from which the researcher chooses people to sample.

3. Sample Size Determination - the sample size is the number of individuals that will be considered while determining the sampling plan. This step defines the number of individuals that the researcher will survey.

4. Sample Design - in this step, the samples are picked from the population. Researchers can select individuals based on probability or non-probability methods.

5. Sample Assessment - this step ensures that the samples chosen are representative enough of the population and ensures quality data collection.

After these processes are finalized, researchers carry forward with the rest of the research, such as drawing conclusions that form a basis for the marketing campaign.

Probability sampling methods are more complex, costly, and time-consuming than non-probability methods.

Sampling Plans Example

Different methods of sampling plans help to yield different types of data. The sampling plan will depend on the company's research goals and limitations. Given below are a few examples of companies that use different types of sampling plans:

1. Simple Random Sampling - A district manager wants to evaluate employee satisfaction at a store. Now, he would go to the store, pick a few employees randomly, and ask them about their satisfaction. Every employee has an equal chance of being selected by the district manager for the survey.

2. Cluster Sampling - A reputed private school is planning to launch in a different city. To gain a better insight into the city, they divided the population based on families with school-aged kids and people with high incomes. These insights will help them decide if starting a branch in that particular city would be worth it or not.

3. Systematic Sampling - A supermarket with many branches decides to reallocate its staff to improve efficiency. The manager decides that every third person, chosen per their employee number, would be transferred to a different location.

4. Stratified Sampling - A research startup is trying to understand people's sleep patterns based on different age groups. Therefore, the whole sampling unit gets divided into different age groups (or strata), such as 0-3 months, 4-12 months, 1-2 years, 3-5 years, 6-12 years, and so on. Some people from all the groups are studied.

5. Convenience Sampling - An NGO is trying to get people to sign up for a "street-clean" program as part of the Earth Day campaign. They have stationed themselves on the sidewalks of a busy shopping street, and are approaching people who pass them by to try and pursue them to join the program.

6. Judgemental Sampling - A real estate company is trying to determine how the rental price hike affects people. To find the answer to this question, they would only have to consider people that live in rented houses, meaning that people who own a home would be excluded from this survey.

7. Snowball Sampling - A pharmaceutical company is trying to get a list of patients with leukemia. As the company cannot go to hospitals to ask for patients' information, they would first find a couple of patients with the illness and then ask them to refer patients with the same illness.

8. Quota Sampling - Recruiters that want to hire employees with a degree from a particular school will group them into a separate subgroup. This type of selection is called quota selection.

Sampling plan - Key takeaways

• During a sampling plan in research, the sampling unit, the sampling size, and the sampling procedure are determined.
• The sample size will specify how many people from the sampling unit will be surveyed or studied.
• The sampling procedure decides how researchers will select the sample size.
• The methods of probability sampling include simple random, cluster, systematic, and stratified sampling.
• The non-probability sampling plan methods include convenience, judgemental, snowball, and quota sampling.
• Sample definition, sample selection, sample size determination, sample design, and sample assessment are the steps of a sample plan.

Flashcards in Sampling Plan 18

A   sampling   plan   outlines the individuals chosen to represent the target population under consideration for research purposes.

During a sampling plan in research, the sampling unit , the sampling size , and the sampling procedure are decided. 

sampling unit

will specify how many people from the sampling unit will be surveyed or studied.

Probability  and  non-probability sampling . 

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Frequently Asked Questions about Sampling Plan

What is a sample plan in marketing? 

Researchers need to study the population to draw conclusions. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes. 

What is a sampling plan and its types? 

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods. Probability sampling methods include simple random, cluster, systematic, and stratified sampling. The non-probability sampling methods include convenience, judgemental, snowball, and quota sampling.

Why is the sampling plan important? 

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research. Observing every person in the selected location is impractical. Therefore, researchers select a group of individuals representative of the population called the sampling unit. This is outlined in the sampling plan. 

What should a marketing plan include? 

A good marketing plan should include the target market, the unique selling proposition, SWOT analysis, marketing strategies, the budget, and the duration of the research. 

What are the components of a sampling plan? 

The sample definition, sample selection, sample size determination, sample design, and sample assessment are the components of a sampling plan. 

Test your knowledge with multiple choice flashcards

The ___________  involves deciding the target population. 

The sample size

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Sampling Marketing — The Complete Guide

Published: March 02, 2023

Oh, samples, the small gifts that help justify any Costco membership. You can get everything from a warmed pizza bite to a smoothie to hand lotion, all in one pass-through.

And don’t get me wrong, this strategy is an incredible tactic that can increase sales, in some cases, by as much as 2,000% . But there is much more to the sample marketing strategy than just enticing snacks and perks.

Product sampling marketing offers benefits to brick-and-mortar companies, online B2C and B2B brands, and everything in between. You can expand your reach, grow customer loyalty, and ultimately increase conversion and decrease churn rates. Sounds intriguing, right? Keep reading to learn how sampling marketing can help your company.

In this article, we will discuss:

What Is Sampling Marketing — In More Detail

Why Sampling Marketing Works

Sampling marketing best practices, sample marketing examples.

What is sampling marketing?

As you probably inferred from above, sampling marketing is nothing more than a tactic to spread awareness of your company and product to a prospective customer. To put it simply, try before you buy.

This can manifest in a variety of forms, from Sephora’s free gifts with a purchase to HubSpot’s 14-day free trial.

The strategy behind sampling marketing is rooted in psychology and behavioral economics. Giving a customer a glimpse of your offering can show them the benefits before they buy.

Here are three major benefits of sampling marketing backed by research.

1. Reciprocity

As Dan Ariely , the modern-day king of behavioral economics at Duke University says, “ Reciprocity is a very, very strong instinct. If somebody does something for you, you really feel a rather surprisingly strong obligation to do something back for them.”

At Costco, the impact of this theory is clear. The graph below shows the direct translation from samples to purchases.

Image source

This same theory stands true for the digital space as well. Giving a potential customer the ability to test out the service before committing creates the same sense of reciprocated obligation.

When they create a relationship with your brand, there is then a further incentive for them to complete the transaction, increasing the number of sales your brand can achieve. You can then build a lasting connection with users that will keep them coming back.

2. Customer Loyalty

Cornell University professor Miguel Gomez conducted a study about wine tastings.

Results showed that customers who enjoyed a tasting were 93% more likely to spend an extra $10 at the winery. They were also highly likely to buy from the business again in the future.

This study furthers the notion that a free sample not only encourages the first purchase but also it promotes a sense of loyalty toward the company.

Customer loyalty is an indispensable tool for growth. In fact, B2B companies with referrals experience a 70% higher conversion rate . This sense of trust will further your business’ customer retention and help you reach new customers alike.

3. Loss Aversion

Sampling marketing works because of our innate human physiological fear of loss, no matter the size. Esteemed of behavioral economist Daniel Kahneman , dedicated much of his studies to this notion and claims that “the concept of loss aversion is certainly the most significant contribution of psychology to behavioral economics.”

Here, when one receives a free trial or sample, they are made to feel as though they own that product. They become much more reluctant to lose it once it’s in their possession. According to Kahneman, the pain of losing is almost twice as powerful as the pleasure of gaining.

How much do I give for free? How do I implement these free samples into my marketing strategy?

You’ll need to answer these age-old questions. But not to worry. These best practices can help you build the right sampling marketing strategy.

Find the sweet-spot quantity.

You have to find the sweet spot for your free offering. Don’t give too much, which would remove the customer’s need to purchase your product. Don’t give too little, or they won’t have the chance to try your offering thoroughly.

Databox found that over 40% of B2B SaaS companies have a free trial between 14-29 days.

Image Source

This timeline is often a sweet spot for software offerings. It’s long enough for users to see how the product can impact their bottom line. However, it’s not so long that users can accomplish everything before the trial is over.

Time-based models won’t work for physical products. For these goods, Shopify shares, “offer a sample that they can use at least two or three times … and customize your sample offering to fit the consumer profile.”

For example, an online news business may offer, on average, five articles a month before asking for a subscription payment.

Bolster new product launches.

The best way to spread the word about a new product is through the users themselves. Product sampling increases the number of users and sales while also promoting user-generated content marketing (UGC).

When these lucky users try out a product, they are more willing to review it and advertise it on their own due to their innate sense of reciprocity .

Today, 89% of shoppers check reviews before making a purchase . Get the word out about your new product through user-generated reviews to reap the benefits.

Use feedback to inform product development.

What better way to understand how your product works for your user than to ask them directly? By giving them a sample of your product for free, with no strings attached, they can try out the product honestly.

Take this opportunity to gather genuine feedback, user reviews, and ratings.

Find ways to tap new markets.

Over 70% of customers look for perspectives that reflect their own , meaning you need to find ways to market to the specific target demographics.

Through product sampling marketing, you can get your foot in the door to these market segments by speaking to them in a relatable way with your UGC strategies.

There are thousands of stellar examples to guide your product sampling journey. Here are three case studies to inspire you.

Warby Parker

Warby Parker is a prime example of how sampling marketing allows the user to try before they buy. The modern, sleek, and trendy eyewear company allows you to choose any five frames to try on at your home for free.

Then, after five days, the customer can buy what makes them feel their best. The rest are shipped back (for free, of course).

After five days of wearing glasses that make me feel like Carrie Bradshaw from Sex and the City, I wouldn’t want to return them either! Warby Parker uses product sampling marketing exactly how it was intended — giving me a taste of the life I could have, but then taking it away before I get too comfortable in my Bradshaw era.

What we love: The personalization of their free samples. Customers can find the perfect frame and then actually use them in practice before committing.

If you are an Apple Music user, it’s okay. We all have that friend and still love them. However, I hate to admit it, but Spotify may have you beat in more than a few ways, such as its personalized interface and accessibility.

Yes, Spotify does have a free tier, but it is definitely not as used as their Premium model. That’s why Spotify offers all of its users a three-month free trial to experience all that Premium has in store.

This free trial really does work. In 2019, they had 217 million active users and 100 million subscribers — meaning a 46% conversion rate.

What we love: Spotify’s three months get you hooked. You’ve made an investment in the app by cultivating playlists that, after three months, you can’t part with. Once you lose that advantage, you can’t go back to the free tier again — with advertisements, worsened audio quality, and no exclusive release access.

Whether COVID made you a Zoom fanatic or an avid Zoom hater, video conferencing is here to stay — and Zoom is at the forefront of that.

However, if you are just using a free personal account, you are limited to 40 minutes. Yesterday, I received an email to upgrade my account with the words: “Sick of the 40-minute limit? This holiday season, stay connected through it all — for free! Today only, claim your FREE MONTH of Zoom!”

What we love: Zoom uses seasons and holidays to target its promotions. The holiday season is a time when everyone wants to connect, and Zoom knows it. They are able to tug on our heartstrings and make us feel glad that they are making their service more accessible at a time when it is needed.

Making the Most of Samples

We’ve all made purchases after using a sample — whether that be Spotify, LinkedIn Premium, Costco, or Sephora. There are plenty of benefits to letting customers try a product, getting them hooked, then closing a sale.

Start small by offering samples of select products. Once you prove out your strategy, you can expand your sampling marketing.

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Sampling and Sample Design – Types and Steps Involved

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Sampling and sample design is an essential factor as it is based on the judgment of the researcher to provide the best information for the objectives study.

A sample is a smaller part of a whole quantitative data that has been collected through surveys or thorough observations. It can be defined as a smaller unit that represents the real data.

The method of collecting samples is called sampling. Sampling is the basis of almost every research and hence is a crucial part of most projects. There are multiple ways that you can use for collecting samples.

Table of Contents

Principles of Choosing a Sample

As mentioned earlier, a sample is just a smaller fragment that represents the real data collected. Thus, the sample should be collected in a way that, when you analyze it, you get the information about the real data.

The sample should be representative of the data. It should be a unit containing all the subdivisions included in the data. This means integrating the sample by reduced proportions must give the recorded quantitative data.

The sample must also be free from errors. Thus, the size of the sample matters too. It shouldn’t be too small to avoid omitting anything or for it to be full of errors. It should be made using a given proportion, so it is error-free.

There is another concept of bias and precision in sampling. You can have four outcomes based on the high and low of the bias and precision scale, respectively. The four outcomes are:

• Precisely wrong, if you are high on both scales.
• Precisely right, if you are high on precision and low on the bias.
• Imprecisely wrong if you are high on bias but low on precision.
• Imprecisely right if you’re low on both scales.

You have a better sample if you have a low bias. Thus, it is preferable to be imprecisely right than to be precisely wrong.

Types of Sampling

There are two types of sampling:

Probability Sampling

• Non Probability Sampling

These two divisions are then subdivided. These are discussed below.

This is the type of sampling where the probability of every part of the sample is known. This type of sampling gives a precise relationship between the sample and the data called the population.

The sample should be representative of the population. This type of sampling tells you for sure if the sample is or not. You can also give a number to the amount of certainty you have the sample being a representative. This number is called significance.

There are different ways of probability sampling. They are:

• Simple Random Sampling
• Stratified Random Sampling
• Proportional Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling

These can be explained as under:

1.  Simple Random Sampling

In this type of sampling, every member of the population, or every constituent of the data, has an equal chance of being selected to be the sample. This is a simple method and doesn’t require a lot of knowledge before the collection of samples.

Even though the method is simple, it has a lot of drawbacks. It is not cost-efficient. It is also not that precise as the sample might not represent the data or population. The samples may have a lot of errors. Thus, this makes this method rather inefficient.

2. Stratified Random Sampling

To better the method of random sampling, the method of stratified random sampling is used. In this type of sampling, the population is divided into strata. The strata are subdivisions of the population that are homogeneous. The sampling is then randomly collected from different strata.

This type of sampling decreases the sampling cost and has a higher accuracy rate than simple random sampling.

It, too, has its disadvantages. The homogeneity traits or the type of data used to construct strata and eventually collect samples may be flawed. This flaw may end up leading to collecting an incorrect sample.

3. Multistage Stratified Random Sampling

This type contains multiple stages for constructing strata and random sampling, hence a multistage stratified random sampling.

The region that has to be sampled is divided into different strata that are randomly selected for sampling. This is the first stage. The next stage includes collecting random samples from the already chosen random strata.

This is different from stratified sampling in the way that a sample is collected from each stratum in the latter as opposed to the former. This is also more efficient and has a lower cost.

Due to randomness in the sampling, it has a lower precision rate. Also, the clustering in this sampling is stronger, even more than simple random sampling.

4. Systematic Sampling

In this type of sampling, the sample is taken from a regularized pattern that can be rectilinear, triangular, or hexagonal; this ensures coverage of all the subsets. The sample selected can be the n th number of each pattern. Thus, this gives systematic coverage.

This also is very efficient, both in terms of sampling and cost. But the downside to this is that it has a lower precision rate.

5. Cluster Sampling

Cluster sampling is done when you have to sample a widespread population. It is done by dividing the population into clusters. Then two or three from the entire clusters are selected.

The sampling is done from the selected two or three clusters. This is cost-efficient but too lacking in high precision.

Non-Probability Sampling

In this sampling method, you can’t know the probability of the part of the sample with confidence.

The conclusions drawn from this probability cannot be for the whole population for sure. This type of sampling method is developed to address specific problems that can’t be solved using random sampling otherwise.

The different types of non-probability sampling are:

• Convenience Sampling
• Quota Sampling
• Purposive sampling
• Snowball Sampling

1. Convenience Sampling

This type of sampling selects a sample based on easy accessibility. The samples are collected as to how convenient they are, hence the name convenience sampling. These samples are easy to collect and organize. But the possibility that the sample is representative of the population is not very high.

2. Quota Sampling

In this type of sampling, the population is divided into categories. The sample is then selected from the divided categories. The sampling is done until the desirable sample is selected from the categories.

3. Purposive sampling

In this type of sampling, only the people who meet the required criteria are approached. It is checked if they meet the other specified criteria. If so, they select the sample. An example where this is done is when doing market research, which is age-specific.

4. Snowball Sampling

In this type of sampling, the research starts with the person who meets the research criteria. This person is then used in aiding to find other people who fit the criteria. This is a good method if thorough research has to be done.

Steps Involved in the Process

Different steps that take the sample process move ahead are

1. Defining the Target Population

For effective business research, the very first step revolves around the definition of the target population. The target population is defined in different terms such as sampling unit, time frame, and extent.

2. Specifying the Sampling Frame

After the target population is defined, the next step lets the researchers decide on the sampling frame that includes the list of elements from which the sample can be easily drawn.

3. Specifying the Sampling Unit

In the third step of sampling and sample design, a sample unit is specified, a basic unit for incorporating a single element or a group of elements of the population that are supposed to be sampled.

4. Selection of the Sampling Method

The fourth step revolves around the selection of different sample units. This method is influenced by different goals, such as business research , time constraints, availability of financial resources, and the nature of the problem that is supposed to be investigated.

5. Determination of Sample Size

In this step of sampling and sample design, the sample size is determined. Different types of classifying techniques come into play while deciding the sample size.

6. Specifying the Sampling Plan

This step plays a crucial role in specifying and deciding the implementation of the research process . You will find out the outlines for the modus operandi of the sampling plan.

7. Selecting the Sample

In this final step of sampling and sample design, the final selection of sample elements occurs. Here, interviewers should stick to those rules crucial for the actual and smooth implementation of the research.

Final Thoughts!

Every method of sampling has its upsides and downsides.

While conducting the research, you have to decide which method is the most suitable for your research.

No one method is exact and is not ideal. Thus, there should be left measures for minute errors or omissions.

The ultimate goal is to select a sample that can be as close as possible to becoming a representative.

Still, having any doubts about what is sampling and sample design? Feel free to ask us in the comment section below.

Liked this post? Check out the complete series on Market research

Related posts:

• Convenience Sampling | How to analyze a convenience sample?
• 7 Steps To Conduct A Sample Survey
• Positioning Process – Steps involved in Positioning
• Report Writing – Elements, Template and Format Sample
• Focus Group Interviews | Purpose, Preparation, and Sample Interviews
• What is Product Sampling? Types, Methods & Tips
• What is Survey Research? Objectives, Sampling Process, Types and Advantages
• Social Exchange Theory – Concept, Benefits, Examples, Variables involved
• Social Identity Theory – Meaning, Variables Involved and Examples
• What is Sampling plan and its application in Market research?

About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Business Jargons

A Business Encyclopedia

Sampling Plan

Definition : A sampling plan provides an outline based on which the researcher performs research. Also, it provides a sketch required for ensuring that the data gathered is a representation of the defined target population. It is widely used in research studies. A researcher designs a sampling plan to prove that the data collected is valid and reliable for the concerned population.

It explains which category the researcher chooses for the survey. Also, it states the right sample size. Additionally, it expresses how the researcher has to be selected out of the population.

Issues Addressed by Sampling Plan

A sampling plan is the base from which the research starts. It includes the following three major decisions:

Sampling Unit

The researcher decides what the sampling unit should be. It involves choosing the category of the population to be surveyed. It defines the specific target population.

Example: In the Banking industry, the researcher decides: what should the sampling unit include. It may cover current account holders, saving account holders, or both.

The researcher takes such decisions at the time of designing the sampling frame. They do so to give all the elements of the target population an equal chance of getting included in the sample.

Sampling unit

The researcher has to determine the sample size. This means how many objects in the sample the researcher will survey. Generally, “the larger the sample size, the more is the reliability”. Therefore, researchers try to cover as many samples as possible.

Sampling Procedure

Which method should the researcher use to perform sampling ? For that, he must ensure that all the objects of the population have a fair and equal change of selection. Generally, researchers use probability sampling for determining the objects for selection. This is because probability sampling represents the sample more accurately.

In this regard, we are going to learn the two sampling methods :

Probability Sampling

• Simple Random Sampling : In this, every item of the sample has an equal chance of getting selected.
• Stratified Sampling : Here, the researcher divides the population into mutually exclusive groups, viz., age group. After that, the researcher will choose the elements randomly from each group.
• Cluster Sampling : Another name for cluster sampling is area sampling. In this, the researcher divides the population into existing groups or clusters. After that he chooses a sample of clusters on a random basis from the population.

However, the researcher usually finds probability sampling costly and time-consuming. In such a case, he can make use of non-probability sampling. It is a sampling by means of choice.

Non-Probability Sampling

• Convenience Sampling : Here, the researcher selects the easiest and most accessible population member.
• Judgment Sampling : Here, the researcher selects those members of the population whom he thinks that will contribute accurate information.
• Quota sampling : Here, the researcher interviews the fixed number of members of each category.

Thus, a researcher can select any kind of sample as per his convenience, subject to it fulfilling the purpose for which research takes place.

Steps involving Sampling Plan

An ideal sampling plan covers the following steps:

Define the target population

First of all, the researcher needs to decide and identify the group or batch for the study. The target population must be alloted identity by using descriptors. These descriptors indicate the characteristics of the elements. This will depict the target population frame.

Choose the data collection method

The researcher must choose a method for collecting the necessary data from the target population elements. For this, he uses information problem definition, data requirements and set research objectives.

Find out the sampling frames required

Once the researcher decides whom or what should be evaluated. The next step is to bring together a list of eligible sampling units. This list must have enough information about each prospective sampling unit. This allows the researcher can communicate with them. An incomplete sampling frame decreases the possibility of drawing a representative sample.

Pick the suitable sampling method

The researcher needs to pick any of the two types of sampling methods. The methods are probability and non-probability sampling. Usually, probability sampling yields better results. Also, it provides valid information about the target population’s criteria.

Ascertain necessary sample sizes and contract rates

The researcher must consider how accurate the sample estimates must be. Also, he needs to take into account how much time and money are available to collect data. To decide the right size of the sample, the researcher has to make the following decisions:

• Variability of population characteristics that is undergoing investigation.
• The confidence level is desired in the estimates.
• Degree of precision needed to estimate the population characteristic.

Design an operating plan for choosing the sample units

The researcher will design the actual procedures to use. He must include all the prospective respondents who form part of the sample.

Execute the operational plan

Carrying out data collection activities. This may involve actually talking to the prospective respondents by way of a telephone interview.

A word from Business Jargons

A sampling plan states the procedure for determining when the group under study is to be accepted or rejected. Further, if the sample gets rejected, the researcher must integrate corrective measures. He should do so after the complete inspection. After that, replacement of defective items with good ones takes place. We call this process a rectifying inspection.

Related terms:

• Stratified Sampling
• Sampling Methods
• Systematic Sampling
• Sampling Error
• Sampling Distribution of Proportion

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• Knowledge Base

Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Chapter Objectives Structure Of The Chapter Random sampling Systematic sampling Stratified samples Sample sizes within strata Quota sampling Cluster and multistage sampling Area sampling Sampling and statistical testing The null hypothesis Type I errors and type II errors Example calculations of sample size Chapter Summary Key Terms Review Questions Chapter References

· Distinguish between probabilistic and non-probabilistic sampling methods · Understand the bases for stratifying samples · Make an informed choice between random and quota samples · Comprehend multistage sampling, and · Appreciate the use of area or aerial sampling.

· if the selection of the sample is done by some non-random method i.e. selection is consciously or unconsciously influenced by human choice · if the sampling frame (i.e. list, index, population record) does not adequately cover the target population · if some sections of the population are impossible to find or refuse to co-operate.

1 the lottery method, e.g. picking numbers out of a hat or bag 2 the use of a table of random numbers.

1 The bases of stratification, i.e. what characteristics should be used to subdivide the universe/population into strata? 2 The number of strata, i.e. how many strata should be constructed and what stratum boundaries should be used? 3 Sample sizes within strata, i.e. how many observations should be taken in each stratum?

1 No stratification scheme will completely "explain" the variability among a set of observations. Past a certain point, the "residual" or "unexplained" variation will dominate, and little improvement will be effected by creating more strata. 2 Depending on the costs of stratification, a point may be reached quickly where creation of additional strata is economically unproductive.

Stratum A (10,000 × 0.5%) = 50 Stratum B (90,000 × 0.5%) = 450

sr = W 1 1 + W 2 2 + W 3 3 + - - - W k k

a. H1: There is a difference between the proportions of housewives aware of the brand, before and after the campaign, or

(a) comparing an experimental product with a currently marketed ones (b) comparing a cheaper product which will be marketed only if it is not inferior to a current product.

for accuracy at the 95% level.

= N.B. This has infinite degrees of freedom. t=6.45

S.E. = 3.3%

(Standard Error)

• First Online: 09 July 2024

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The purpose of international marketing research is to study the characteristics and preferences of a population. The population is defined as the set of all objects that possess some common set of characteristics with respect to some marketing research problems.

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6 Market Research Methods & What They Reveal About Your Audience

Market research, when it’s done well, makes sure that you step into any market with your eyes wide open and a strong understanding of what your target customers will best respond to.

But how do you get market research right? What methods should you use, and how can you entice your target market to talk to you?

Today, we’re going to go through everything you need to know about market research, from why it’s important, to the best methods for your brand.

Table of Contents

Why marketers should care about market research

Qualitative vs quantitative research method, 1. consumer behavior observation, 2. market and competitive analysis, social media listening and analytics with keyhole, 4. surveys and online polls, 5. focus groups and market testing, hashtag analytics and tracking with keyhole, final thoughts, 1. what are primary and secondary research methods, 2. what are paid market research surveys, 3. what is the difference between market and user research, 4. what are common mistakes to avoid in market research.

Market research is vital for everything from pitching your marketing messaging to building customer loyalty. Some benefits of good market research include:

• Helping your brand to give customers exactly what they want.
• Strengthening your position in the market.
• Minimizes investment risk by helping to inform decisions.
• Identifies threats to avoid and opportunities to grab.
• Gives insight into competitor strengths and weaknesses.

Qualitative research has qualifiers. Qualifiers are markers of confident uncertainty. Qualifiers are necessary when data is opinion-based, or isn’t underpinned by numerical data.

So, qualitative research tends to deal in opinions and descriptions. In market research terms, qualitative data tends to come in the form of customer opinions and feedback. It’s gathered using open-ended questions such as “What do you like about our product?”.

Qualitative data is very useful for understanding nuances that can’t be revealed by numerical data. That being said, it can be difficult, costly, and time-consuming both to gather and to analyze.

Quantitative data, by contrast, deals in quantities. Quantitative data is all about numbers. Numerical data based on metrical analysis forms the backbone of quantitative market research. Quantitative customer surveys will use answer formats that can easily be entered into a graph or chart, such as “Yes/No” answers or “Rate X on a scale of 1 to 5”.

6 Market research methods to gather audience insights

The best market research will combine qualitative and quantitative methods for a complete, nuanced, and easily understandable picture of their target market and its needs.

When done well, consumer behavior observation takes a ‘fly on the wall’ approach to consumers. As the name suggests, it involves monitoring consumers to see how they behave in natural settings. 

If you run a bricks and mortar shop, consumer observation would involve watching how your customers behave in your store. You might note down things like the displays that catch their eye, which products they linger over, the route they take around the store, how they respond to atmospheric features like scent, lighting, and music. After a while, behavioral patterns will emerge which will help you to arrange your store and products for best effect.

In an online context, consumer behavior observation will rely more on behavioral analytics. For example, you might look for patterns in page traffic, bounce rates, purchasing behavior, and so on. This will yield valuable insights into the pages and products that catch consumers’ eyes, elements of your website they find frustrating, and so on.

Market and competitive analysis involves looking at your wider market context and taking a peek at how your competitors are faring.

Competitive analysis is a very strategic way to improve your position. Learning more about your competitors and the ways that they engage your primary market helps you to gain a competitive advantage, both by utilizing their more successful strategies (but better!) and differentiating yourself so that you stand out.

In order to analyze your competitors, you need to understand your market. So, before you start, define your primary and secondary markets, including the products you’re pitching at them, the consumers that occupy them, and the competitors also in that space. 

Then, you can start competitor analysis. This can involve everything from signing up to competitor marketing materials to reading their case studies, looking up their publically-available metrics, and monitoring their brand mentions.

Less glamorous brands that struggle to make a splash on social media can benefit a lot from market and competitive analysis, especially when it comes to things like SEO. For example, smaller SaaS brands are unlikely to get a statistically significant amount of brand mentions on platforms like Facebook. But they could benefit from reading a relevant SaaS SEO case study.

3. Social media listening

Social media listening is a powerful way to gain deeper insights about your brand and how your target audience thinks about you. Put very simply, social media listening involves monitoring social platforms for mentions of your brands, engagement with your brand materials, and so on. 

Social media is a very qualitative market, so it’s worth bearing in mind that a lot of what you hear will be opinion. Rather than taking everything you learn personally, look for broad patterns in your brand mentions. For example, if a lot of people are raving about a certain feature of your product, build on that in your next marketing campaign.

Social media listening is where Keyhole comes into its own. Keyhole’s Social Listening Analytics Suite will constantly comb the internet and log all mentions of your brand. You can use this to easily see how widespread your brand mentions are, and to take the temperature of discourse about your brand.

Keyhole will also alert you if something changes in your brand mentions. For example, if you suddenly get a spike in mentions and coverage, Keyhole will let you know.

This allows you to take action quickly. If you’re getting traction for good reasons, you can leap on the opportunity. If it’s for bad reasons, you can quickly dive into damage limitation mode and save your brand from a PR disaster.

Social listening is one of the very best ways to understand how your brand is perceived by your audience. With Keyhole, you won’t miss a single mention.

Online surveys and polls are a good way to gain nuanced consumer insights and get a read on general customer satisfaction. There are various different types of surveys, designed for both qualitative and quantitative research.

Many brands use popups to offer quick surveys to customers based on their experience of the product, site etc. Popup surveys are usually quick and easy for customers to complete, and they’re a good way to get a lot of data very quickly. That being said, some consumers find popup surveys frustrating, and they do add an extra layer of friction to your site experience.

Longer-form questions and surveys allow you to get detailed information from your target customers on a wide range of things. However, it’s harder to get responses to these surveys as they take up more time. In order to encourage people to take more detailed surveys, some brands offer incentives like gift cards or entry into a prize draw.

This method involves bringing people who fit your target audience profile together and holding in-depth interviews and discussions about your product, your marketing messages, your competitors, and so on.

Focus group discussions can be very productive. People will reveal personal insights about your product/service and what they’re looking for that would be hard to glean through other market research techniques.

Market testing is a form of market research that sometimes occurs in focus group settings. This involves handing out product samples to your focus group and asking for feedback. It could also involve showing your customers different types of marketing content and asking them to rate or comment on them.

Market testing in a focus group context gives you the opportunity to observe how customers interact with your product or content, and draw insights that might not otherwise be possible. For example, you can observe non-verbal cues like frowning or enthusiastically grabbing a product. These cues might indicate discomfort or delight in ways that a survey can’t express.

6. Online market monitoring

Online market monitoring involves things like following market trends, perusing publically available sales data, watching follower counts, observing fluctuations in customer behavior, and so on.

Online market monitoring is particularly useful for quickly spotting and grabbing trends and opportunities. For example, many successful B2B SEO strategies involve closely monitoring the B2B market and taking advantage of keyword trends as soon as they appear. As B2B SEO is hard to achieve through means like focus groups and online surveys, online market monitoring is crucial to nail this tricky market.

You need a tool like Keyhole to get online market monitoring right. Keyhole’s hashtag analytics and tracking helps you to effortlessly measure every campaign you’re running, across every social platform. It will tell you what’s working, what isn’t, and what trends you could take advantage of.

Additionally, a robust content management system can streamline the creation, management, and optimization of digital content across your marketing channels, complementing tools like Keyhole.

And that’s not all. Keyhole can generate great-looking reports on your online monitoring with just a few clicks. This is great for seeing success trends at a glance and sharing them with stakeholders.

A good understanding of the market gives you a huge competitive advantage. But understanding doesn’t happen automatically. In order to gain the actionable insights you need, market research is a must.

Keeping track of your market, your target customers, and the ever-changing trends you could use to your advantage. It’s important to conduct regular market research. It’s also a good idea to monitor markets on an ongoing basis.

This is where tools like Keyhole come in. With Keyhole, you can keep close tabs on everything from social media engagement to brand mentions. It’s perfect for social listening and audience analytics. Why not get in touch today and find out what Keyhole can do for you?

Related Articles

Best Practices For Integrating Email Marketing & Social Media Analytics 

How To Use User Generated Content To Bring More Customers 

Frequently Asked Questions

Primary research involves getting data directly from the originator. For example, surveys and focus groups are primary research methods because they involve asking people directly for their own opinions and experiences. Secondary research takes data from a third party source. For example, online market monitoring is usually secondary research, because it uses pre-existing data and analytics gathered by digital platforms.

Paid market research involves rewarding people for completing market research surveys. Monetary incentives are a great way to encourage people to take market research surveys. It also allows you to create longer, more detailed surveys: people are more likely to spend time and effort on a survey they're getting paid for.

Market research studies a broad swathe of consumer behaviors, trends, and needs. User research is more focused on the specific needs and behaviors of product users.

Common market research mistakes include: -Not having clear research goals from the outset. -Asking the wrong questions. -Speaking to the wrong people. -Picking the wrong consumer sample. -Not analyzing your results properly. -Presenting your findings poorly.

How to Write a Business Plan Outline in 9 Steps (Example Included!)

Starting a business often begins with writing a business plan , especially if you need funding . It acts as a roadmap, guiding you through each stage of launching and managing your company, and it presents a clear, compelling case to potential investors and partners. But here's the thing: not everyone finds this step intuitive. That's where a business plan outline can be incredibly helpful.

Creating a detailed business plan outline helps you organize your thoughts and ensure you cover all the key aspects of your business strategy. Plus, it might be just what you need to overcome that blank page and start typing.

Below, you'll find an easy-to-follow guide on how to craft your business plan outline, and an example to show you what it should look like.

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What is an outline of a business plan?

Think of a business plan outline as the skeleton of your entire business plan. It gives a high-level overview of the main sections you'll need to flesh out later. It's not the final document but a crucial step in getting you there.

Simply put, it's like creating a detailed table of contents for your business plan, showing you exactly what information to include and how everything fits together. A well-structured business plan outline also helps you plan things ahead, saving time and effort.

Writing a business plan outline in 9 steps

Follow these steps to build your business plan outline and learn exactly what each section should include.

(Bear in mind that every business plan is unique, tailored to the specific needs and goals of the business. While the structure below is common, the order of sections may vary—only the executive summary will always come first.)

1. Executive summary

Imagine you have just 60 seconds to convince someone to invest in your business. That's the essence of a strong executive summary. Although it appears first on your business plan, this section is often written last because it sums up the entire plan. Think of it as your elevator pitch . This section gives a quick overview of your entire business plan, highlighting key points that grab the reader's attention.

Keep it clear and concise. Start with a brief overview of your business, including its name and what it offers. Summarize your mission statement and objectives, and don’t forget to mention crucial aspects like financial projections and competitive advantages.

2. Company description

Here's where you provide detailed information about your company. Begin with the business name and location. Describe the legal structure (e.g., sole proprietorship, partnership, corporation) and ownership. If your business already exists, share a brief history.

For new ventures, explain the business's nature and the problems you aim to solve. Go into more detail about your vision and mission statements, outlining your goals and the principles guiding your business. This section helps potential investors and stakeholders grasp your company’s identity and purpose.

3. Market research and analysis

This section shares insights into your company’s industry. Start with a landscape analysis to give an overview of the market, including its size, growth rate, and key players.

Next, define your target market and customer demographics—age, location, income, and interests—detailing who your ideal customers are. Identify market needs and trends your business will address, and highlight customer pain points your product or service aims to solve.

Consider conducting a SWOT analysis to evaluate your business's strengths, weaknesses, opportunities, and threats, and gain a strategic view of where your business stands in the competitive landscape.

4. Organization and management

Describe how your business is structured and who runs it. Outline the organizational structure, and if helps, include a chart. Introduce the leadership team and key personnel, highlighting their qualifications and roles. If you have a board of directors, mention them and briefly explain their involvement.

Then, outline your production processes, detailing how your product or service is (or will be) created—from sourcing materials to delivery—to give a comprehensive view of your operational capabilities.

5. Products and services

This section of your business plan outline is crucial for showing potential investors what makes your products and services unique and valuable.

Clearly describe what your business offers, emphasizing your unique selling propositions (USPs) and the benefits and features that set you apart from the competition. Talk about the product life cycle, including any plans for future updates.

If your business holds any intellectual property or proprietary technologies, detail them here to underscore your competitive advantages.

6. Marketing strategy

Having a fantastic product or service is just half the battle. The marketing plan section should outline how you'll reach your target market and convert them into customers.

Begin with market positioning and branding, explaining how you want your brand perceived. Detail your marketing and promotional strategies, including specific tactics to reach your target audience.

Discuss your sales strategy, focusing on how you'll convert leads into customers. Lastly, include your pricing strategy and provide a sales forecast, projecting your expected revenue over a certain period.

7. Operations plan

Here, the goal is to give a detailed overview of the physical and logistical aspects of your company. Start with the business location and facilities, describing where it operates and any significant physical assets. Detail the technology and equipment needed for daily operations.

Briefly describe your supply chain and logistics processes to illustrate how you manage inventory, procurement, and distribution. Finish it by outlining your production process and quality control measures to ensure your products or services consistently meet high standards.

8. Financial plan

Use this section of the business plan to show how your company will succeed financially. Include financial projections like income statements and cash flow statements. Specify how much capital you need and how you plan to use it, discussing funding sources.

Conduct a break-even analysis to estimate when your business will become profitable. Be transparent and address any financial risks and assumptions, outlining how you plan to mitigate them.

9. Appendices and exhibits

In this section, include any additional information that supports your business plan. This might be resumes of key personnel to highlight your team's expertise and experience, or even legal documents and agreements.

Include market research data and surveys to back up your market analysis. Add financial statements for a detailed look at your financial plan. Also, provide detailed product specifications to give a clear understanding of your products and services.

Here's a business plan outline example

Not quite there yet? Take a look at this business plan outline example—it will make everything clear for you.

3.1 Executive Summary

• Overview of the business
• Key points of the business plan

3.2 Company Description

• Business name and location
• History and nature of the business
• Legal structure and ownership
• Vision and mission statement

3.3 Market Research and Analysis

• Industry analysis
• Target market and customer demographics
• Market needs, trends
• Customer pain points
• SWOT analysis

3.4 Organization and Management

• Organizational structure
• Leadership team and key personnel
• Roles and responsibilities
• Board of directors (if applicable)
• Production processes

3.5 Products and Services

• Description of products or services offered
• Unique selling propositions, benefits, features
• Product lifecycle and development plans
• Intellectual property and proprietary technologies

3.6 Marketing Strategy

• Market positioning and branding
• Marketing and promotional strategies
• Sales strategy and tactics
• Pricing strategy and sales forecast

3.7 Operations Plan

• Business location and facilities
• Technology and equipment
• Supply chain and logistics
• Production process and quality control

3.8 Financial Plan

• Financial projections (income statements, balance sheets, cash flow statements)
• Funding requirements and sources
• Break-even analysis
• Financial risks and assumptions

3.9 Appendices and Exhibits (if applicable)

• Supporting documents and additional information
• Resumes of key personnel
• Legal documents and agreements
• Market research data and surveys
• Financial Statements
• Detailed Product Specifications

Bonus tips on how to write a winning business plan

Once you've done your business plan outline, it's time to fill in the gaps and craft a winning business plan. Here are some bonus tips to keep in mind:

• Tailor it to fit your business : Customize sections to meet industry-specific needs and highlight what makes your business unique.
• Keep it clear and concise : Use straightforward language and support your points with data to ensure easy understanding and avoid any confusion.
• Set actionable and realistic goals : Define measurable objectives with clear timelines and milestones to track your progress.
• Update regularly : Keep your plan dynamic by making regular updates to reflect changes in goals, market conditions, and strategies.
• Seek feedback : Gain insights from mentors and advisors to refine your plan.

Read this next: How to Start a Business in 8 Steps: A Comprehensive Guide from Concept to Launch

CNN Plans Launch of Digital Subscription Product by End of 2024 Amid Newsroom Layoffs

By Brian Steinberg

Brian Steinberg

Senior TV Editor

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CNN is finally gearing up to compete more aggressively in the digital future, where rivals have already staked out ground.

The Warner Bros. Discovery -backed news outlet will launch a new subscription product on CNN.com before the end of the year, according to a new memo from CNN Chairman and CEO Mark Thompson , and debut two new free ad-supported digital offerings, one based on CNN’s original series and productions, and another based on its Spanish-language service. The moves come amid an overhaul of CNN’s newsroom that will result in the elimination of 100 positions.

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In some ways, the new concepts echo one of CNN’s biggest recent initiatives: The company in 2022 debuted CNN+, a subscription-based streaming hub that executives said represented the best way to capture die-hard viewers with a mix of lifestyle and news programming that relied on personalities including Wolf Blitzer, Kate Bolduan and Kasie Hunt. Within a month of its launch, the service was shut down by CNN’s new parent corporation .

Thompson, who joined CNN as its new leader in 2023 after logging stints at the BBC and New York Times Co., has vowed to push the journalism unit into new digital frontiers, a bet that he can help build new revenue as CNN’s flagship cable network suffers from the defection of traditional TV watchers to streaming. CNN’s subscriber base is projected to fall 5.6% to 66.3 million in 2024 — an election year — according to estimates from Kagan, a market-research unit of S&P Global Intelligence. According to Kagan, CNN ended 2023 with 70.3 million subscribers.

To accomplish his goals, Thompson said in his memo he will reorganize CNN’s newsgathering structure and eliminate about 100 positions — about 2.8% of CNN’s employee base of 3,500. “Our priority throughout this process will be to treat them and every other CNN colleague with the respect, dignity, and the support you all deserve, including severance packages, career counseling and assistance with job placement,” he said.

CNN will break down divisions between U.S.-based and international news teams, as well as those that may exist between digital, text and video production. “Rather than separate tribes of TV and digital, international and domestic, we need to recognize that we are all journalists and storytellers first and foremost,” Thompson said. “We plan to provide more opportunities for everyone to learn new skills and new forms of storytelling, and more chances to move from one part of CNN to another.”

Thompson also laid out some goals for CNN’s TV operations, where he has recently been willing to cut costs. In February, CNN eliminated its regular morning program, “CNN This Morning” in the belief that the production outlays required to compete with MSNBC’s “Morning Joe” and Fox News Channel’s “Fox & Friends,” along with the usual array of broadcast news mainstays, were no longer worth the price. CNN also launched the short-form news program “5 Things,” which has been streaming on Warner’s Max service, and will expand it to CNN.com

But that won’t be enough. Viewership for CNN’s current primetime lineup has sunk to some of the lowest levels in the network’s history. Charlie Moore, a longtime producer for Anderson Cooper, has been assigned to “find ways to further develop and strengthen our domestic primetime offering,” Thompson said.  In a bid to devise new programming concepts, CNN will launch a TV Futures Lab that is charged with developing and managing programming for Max, but also with devising ways “to migrate the linear news experience to other new digital environments,” Thompson said.

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• Sampling Plan

Do you like free samples? I do too! Unfortunately, this is not an explanation of free samples, but it's an article about something that sounds quite similar - a sampling plan.

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• Cell Biology

Define sampling plan.

The sampling plan is a part of the _________ phase.

During a sampling plan in research, _____________, ___________, and the sampling procedure are decided. 

The ___________    involves deciding the target population.  

The   sample size

What are the two types of sampling plans?

Select the probability sampling methods:

C onvenience sampling and judgemental sampling are types of ____________ sampling.

Quota sampling and stratified sampling are examples of probability sampling plan methods.

__________  depends on the ease of accessing a person of interest.   

In stratified sampling, r esearchers divide the group into smaller subgroups called ________ based on their characteristics.  

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This might not be a term you are very familiar with, but it is a significant part of marketing. We know how important research is for marketing. We need to know the target audience to plan a successful marketing campaign, and a sampling plan is essential to make it successful. Wondering how? Keep reading to find out!

Sampling Plan Definition

Knowing the target audience is vital to understanding their needs and wants. Researchers need to study the population to draw conclusions. These conclusions will serve as a basis for constructing a suitable marketing campaign. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan is an outline based on which research is conducted.

A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes.

It is crucial to verify that the sampling plan is representative of all kinds of people to draw accurate conclusions.

Sampling Plan Research

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research.

Check out our explanation of market research to find out more.

Researchers decide the sampling unit, size, and procedure when creating a sampling plan.

Deciding the sampling unit involves defining the target population. The area of interest for the research may contain people that may be out of the scope of the research. Therefore, the researcher must first identify the type of people within the research's parameters.

The sample size will specify how many people from the sampling unit will be surveyed or studied. Usually, in realistic cases, the target population is colossal. Analyzing every single individual is an arduous task. Therefore, the researcher must decide which individuals should be considered and how many people to survey.

The sampling procedure decides how the sample size is chosen. Researchers can do this based on both probability sampling methods and non-probability sampling methods. We will talk about this in more detail in the following sections.

Sampling Plan Types

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods .

In the probability sampling method, the researcher lists a few criteria and then chooses people randomly from the population. In this method, all people of the population have an equal chance to be selected. The probability methods are further classified into:

1. Simple Random Sampling - as the name suggests, this type of sampling picks individuals randomly from the selection.

2. Cluster Sampling - the whole population gets divided into groups or clusters. Researchers then survey people from the selected clusters.

3. Systematic Sampling - researchers select individuals at a regular interval; for example, the researcher will select every 15th person on the list for interviews.

4. Stratified Sampling - researchers divide the group into smaller subgroups called strata based on their characteristics. Researchers then pick individuals at random from the strata.

Difference between cluster sampling and stratified sampling

In cluster sampling, all individuals are put into different groups, and all people in the selected groups are studied.

In stratified sampling, all the individuals are put into different groups, and some people from all groups are surveyed.

A non-probability method involves choosing people at random without any defined criteria. This means that not everybody has an equal chance of being selected for the survey. N on-probability techniques can be further classified into:

1. Convenience Sampling - this depends on the ease of accessing a person of interest.

2. Judgemental Sampling - also known as purposive sampling, includes selecting people with a particular characteristic that supports the scope of the research.

3. Snowball Sampling - used when trying to find people with traits that are difficult to trace. In such cases, the researcher would find one or two people with the traits and then ask them to refer to people with similar characteristics.

4. Quota Sampling - this involves collecting information from a homogenous group.

Steps of a Sample Plan

A sampling plan helps researchers collect data and get results quicker, as only a group of individuals is selected to be studied instead of the whole population. But how is a sampling plan conducted? What are the steps of a sample plan?

A sampling plan study consists of 5 main steps:

1. Sample Definition - this step involves identifying the research goals or what the research is trying to achieve. Defining the sample will help the researcher identify what they have to look for in the sample.

2. Sample Selection - after the sample definition, researchers now have to obtain a sample frame. The sample frame will give the researchers a list of the population from which the researcher chooses people to sample.

3. Sample Size Determination - the sample size is the number of individuals that will be considered while determining the sampling plan. This step defines the number of individuals that the researcher will survey.

4. Sample Design - in this step, the samples are picked from the population. Researchers can select individuals based on probability or non-probability methods.

5. Sample Assessment - this step ensures that the samples chosen are representative enough of the population and ensures quality data collection.

After these processes are finalized, researchers carry forward with the rest of the research, such as drawing conclusions that form a basis for the marketing campaign.

Probability sampling methods are more complex, costly, and time-consuming than non-probability methods.

Sampling Plans Example

Different methods of sampling plans help to yield different types of data. The sampling plan will depend on the company's research goals and limitations. Given below are a few examples of companies that use different types of sampling plans:

1. Simple Random Sampling - A district manager wants to evaluate employee satisfaction at a store. Now, he would go to the store, pick a few employees randomly, and ask them about their satisfaction. Every employee has an equal chance of being selected by the district manager for the survey.

2. Cluster Sampling - A reputed private school is planning to launch in a different city. To gain a better insight into the city, they divided the population based on families with school-aged kids and people with high incomes. These insights will help them decide if starting a branch in that particular city would be worth it or not.

3. Systematic Sampling - A supermarket with many branches decides to reallocate its staff to improve efficiency. The manager decides that every third person, chosen per their employee number, would be transferred to a different location.

4. Stratified Sampling - A research startup is trying to understand people's sleep patterns based on different age groups. Therefore, the whole sampling unit gets divided into different age groups (or strata), such as 0-3 months, 4-12 months, 1-2 years, 3-5 years, 6-12 years, and so on. Some people from all the groups are studied.

5. Convenience Sampling - An NGO is trying to get people to sign up for a "street-clean" program as part of the Earth Day campaign. They have stationed themselves on the sidewalks of a busy shopping street, and are approaching people who pass them by to try and pursue them to join the program.

6. Judgemental Sampling - A real estate company is trying to determine how the rental price hike affects people. To find the answer to this question, they would only have to consider people that live in rented houses, meaning that people who own a home would be excluded from this survey.

7. Snowball Sampling - A pharmaceutical company is trying to get a list of patients with leukemia. As the company cannot go to hospitals to ask for patients' information, they would first find a couple of patients with the illness and then ask them to refer patients with the same illness.

8. Quota Sampling - Recruiters that want to hire employees with a degree from a particular school will group them into a separate subgroup. This type of selection is called quota selection.

Sampling plan - Key takeaways

• During a sampling plan in research, the sampling unit, the sampling size, and the sampling procedure are determined.
• The sample size will specify how many people from the sampling unit will be surveyed or studied.
• The sampling procedure decides how researchers will select the sample size.
• The methods of probability sampling include simple random, cluster, systematic, and stratified sampling.
• The non-probability sampling plan methods include convenience, judgemental, snowball, and quota sampling.
• Sample definition, sample selection, sample size determination, sample design, and sample assessment are the steps of a sample plan.

Flashcards in Sampling Plan 18

A   sampling   plan   outlines the individuals chosen to represent the target population under consideration for research purposes.

During a sampling plan in research, the sampling unit , the sampling size , and the sampling procedure are decided. 

sampling unit

will specify how many people from the sampling unit will be surveyed or studied.

Probability  and  non-probability sampling . 

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Frequently Asked Questions about Sampling Plan

What is a sample plan in marketing? 

Researchers need to study the population to draw conclusions. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes. 

What is a sampling plan and its types? 

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods. Probability sampling methods include simple random, cluster, systematic, and stratified sampling. The non-probability sampling methods include convenience, judgemental, snowball, and quota sampling.

Why is the sampling plan important? 

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research. Observing every person in the selected location is impractical. Therefore, researchers select a group of individuals representative of the population called the sampling unit. This is outlined in the sampling plan. 

What should a marketing plan include? 

A good marketing plan should include the target market, the unique selling proposition, SWOT analysis, marketing strategies, the budget, and the duration of the research. 

What are the components of a sampling plan? 

The sample definition, sample selection, sample size determination, sample design, and sample assessment are the components of a sampling plan. 

Test your knowledge with multiple choice flashcards

The ___________  involves deciding the target population. 

The sample size

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Finding the Best Time to Buy a House

Since there’s no single perfect time to buy a house, buyers should carefully plan while understanding their financial limitations and the market trends.

NEW YORK – The desire to own your own home burns fiercely within many of us. But pinpointing the exact moment to make that dream a reality can feel as elusive as catching a shooting star. Market conditions, interest rates and even the season – all these factors swirl around this momentous decision.

Key considerations that will empower you to conquer your own homeownership journey:

Striking a balance

The allure of a seller’s market is undeniable but remember, a seller’s market often translates to steeper property values. On the other hand, a buyer’s market offers an abundance of options but might raise questions about market stability. The ultimate goal? Finding the sweet spot where affordability meets long-term value.

Interest rates play a crucial role in determining your monthly mortgage payment. Historically low rates can open a golden window of opportunity, but don’t forget that rates are subject to change. Don’t get so caught up in the market that you miss out on a chance to make your move.

Are you ready to be a homeowner?

Homeownership isn’t just about the joy of having your own space; it’s a financial commitment. A crucial factor in this equation is your personal financial readiness.

What you need to consider:

• Credit Score: A strong credit score unlocks the door to better interest rates, saving you a significant amount of money in the long run.
• Down Payment: A substantial down payment reduces the loan amount you need to borrow, leading to lower monthly payments.

A long-term vision

Buying a house is an investment in your future, a marathon, not a sprint. Look beyond the initial mortgage payment and factor in expenses like property taxes, homeowners’ insurance and potential maintenance costs. Create a realistic budget that incorporates the factors to ensure your long-term financial stability.

Buyer’s frenzy or negotiation nirvana?

Spring and autumn are traditionally the busiest seasons for real estate, with a wider range of houses hitting the market. While this offers more options, it can also lead to intense bidding wars. Winter months, on the other hand, can be a haven for negotiation due to the quieter market conditions.

Timing is everything

The most important factor is to be financially prepared and make informed decisions about the timing of your home purchase. This ensures you maximize your investment and secure the best possible deal.

There’s no single perfect time to buy a house. The key lies in careful planning, understanding your financial capabilities and staying informed about market trends. By arming yourself with this knowledge, you’ll be well-equipped to make a wise choice and transform the dream of homeownership into a triumphant reality.

(c) 2024 2022 Independent Media and affiliated companies. All rights reserved Provided by SyndiGate Media Inc. (Syndigate.info).

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