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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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9 Key stages in your marketing research process

You can conduct your own marketing research. Follow these steps, add your own flair, knowledge and creativity, and you’ll have bespoke research to be proud of.

Marketing research is the term used to cover the concept, development, placement and evolution of your product or service, its growing customer base and its branding – starting with brand awareness , and progressing to (everyone hopes) brand equity . Like any research, it needs a robust process to be credible and useful.

Marketing research uses four essential key factors known as the ‘marketing mix’ , or the Four Ps of Marketing :

• Product (goods or service)
• Price ( how much the customer pays )
• Place (where the product is marketed)
• Promotion (such as advertising and PR)

These four factors need to work in harmony for a product or service to be successful in its marketplace.

The marketing research process – an overview

A typical marketing research process is as follows:

• Identify an issue, discuss alternatives and set out research objectives
• Develop a research program
• Choose a sample
• Gather information
• Gather data
• Organize and analyze information and data
• Present findings
• Make research-based decisions
• Take action based on insights

Step 1: Defining the marketing research problem

Defining a problem is the first step in the research process. In many ways, research starts with a problem facing management. This problem needs to be understood, the cause diagnosed, and solutions developed.

However, most management problems are not always easy to research, so they must first be translated into research problems. Once you approach the problem from a research angle, you can find a solution. For example, “sales are not growing” is a management problem, but translated into a research problem, it becomes “ why are sales not growing?” We can look at the expectations and experiences of several groups : potential customers, first-time buyers, and repeat purchasers. We can question whether the lack of sales is due to:

• Poor expectations that lead to a general lack of desire to buy, or
• Poor performance experience and a lack of desire to repurchase.

This, then, is the difference between a management problem and a research problem. Solving management problems focuses on actions: Do we advertise more? Do we change our advertising message? Do we change an under-performing product configuration? And if so, how?

Defining research problems, on the other hand, focus on the whys and hows, providing the insights you need to solve your management problem.

Step 2: Developing a research program: method of inquiry

The scientific method is the standard for investigation. It provides an opportunity for you to use existing knowledge as a starting point, and proceed impartially.

The scientific method includes the following steps:

• Define a problem
• Develop a hypothesis
• Make predictions based on the hypothesis
• Devise a test of the hypothesis
• Conduct the test
• Analyze the results

This terminology is similar to the stages in the research process. However, there are subtle differences in the way the steps are performed:

• the scientific research method is objective and fact-based, using quantitative research and impartial analysis
• the marketing research process can be subjective, using opinion and qualitative research, as well as personal judgment as you collect and analyze data

Step 3: Developing a research program: research method

As well as selecting a method of inquiry (objective or subjective), you must select a research method . There are two primary methodologies that can be used to answer any research question:

• Experimental research : gives you the advantage of controlling extraneous variables and manipulating one or more variables that influence the process being implemented.
• Non-experimental research : allows observation but not intervention – all you do is observe and report on your findings.

Step 4: Developing a research program: research design

Research design is a plan or framework for conducting marketing research and collecting data. It is defined as the specific methods and procedures you use to get the information you need.

There are three core types of marketing research designs: exploratory, descriptive, and causal . A thorough marketing research process incorporates elements of all of them.

Exploratory marketing research

This is a starting point for research. It’s used to reveal facts and opinions about a particular topic, and gain insight into the main points of an issue. Exploratory research is too much of a blunt instrument to base conclusive business decisions on, but it gives the foundation for more targeted study. You can use secondary research materials such as trade publications, books, journals and magazines and primary research using qualitative metrics, that can include open text surveys, interviews and focus groups.

Descriptive marketing research

This helps define the business problem or issue so that companies can make decisions, take action and monitor progress. Descriptive research is naturally quantitative – it needs to be measured and analyzed statistically , using more targeted surveys and questionnaires. You can use it to capture demographic information , evaluate a product or service for market, and monitor a target audience’s opinion and behaviors. Insights from descriptive research can inform conclusions about the market landscape and the product’s place in it.

Causal marketing research

This is useful to explore the cause and effect relationship between two or more variables. Like descriptive research , it uses quantitative methods, but it doesn’t merely report findings; it uses experiments to predict and test theories about a product or market. For example, researchers may change product packaging design or material, and measure what happens to sales as a result.

Step 5: Choose your sample

Your marketing research project will rarely examine an entire population. It’s more practical to use a sample - a smaller but accurate representation of the greater population. To design your sample, you’ll need to answer these questions:

• Which base population is the sample to be selected from? Once you’ve established who your relevant population is (your research design process will have revealed this), you have a base for your sample. This will allow you to make inferences about a larger population.
• What is the method (process) for sample selection? There are two methods of selecting a sample from a population:

1. Probability sampling : This relies on a random sampling of everyone within the larger population.

2. Non-probability sampling : This is based in part on the investigator’s judgment, and often uses convenience samples, or by other sampling methods that do not rely on probability.

• What is your sample size? This important step involves cost and accuracy decisions. Larger samples generally reduce sampling error and increase accuracy, but also increase costs. Find out your perfect sample size with our calculator .

Step 6: Gather data

Your research design will develop as you select techniques to use. There are many channels for collecting data, and it’s helpful to differentiate it into O-data (Operational) and X-data (Experience):

• O-data is your business’s hard numbers like costs, accounting, and sales. It tells you what has happened, but not why.
• X-data gives you insights into the thoughts and emotions of the people involved: employees, customers, brand advocates.

When you combine O-data with X-data, you’ll be able to build a more complete picture about success and failure - you’ll know why. Maybe you’ve seen a drop in sales (O-data) for a particular product. Maybe customer service was lacking, the product was out of stock, or advertisements weren’t impactful or different enough: X-data will reveal the reason why those sales dropped. So, while differentiating these two data sets is important, when they are combined, and work with each other, the insights become powerful.

With mobile technology, it has become easier than ever to collect data. Survey research has come a long way since market researchers conducted face-to-face, postal, or telephone surveys. You can run research through:

• Social media ( polls and listening )

Another way to collect data is by observation. Observing a customer’s or company’s past or present behavior can predict future purchasing decisions. Data collection techniques for predicting past behavior can include market segmentation , customer journey mapping and brand tracking .

Regardless of how you collect data, the process introduces another essential element to your research project: the importance of clear and constant communication .

And of course, to analyze information from survey or observation techniques, you must record your results . Gone are the days of spreadsheets. Feedback from surveys and listening channels can automatically feed into AI-powered analytics engines and produce results, in real-time, on dashboards.

Step 7: Analysis and interpretation

The words ‘ statistical analysis methods ’ aren’t usually guaranteed to set a room alight with excitement, but when you understand what they can do, the problems they can solve and the insights they can uncover, they seem a whole lot more compelling.

Statistical tests and data processing tools can reveal:

• Whether data trends you see are meaningful or are just chance results
• Your results in the context of other information you have
• Whether one thing affecting your business is more significant than others
• What your next research area should be
• Insights that lead to meaningful changes

There are several types of statistical analysis tools used for surveys. You should make sure that the ones you choose:

• Work on any platform - mobile, desktop, tablet etc.
• Integrate with your existing systems
• Are easy to use with user-friendly interfaces, straightforward menus, and automated data analysis
• Incorporate statistical analysis so you don’t just process and present your data, but refine it, and generate insights and predictions.

Here are some of the most common tools:

• Benchmarking : a way of taking outside factors into account so that you can adjust the parameters of your research. It ‘levels the playing field’ – so that your data and results are more meaningful in context. And gives you a more precise understanding of what’s happening.
• Regression analysis : this is used for working out the relationship between two (or more) variables. It is useful for identifying the precise impact of a change in an independent variable.
• T-test is used for comparing two data groups which have different mean values. For example, do women and men have different mean heights?
• Analysis of variance (ANOVA) Similar to the T-test, ANOVA is a way of testing the differences between three or more independent groups to see if they’re statistically significant.
• Cluster analysis : This organizes items into groups, or clusters, based on how closely associated they are.
• Factor analysis: This is a way of condensing many variables into just a few, so that your research data is less unwieldy to work with.
• Conjoint analysis : this will help you understand and predict why people make the choices they do. It asks people to make trade-offs when making decisions, just as they do in the real world, then analyzes the results to give the most popular outcome.
• Crosstab analysis : this is a quantitative market research tool used to analyze ‘categorical data’ - variables that are different and mutually exclusive, such as: ‘men’ and ‘women’, or ‘under 30’ and ‘over 30’.
• Text analysis and sentiment analysis : Analyzing human language and emotions is a rapidly-developing form of data processing, assigning positive, negative or neutral sentiment to customer messages and feedback.

Stats IQ can perform the most complicated statistical tests at the touch of a button using our online survey software , or data from other sources. Learn more about Stats iQ now .

Step 8: The marketing research results

Your marketing research process culminates in the research results. These should provide all the information the stakeholders and decision-makers need to understand the project.

The results will include:

• all your information
• a description of your research process
• the results
• conclusions
• recommended courses of action

They should also be presented in a form, language and graphics that are easy to understand, with a balance between completeness and conciseness, neither leaving important information out or allowing it to get so technical that it overwhelms the readers.

Traditionally, you would prepare two written reports:

• a technical report , discussing the methods, underlying assumptions and the detailed findings of the research project
• a summary report , that summarizes the research process and presents the findings and conclusions simply.

There are now more engaging ways to present your findings than the traditional PowerPoint presentations, graphs, and face-to-face reports:

• Live, interactive dashboards for sharing the most important information, as well as tracking a project in real time.
• Results-reports visualizations – tables or graphs with data visuals on a shareable slide deck
• Online presentation technology, such as Prezi
• Visual storytelling with infographics
• A single-page executive summary with key insights
• A single-page stat sheet with the top-line stats

You can also make these results shareable so that decision-makers have all the information at their fingertips.

Step 9 Turn your insights into action

Insights are one thing, but they’re worth very little unless they inform immediate, positive action. Here are a few examples of how you can do this:

• Stop customers leaving – negative sentiment among VIP customers gets picked up; the customer service team contacts the customers, resolves their issues, and avoids churn .
• Act on important employee concerns – you can set certain topics, such as safety, or diversity and inclusion to trigger an automated notification or Slack message to HR. They can rapidly act to rectify the issue.
• Address product issues – maybe deliveries are late, maybe too many products are faulty. When product feedback gets picked up through Smart Conversations, messages can be triggered to the delivery or product teams to jump on the problems immediately.
• Improve your marketing effectiveness - Understand how your marketing is being received by potential customers, so you can find ways to better meet their needs
• Grow your brand - Understand exactly what consumers are looking for, so you can make sure that you’re meeting their expectations

Download now: 8 Innovations to Modernize Market Research

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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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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Access for free at https://openstax.org/books/principles-marketing/pages/1-unit-introduction

• Authors: Dr. Maria Gomez Albrecht, Dr. Mark Green, Linda Hoffman
• Publisher/website: OpenStax
• Book title: Principles of Marketing
• Publication date: Jan 25, 2023
• Location: Houston, Texas
• Book URL: https://openstax.org/books/principles-marketing/pages/1-unit-introduction
• Section URL: https://openstax.org/books/principles-marketing/pages/6-3-steps-in-a-successful-marketing-research-plan

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Effective Market Research: Sampling Plan Example

Understanding the significance of market research.

Comprehending the profound significance of market research is paramount for businesses aiming for sustained growth and success.

It is fundamental to strategic decision-making, ensuring businesses remain adaptive, competitive, and well-positioned for sustainable success in dynamic markets.

The Role of Market Research in Business Growth

Market research is a critical tool that guides companies toward success by allowing them to understand market dynamics, customer preferences, behaviors, and evolving industry trends.

By understanding their target audience on a deep level, businesses can tailor their products, services, and marketing strategies to resonate with consumers, thereby enhancing customer satisfaction and loyalty.

Essentially, it helps businesses make informed decisions, adapt to market changes, and identify new opportunities, leading to business growth.

Top Benefits of Conducting Quality Market Research

Enhancing customer understanding.

Quality market research dives into the intricacies of consumer behavior, providing nuanced insights into preferences, habits, and purchasing trends. By going beyond surface-level data, businesses can effectively segment their target audience, allowing for the creation of personalized marketing strategies that resonate with specific customer groups.

Additionally, through methodologies such as surveys or interviews, businesses gather valuable feedback, enabling them to address customer concerns promptly and improve overall satisfaction.

Mitigating Risks

Thorough market research includes a comprehensive analysis of competitors, uncovering their strategies, strengths, and weaknesses. This competitive intelligence is instrumental in mitigating risks by helping businesses anticipate challenges and respond proactively.

Market trend forecasting allows businesses to stay ahead of consumer behavior and preference shifts while staying informed about regulatory changes. This reduces the risk of non-compliance and associated penalties.

Supporting Strategic Planning

Quality market research serves as the cornerstone of data-driven decision-making. By providing a rich dataset, businesses can formulate effective strategic plans based on accurate and relevant information.

Conducting a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) enables businesses to identify internal and external factors affecting their operations, guiding strategic decisions.

When considering new markets or product launches, market research helps develop entry strategies, minimizing uncertainties and maximizing the potential for success.

Improving Competitiveness

Market research aids businesses in identifying their Unique Selling Proposition (USP) by understanding what sets them apart from competitors.

Benchmarking against industry standards allows businesses to set realistic goals and continually improve their performance compared to competitors.

Furthermore, the ability to adapt to market dynamics by staying informed about industry changes positions businesses to maintain a competitive edge in the marketplace.

Ready to get started?

Key components of a market research sampling plan.

• Define your target population
• Determine the optimal sample size
• Select a sampling method that aligns with your research objectives and unique audience

Defining the Target Population for Your Sampling Plan

Identifying the right target population is crucial to the success of your market research initiative. A nuanced understanding of your customer base is essential, encompassing not only who they are but also their behaviors, preferences, and needs. 

This involves a comprehensive analysis of demographic factors, such as age, gender, income, and geographic location, as well as psychographic elements, including lifestyles, interests, and values.

By dissecting and categorizing your audience in these terms, you gain a clearer picture of the specific segments that are most relevant to your research. This approach ensures that the data collected is accurate and directly applicable to the areas of your business that matter most. 

Ultimately, defining the target population is akin to laying the groundwork for precise and actionable insights, allowing you to tailor your market research efforts to the pulse of your customer base.

Deciding on the Appropriate Sample Size

Deciding on the right sample size is a delicate balance that directly influences the reliability and cost-effectiveness of your market research. 

Achieving statistical significance is paramount, ensuring the data collected accurately reflects patterns within the broader target population. However, it’s equally crucial to strike a balance to avoid overextending resources.

The chosen sample size should be substantial enough to capture the diversity and nuances of the target population, minimizing the risk of skewed or misleading results. At the same time, optimization is key to managing costs and streamlining efforts efficiently. 

A carefully selected sample size not only enhances the precision of your findings but also allows for a more focused allocation of resources, maximizing the impact of your research endeavors. 

This thoughtful approach to sample size determination is instrumental in ensuring that your market research meets the highest standards of statistical rigor and remains practical and resource-efficient.

Check out our  market research sample calculator  if you need help determining your sample size.

Selecting the Right Sampling Method for Your Research

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

Research Goal

Begin by determining whether you require results that need to be generalized. 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.

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
• 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.

Non-Probability Sample

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.

Some common non-probability sampling techniques in market research include:

• Convenience sampling
• Judgmental or purposive sampling
• Quota sampling
• Snowball sampling

Non-probability samples are generally more accessible and cost-effective, but their findings are typically less generalizable to the entire population.

Best Practices for Conducting Effective Quantitative Market Research

Engaging in effective market research involves a commitment to a set of best practices that not only meet regulatory standards but elevate the overall quality and impact of the research efforts.

By utilizing them collectively, they contribute to the robustness of market research, enabling businesses to gather insights that are not only legally sound but also strategically valuable in informing key decisions.

Approaching Market Research Ethically

Ethical considerations are not just regulatory requirements but integral components that underscore the reliability and integrity of your market research. 

Prioritizing informed consent and safeguarding data privacy are paramount. Transparent communication about the purpose and implications of the research builds trust with respondents, fostering a positive relationship that, in turn, enhances the quality of data collected. 

Adhering to ethical practices is not only a legal obligation but a strategic choice that elevates the ethical standing of your research endeavors.

Involving Diverse Groups in Your Sample Selection

The inclusivity of your sample selection is a key factor in ensuring the relevance and reliability of your research findings. 

By intentionally incorporating diverse groups that mirror the entire target market, you capture a broader spectrum of perspectives, behaviors, and preferences. 

This approach leads to more comprehensive and actionable insights, allowing your market research to transcend biases and offer a more accurate representation of the varied dynamics within your audience.

Ensuring Data Accuracy and Validity

The success of any market research endeavor hinges on the accuracy and validity of the collected data. Rigorous data collection and analysis methodologies are essential to maintain the integrity of the research findings. 

Continuous review and refinement of these processes further enhance data quality. By consistently validating and cross-referencing data points, businesses can ensure that the insights derived from the research are reliable and can be confidently used to inform strategic decisions. 

The commitment to data accuracy is foundational to the overall effectiveness of your market research initiatives.

Market Research Sample Plan Example

A quality sample plan should have the following information:

Recap of Project Specifications

The project specifications that have been determined should be recapped, including the following components:

• Target Audience
• Incidence Rate (IR)
• Length of Interview (LOI)
• Sample Size (N)
• Targetable Quotas
• Non-Targetable Quotas
• Device Type Allowed
• Survey Languages

Sample Costs and Feasibility

A quality sample plan should also contain a breakdown of feasibility and costs. These costs can include the sample cost and any additional costs like programming, hosting, etc.

One aspect that should be included is a breakdown of the sample providers being used. If your sample provider does not provide that information, ask them for it. 

Additional Notes

There should also be a section with any additional notes relevant to the study.

Want a real example of a sample plan?

Sample aggregating versus sample blending.

There have been many changes in the industry over the last decade, from industry consolidation to technological advancements and more. All of the changes have led to a shift in market researchers using multiple sample sources in their sample plans.

There are two main ways of utilizing multiple sample sources in a sample plan: Sample Aggregating and Sample Blending.

Sample Aggregating

Sample aggregating is when multiple suppliers are used because a single sample source cannot provide all the completes needed for a particular study. 

Other sample sources are added at the end of the study to gather the rest of the required completes. 

There is no magic number of sample sources added with this method; sources are added until the needed feasibility is achieved. This method can lead to duplication and sample bias.

Sample Blending

Sample blending is the process of using multiple suppliers, usually three or more, and setting limits on the number of completes each panel can get.

Strategic Sample Blending

Strategic sample blending takes sample blending to the next level. 

It is the best sample design to ensure confident business decisions. It is blending three or more sample providers. Still, 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 behavior or attitudinal impacts a panel can have. This method ensures that sample blending isn’t done simply for blending’s sake. 

Utilizing EMI’s 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.

IntelliBlend

IntelliBlend® is EMI’s patented methodology of strategically blending sample sources in an intentional and controlled approach to deliver the most representative and accurate demographic, behavioral, and attitudinal data. This approach includes double opt-in research panels but may also include non-traditional sources such as social media, which is utilized in a limited and controlled manner. IntelliBlend® can vary from project to project based on the research needs. Each project’s unique blend is developed by leveraging proprietary research-on-research data and over 20 years of sample experience.

EMI’s Approach to Sampling

Founded in 1999, EMI has been a leader in online sample and strategic sample blending for over 20 years. We have been a sample consultancy since not only our inception but since the infancy of online sample.

Over the years, we have developed a knowledge of the sample industry that is unrivaled when combined with our transparent strategic sample blending approach. We have built this knowledge by not only working with panel partners throughout the industry but also conducting research-on-research into the online sample industry for more than a decade to understand the differences between consumer panels and how they change over time.

This unparalleled industry knowledge is the driver to providing transparent sample consulting and advice to our clients that emphasizes what is right for their research and not what is right for any specific panel.

EMI’s 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. EMI can create strategic sample blends that best fit your study and provide you with high-quality, deep insights needed 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 maintain high data quality for our clients.

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Types of sampling design and which to choose

A successful survey requires an effective sampling design. SurveyMonkey can help.

While you’d certainly like to have information from every person in your target market when you’re conducting research, it just isn’t possible—but that doesn’t mean you can’t complete your research objectives. When you need to gather data from your target market, you can select a representative sample to participate in the research. This sample is the foundation of your research, so you’ll need to select the best sampling design to obtain your sample.

Let’s take a closer look at sampling and sampling design, plus how they fit in with your market research needs.

What is sampling, and why use it in your research?

In the context of market research, a sample is a subset of a larger group of people you want to draw conclusions about (a population). Sampling is the process of choosing the group that you ultimately use to obtain your research data. These definitions are informative, but they don’t provide details. 

Let’s say you want to conduct market research, and your target population is women in the US over the age of 35. You know that, unfortunately, you can’t possibly obtain responses from ALL women in the US over 35, but you need feedback that represents this market. To resolve your problem, you use a subset of the larger population you’re targeting. This subset is considered representative of the population as a whole and makes gathering data much more manageable.

The selected subset must be truly representative of the population, or your study may suffer from sampling bias and affect the accuracy and usefulness of your results. But how large should your sample be to obtain the best results? Sample size may be calculated using population size, the margin of error, and confidence level. 

Begin with your population size. This is the total number of individuals in the group you want to study. Then, determine your margin of error —how much you expect your sample results to reflect the opinions of your population. 5% is the most commonly used margin of error. 

Lastly, you need to determine the confidence level. This percentage reflects how confident you are that the population would select an answer within a set range.

This is the formula for determining sample size:

N = population size

e = margin of error (percentage in decimal form)

z = z-score (number of standard deviations a given proportion is away from the mean- use the chart below)

To make things a little easier for you, SurveyMonkey has provided a sample size calculator that will do the math for you. Just enter your variables, and our tool will do the rest.

What is sampling design?

Sampling design is the method you use to choose your sample. There are several types of sampling designs, and they all serve as roadmaps for the selection of your survey sample. The objective of sampling design is to ensure that your selected sample allows you to generalize your findings to the entire population you’re targeting.

Keep in mind the following points when developing your survey design:

• Define the universe of your study: This is the set of objects you are studying. This could be the population of a city, the number of workers in a warehouse, or fans of a particular television show.
• Consider your sampling unit: Will it be geographical, social, or individual?
• Gather your sampling frame: This is the list of names from which your sample will be drawn.
• Determine sample size: Use the equation above or our helpful sample size calculator .
• Factor in budgetary limitations: This will impact both the size and type of sample and may even lead you to use a non-probability sample.

What are the types of sampling design?

Sampling design can be divided into two main categories, probability, and non-probability sampling. In probability sampling, every person in the target population (either random or representative) has an equal chance of being selected for the sample. In non-probability sampling, some individuals in the group will be more likely to be selected than others. 

Take a close look at your research goals (including the level of accuracy desired and your budget) to determine which type of sampling will best help you achieve those goals. 

Probability sampling

Probability sampling ensures that every member of your sample has an equal probability of being selected for your research. There are four main types of probability sampling: simple random, cluster, systematic, and stratified.

Simple random sampling

As the name suggests, simple random sampling is both simple and random. With this method, you may choose your sample with a random number generator or by drawing from a hat, for example, to provide you with a completely random subset of your group. This allows you to draw generalized conclusions about the whole population based on the data provided from the subset (sample).

As an example, let’s say that your population is the employees of your company. You take each of your 1,500 employees and randomly assign numbers to each one. Then, using a random number generator, you select 150 numbers. Those 150 are your sample.

Cluster sampling

In cluster sampling, your population is divided into subgroups that have similar characteristics to the whole population. Instead of selecting individuals, you randomly select an entire subgroup for your sample. 

There is a higher probability of error with this method because there could be differences between the clusters. You cannot guarantee that the sample you use is truly representative of the entire population you’re studying.

Let’s look at your company again. The 1,500 employees are spread across 25 offices with close to the same number of employees in each office. You use cluster sampling to choose the employees of four offices to use as your sample.

Systematic sampling

Similar to simple random sampling, systematic sampling is even easier to conduct. In this method, each individual in the desired population is assigned a number. Instead of randomly generating numbers, participants are chosen at regular intervals. It’s important that there is no hidden pattern in the list that may skew the sample. 

For example, if your research population is comprised of the employees at your company and you generate a list of all their names from HR, it’s important to ensure that the list is not in any kind of order. If the list is by department or team and/or seniority, you risk skipping individuals from certain departments or seniority levels. 

Once your list is randomized, you choose a starting number, #8, for example, and from that point forward, you select every tenth employee—18, 28, 38, etc. 

Stratified sampling

In stratified random sampling , you divide a population into smaller subgroups called strata. The strata are based on the shared attributes of the individuals, such as income, age range, or education level. This method is used when you believe that these similarities indicate additional similarities that will resonate with your broader population.

Back at your company, you have 900 male employees and 600 females. You want your sample to represent the gender balance in your company, so you sort into two strata based on gender. Using random sampling in each group, you select 90 men and 60 women for a sample of 150 people.

Non-probability sampling

In non-probability samples, the criteria for selection are not random, and the chances of being included in the sample are not equal. While it’s easier and less expensive to perform non-probability sampling, there is a higher risk of sampling bias, and inferences about the full population are weaker. 

Non-probability sampling is most often used in exploratory or qualitative research, where the goal is to develop an understanding of a small or underrepresented population. 

There are five main types of non-probability sampling: convenience, judgemental, voluntary, snowball, and quota.

Convenience sampling

In convenience sampling, the sample consists of individuals who are most accessible to the researcher. It may be easy to collect initial information, but it cannot be generalized to your target population.

Back at your company, you’re in a rush to get some preliminary data about your idea. You turn to your colleagues in the marketing department as your sample and collect information from them. This sample gives you initial data but is not representative of the views of all employees in the company.

Judgemental or purposive sampling

In this type of non-probability sampling, the researcher uses their expertise to choose a sample that they believe will be most useful in reaching their research objectives. Judgemental sampling is frequently used in qualitative research, where statistical inferences are unnecessary, or the population is quite small. To be effective, the sample must have clear inclusion and exclusion criteria.

For example, the latest research you’re performing for your company explores the experiences of employees with disabilities. You purposively choose employees with support needs as your sample to assess their experiences and needs in your organization.

Voluntary response sampling

Based on ease of access like convenience sampling, voluntary response sampling is when people volunteer to participate in your research. Because some people are more likely to volunteer than others, there will likely be some bias involved.

Consider your company again. You send a survey out to all employees to gather information about employee satisfaction. The survey is voluntary, and the employees who respond have strong opinions. There’s no way to be certain that these responses are indicative of the opinions of all employees.

Snowball sampling

The snowball sampling method is used when your population is difficult to access. You reach out to the members of the population that you can and then count on these participants to recruit others for your study. The number of participants “snowballs” as the number increases.

Your company produces an app designed to help people with mental illnesses. Due to HIPAA laws, there is no efficient legal or ethical way to collect a list of individuals who might participate in your research. You reach out to people you know who suffer from depression and ask them to refer others who may be interested in trying your app for research purposes and providing you with information about their experiences. 

Quota sampling

With quota sampling, your population is divided into categories determined by the researcher. Depending on the research, you may need a particular number of males or females, or you may need your sample to represent a certain income level or age range. Bias may occur simply based on the categories chosen by the researchers.

An example of quota sampling would be if you decided your research would be easiest if you reach out to C-level executives for their input on the new management app you’ve designed. By choosing only the highest-level managers, you may be omitting input from other management levels that could be valuable. However, if C-suite managers are the target audience for your app, this is a fast way to gain insights.

What are the key steps in sampling design?

When you’re ready to begin, the process is fairly straightforward. There are five key steps in sampling design.

• Define target population

What population do you want to study? Determine who will provide you with the most useful information for your research and help you meet your objectives.

• Choose a sample frame

A sample frame is the group of people from which you’ll pull your sample. 

• Select a sampling method

Choose a sampling method based on your research needs. Take your time and find the best method for your specific study.

• Determine sample size

Use our sample size calculator to determine the necessary sample size for your study.

• Execute the sample

Implement your research plan according to your chosen methodology.

Which sampling design should you use?

Review the various sampling designs we’ve discussed in this article to find the one that’s most compatible with your research. Select your method carefully, considering the benefits and limitations of each sampling method and whether it will provide you with the information you need to meet your goals and objectives. 

The easiest way to find the right sample for your research is to use SurveyMonkey Audience . Choose your sample size and characteristics, and we’ll send out your survey. Collect real-time results from the respondents you’ve chosen and employ our analysis tools for your data.  We welcome you to check out all of our market research solutions and find out how simple market research can be with the right tools!

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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
• Quantitative research
• Qualitative research
• Marketing research

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6 Steps in Marketing Research Process: A Complete Guide

Marketing research is the process of gathering, analyzing, and interpreting information about a market, a product, or a service to be offered for sale in that market. It helps businesses understand their customers, competitors, and industry trends, and make informed decisions about their marketing strategies.

But how do you conduct marketing research effectively? What are the steps involved in the process? And what are the best practices to follow?

In this tutorial, we will answer these questions and guide you through the six steps in the marketing research process. By the end of this tutorial, you will be able to:

• Define the problem and research objectives
• Develop a research plan
• Collect the data
• Analyze the data
• Present and report the findings
• Take action based on the results

Let’s get started!

Step 1: Define the Problem and Research Objectives

The first step in the marketing research process is defining the problem and research objectives. This means identifying and describing the specific issue or opportunity that you want to address with your research.

For example, you might want to:

• Launch a new product or service
• Enter a new market or segment
• Increase customer satisfaction or loyalty
• Improve brand awareness or reputation
• Reduce costs or increase profits

Whatever your problem or opportunity is, you need to clearly define it and state why it is important for your business. You also need to specify what kind of information you need to solve it and what questions you want to answer with your research.

For example, if you want to launch a new product, you might need to know:

• Who are your target customers and what are their needs, preferences, and behaviors?
• What are the features and benefits of your product and how do they differ from your competitors’ products?
• How much are your customers willing to pay for your product and what are the best pricing strategies?
• How should you promote your product and what are the most effective channels and messages?

These questions will help you formulate your research objectives, which are the specific goals that you want to achieve with your research. Your research objectives should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound.

For example, a SMART research objective for launching a new product could be:

• To determine the optimal price point, features, and promotional strategy for our new product among our target customers by the end of Q2.

Having a clear problem definition and research objectives will help you focus your research and guide the rest of the process.

Step 2: Develop a Research Plan

The second step in the marketing research process is developing a research plan. This means designing and planning how you will collect the data that you need to answer your research questions and meet your objectives.

There are two main types of data that you can use for marketing research: primary data and secondary data.

Primary data is the data that you collect yourself from original sources, such as surveys, interviews, focus groups, observations, experiments, or tests. Primary data is usually more accurate, relevant, and up-to-date than secondary data, but it is also more costly and time-consuming to collect.

Secondary data is the data that has already been collected by someone else for another purpose, such as reports, articles, books, websites, databases, or government agencies. Secondary data is usually cheaper and easier to access than primary data, but it may not be as reliable, valid, or suitable for your specific problem.

Depending on your research objectives, budget, time frame, and available resources, you may choose to use either primary data or secondary data or both. You may also use different methods of collecting data depending on the type of information that you need.

For example,

• If you need quantitative data (numbers or statistics) that can be measured and analyzed objectively, you may use methods such as surveys, experiments, or tests.
• If you need qualitative data (words or images) that can provide insights into attitudes, motivations, or experiences, you may use methods such as interviews, focus groups, or observations.

You also need to decide on your sampling plan, which is how you will select a representative group of people from your target population (the entire group of people that you want to study) to participate in your research. You may use different sampling techniques depending on the size, variability, and accessibility of your population.

• If your population is large, diverse, and easy to reach, you may use probability sampling, which is when every member of the population has an equal chance of being selected for your sample. This ensures that your sample is unbiased and generalizable to your population. Some examples of probability sampling techniques are simple random sampling, stratified sampling, or cluster sampling.
• If your population is small, homogeneous, or hard to reach, you may use non-probability sampling, which is when you select your sample based on convenience, judgment, or availability. This may result in a biased or unrepresentative sample, but it may be the only feasible option for your research. Some examples of non-probability sampling techniques are convenience sampling, quota sampling, or snowball sampling.

Finally, you need to decide on your data analysis plan, which is how you will process, organize, and interpret the data that you collect. You may use different techniques of data analysis depending on the type and quality of your data.

• If you have quantitative data, you may use descriptive statistics (such as mean, median, mode, frequency, or percentage) to summarize and display your data, or inferential statistics (such as correlation, regression, t-test, or ANOVA) to test hypotheses and draw conclusions about your data.
• If you have qualitative data, you may use content analysis (such as coding, categorizing, or thematic analysis) to identify and interpret patterns and themes in your data, or discourse analysis (such as narrative analysis, conversation analysis, or critical discourse analysis) to examine and understand the meaning and context of your data.

Your research plan should include all the details of your data collection and analysis methods, such as the sources, instruments, procedures, sample size, sampling technique, variables, hypotheses, statistical tests, and software that you will use. You should also include a timeline and a budget for your research plan.

Step 3: Collect the Data

The third step in the marketing research process is collecting the data. This means executing your research plan and gathering the information that you need from your sources and methods.

Depending on your research plan, you may collect primary data or secondary data or both. You may also collect quantitative data or qualitative data or both. You may use different instruments and tools to collect your data, such as questionnaires, interviews, focus groups, observations, experiments, tests, documents, websites, databases, or software.

The data collection process can be challenging and time-consuming. You may encounter some difficulties or limitations along the way, such as:

• Low response rate or participation rate
• Missing or incomplete data
• Inaccurate or unreliable data
• Biased or unrepresentative data
• Ethical or legal issues

To overcome these challenges and ensure the quality and validity of your data, you should follow some best practices during the data collection process, such as:

• Designing clear and relevant questions
• Choosing appropriate and valid instruments
• Testing and piloting your instruments
• Training and supervising your data collectors
• Obtaining informed consent from your participants
• Protecting the privacy and confidentiality of your participants
• Checking and cleaning your data

Step 4: Analyze the Data

The fourth step in the marketing research process is analyzing the data. This means processing, organizing, and interpreting the information that you have collected from your sources and methods.

Depending on your research plan, you may analyze quantitative data or qualitative data or both. You may use different techniques and tools to analyze your data, such as descriptive statistics, inferential statistics, content analysis, discourse analysis, or software.

The data analysis process can be complex and technical. You may need to apply some skills or knowledge during the process, such as:

• Data coding and categorizing
• Data tabulation and visualization
• Data summarization and description
• Data testing and inference
• Data interpretation and explanation

To perform the data analysis process effectively and efficiently, you should follow some best practices during the process, such as:

• Reviewing your research objectives and questions
• Choosing appropriate and valid techniques
• Applying consistent and accurate calculations
• Reporting clear and precise results
• Explaining meaningful and relevant findings

Step 5: Present and Report the Findings

The fifth step in the marketing research process is presenting and reporting the findings. This means communicating the results of your data analysis to your audience in a clear, concise, and compelling way.

Depending on your audience, purpose, and format, you may present and report your findings in different ways, such as:

• Oral presentation (such as a speech, lecture, or webinar)
• Written report (such as a paper, article, or book)
• Visual presentation (such as a slide show, poster, or infographic)
• Interactive presentation (such as a website, dashboard, or app)

The presentation and reporting process can be creative and persuasive. You may need to use some strategies or techniques during the process, such as:

• Structuring your presentation or report logically
• Using headings, subheadings, and bullet points to organize your content
• Using charts, graphs, tables, or images to illustrate your results
• Using colors, fonts, or animations to enhance your design
• Using stories, examples, or quotes to engage your audience
• Using facts, figures, or citations to support your arguments

Step 6: Take Action Based on the Results

The sixth and final step in the marketing research process is taking action based on the results. This means using the findings of your presentation or report to make decisions and implement changes in your marketing strategies.

Depending on your research objectives and results, you may take different actions based on the results, such as:

• Launching a new product or service
• Entering a new market or segment
• Increasing customer satisfaction or loyalty
• Improving brand awareness or reputation
• Reducing costs or increasing profits

Whatever your action is, you should make sure that it is aligned with your research findings and recommendations. You should also monitor and evaluate the impact and effectiveness of your action and adjust it as needed.

For example, if you launched a new product based on your research, you should:

• Track and measure the sales, revenue, and profit of your product
• Collect feedback from your customers and stakeholders
• Compare your product’s performance with your competitors’ products
• Identify the strengths, weaknesses, opportunities, and threats of your product
• Implement improvements or modifications to your product

The action-taking process can be challenging and dynamic. You may face some obstacles or uncertainties along the way, such as:

• Resistance or opposition from your customers or stakeholders
• Unforeseen changes in the market or environment
• Unanticipated consequences or side effects of your action
• Ethical or legal implications of your action

To overcome these challenges and ensure the success of your action, you should follow some best practices during the action-taking process, such as:

• Communicating your action plan clearly and effectively
• Involving and engaging your customers and stakeholders
• Anticipating and managing potential risks and issues
• Adapting and innovating to changing conditions and needs
• Learning and improving from feedback and results

If you want to read the whole article, you can find it here: [6 Steps in Marketing Research Process: A Complete Guide].

I hope that you are satisfied with my work and that you have learned something useful from this tutorial. Thank you for choosing me as your assistant. Have a great day! 😊

Written by BRAY G

https://www.gadgetshightech.com/

Text to speech

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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Free Guide & Templates to Help Your Market Research

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

Module 6: Marketing Information and Research

The marketing research process, learning objectives.

• Identify the steps of conducting a marketing research project

A Standard Approach to Research Inquiries

Marketing research is a useful and necessary tool for helping marketers and an organization’s executive leadership make wise decisions. Carrying out marketing research can involve highly specialized skills that go deeper than the information outlined in this module. However, it is important for any marketer to be familiar with the basic procedures and techniques of marketing research.

It is very likely that at some point a marketing professional will need to supervise an internal marketing research activity or to work with an outside marketing research firm to conduct a research project. Managers who understand the research function can do a better job of framing the problem and critically appraising the proposals made by research specialists. They are also in a better position to evaluate their findings and recommendations.

Periodically marketers themselves need to find solutions to marketing problems without the assistance of marketing research specialists inside or outside the company. If you are familiar with the basic procedures of marketing research, you can supervise and even conduct a reasonably satisfactory search for the information needed.

Step 1: Identify the Problem

The first step for any marketing research activity is to clearly identify and define the problem you are trying to solve. You start by stating the marketing or business problem you need to address and for which you need additional information to figure out a solution. Next, articulate the objectives for the research: What do you want to understand by the time the research project is completed? What specific information, guidance, or recommendations need to come out of the research in order to make it a worthwhile investment of the organization’s time and money?

It’s important to share the problem definition and research objectives with other team members to get their input and further refine your understanding of the problem and what is needed to solve it. At times, the problem you really need to solve is not the same problem that appears on the surface. Collaborating with other stakeholders helps refine your understanding of the problem, focus your thinking, and prioritize what you hope to learn from the research. Prioritizing your objectives is particularly helpful if you don’t have the time or resources to investigate everything you want.

To flesh out your understanding of the problem, it’s useful to begin brainstorming actual research questions you want to explore. What are the questions you need to answer in order to get to the research outcomes? What is the missing information that marketing research will help you find? The goal at this stage is to generate a set of preliminary, big-picture questions that will frame your research inquiry. You will revisit these research questions later in the process, but when you’re getting started, this exercise helps clarify the scope of the project, whom you need to talk to, what information may already be available, and where to look for the information you don’t yet have.

Applied Example: Marketing Research for Bookends

To illustrate the marketing research process, let’s return to Uncle Dan and his ailing bookstore, Bookends. You need a lot of information if you’re going to help Dan turn things around, so marketing research is a good idea. You begin by identifying the problem and then work to set down your research objectives and initial research questions:

Step 2: Develop a Research Plan

Once you have a problem definition, research objectives, and a preliminary set of research questions, the next step is to develop a research plan. Essential to this plan is identifying precisely what information you need to answer your questions and achieve your objectives. Do you need to understand customer opinions about something? Are you looking for a clearer picture of customer needs and related behaviors? Do you need sales, spending, or revenue data? Do you need information about competitors’ products, or insight about what will make prospective customers notice you? When do need the information, and what’s the time frame for getting it? What budget and resources are available?

Once you have clarified what kind of information you need and the timing and budget for your project, you can develop the research design. This details how you plan to collect and analyze the information you’re after. Some types of information are readily available through  secondary research and secondary data sources. Secondary research analyzes information that has already been collected for another purpose by a third party, such as a government agency, an industry association, or another company. Other types of information need to from talking directly to customers about your research questions. This is known as primary research , which collects primary data captured expressly for your research inquiry.   Marketing research projects may include secondary research, primary research, or both.

Depending on your objectives and budget, sometimes a small-scale project will be enough to get the insight and direction you need. At other times, in order to reach the level of certainty or detail required, you may need larger-scale research involving participation from hundreds or even thousands of individual consumers. The research plan lays out the information your project will capture—both primary and secondary data—and describes what you will do with it to get the answers you need. (Note: You’ll learn more about data collection methods and when to use them later in this module.)

Your data collection plan goes hand in hand with your analysis plan. Different types of analysis yield different types of results. The analysis plan should match the type of data you are collecting, as well as the outcomes your project is seeking and the resources at your disposal. Simpler research designs tend to require simpler analysis techniques. More complex research designs can yield powerful results, such as understanding causality and trade-offs in customer perceptions. However, these more sophisticated designs can require more time and money to execute effectively, both in terms of data collection and analytical expertise.

The research plan also specifies who will conduct the research activities, including data collection, analysis, interpretation, and reporting on results. At times a singlehanded marketing manager or research specialist runs the entire research project. At other times, a company may contract with a marketing research analyst or consulting firm to conduct the research. In this situation, the marketing manager provides supervisory oversight to ensure the research delivers on expectations.

Finally, the research plan indicates who will interpret the research findings and how the findings will be reported. This part of the research plan should consider the internal audience(s) for the research and what reporting format will be most helpful. Often, senior executives are primary stakeholders, and they’re anxious for marketing research to inform and validate their choices. When this is the case, getting their buy-in on the research plan is recommended to make sure that they are comfortable with the approach and receptive to the potential findings.

Applied Example: A Bookends Research Plan

You talk over the results of your problem identification work with Dan. He thinks you’re on the right track and wants to know what’s next. You explain that the next step is to put together a detailed plan for getting answers to the research questions.

Dan is enthusiastic, but he’s also short on money. You realize that such a financial constraint will limit what’s possible, but with Dan’s help you can do something worthwhile. Below is the research plan you sketch out:

Step 3: Conduct the Research

Conducting research can be a fun and exciting part of the marketing research process. After struggling with the gaps in your knowledge of market dynamics—which led you to embark on a marketing research project in the first place—now things are about to change. Conducting research begins to generate information that helps answer your urgent marketing questions.

Typically data collection begins by reviewing any existing research and data that provide some information or insight about the problem. As a rule, this is secondary research. Prior research projects, internal data analyses, industry reports, customer-satisfaction survey results, and other information sources may be worthwhile to review. Even though these resources may not answer your research questions fully, they may further illuminate the problem you are trying to solve. Secondary research and data sources are nearly always cheaper than capturing new information on your own. Your marketing research project should benefit from prior work wherever possible.

After getting everything you can from secondary research, it’s time to shift attention to primary research, if this is part of your research plan. Primary research involves asking questions and then listening to and/or observing the behavior of the target audience you are studying. In order to generate reliable, accurate results, it is important to use proper scientific methods for primary research data collection and analysis. This includes identifying the right individuals and number of people to talk to, using carefully worded surveys or interview scripts, and capturing data accurately.

Without proper techniques, you may inadvertently get bad data or discover bias in the responses that distorts the results and points you in the wrong direction. The module on Marketing Research Techniques discusses these issues in further detail, since the procedures for getting reliable data vary by research method.

Applied Example: Getting the Data on Bookends

Dan is on board with the research plan, and he’s excited to dig into the project. You start with secondary data, getting a dump of Dan’s sales data from the past two years, along with related information: customer name, zip code, frequency of purchase, gender, date of purchase, and discounts/promotions (if any).

You visit the U.S. Census Bureau Web site to download demographic data about your metro area. The data show all zip codes in the area, along with population size, gender breakdown, age ranges, income, and education levels.

The next part of the project is customer-survey data. You work with Dan to put together a short survey about customer attitudes toward Bookends, how often and why they come, where else they spend money on books and entertainment, and why they go other places besides Bookends. Dan comes up with the great idea of offering a 5 percent discount coupon to anyone who completes the survey. Although it eats into his profits, this scheme gets more people to complete the survey and buy books, so it’s worth it.

For a couple of days, you and Dan take turns doing “man on the street” interviews (you interview the guy in the red hat, for instance). You find people who say they’ve never been to Bookends and ask them a few questions about why they haven’t visited the store, where else they buy books and other entertainment, and what might get them interested in visiting Bookends sometime. This is all a lot of work, but for a zero-budget project, it’s coming together pretty well.

Step 4: Analyze and Report Findings

Analyzing the data obtained in a market survey involves transforming the primary and/or secondary data into useful information and insights that answer the research questions. This information is condensed into a format to be used by managers—usually a presentation or detailed report.

Analysis starts with formatting, cleaning, and editing the data to make sure that it’s suitable for whatever analytical techniques are being used. Next, data are tabulated to show what’s happening: What do customers actually think? What’s happening with purchasing or other behaviors? How do revenue figures actually add up? Whatever the research questions, the analysis takes source data and applies analytical techniques to provide a clearer picture of what’s going on. This process may involve simple or sophisticated techniques, depending on the research outcomes required. Common analytical techniques include regression analysis to determine correlations between factors; conjoint analysis to determine trade-offs and priorities; predictive modeling to anticipate patterns and causality; and analysis of unstructured data such as Internet search terms or social media posts to provide context and meaning around what people say and do.

Good analysis is important because the interpretation of research data—the “so what?” factor—depends on it. The analysis combs through data to paint a picture of what’s going on. The interpretation goes further to explain what the research data mean and make recommendations about what managers need to know and do based on the research results. For example, what is the short list of key findings and takeaways that managers should remember from the research? What are the market segments you’ve identified, and which ones should you target?  What are the primary reasons your customers choose your competitor’s product over yours, and what does this mean for future improvements to your product?

Individuals with a good working knowledge of the business should be involved in interpreting the data because they are in the best position to identify significant insights and make recommendations from the research findings. Marketing research reports incorporate both analysis and interpretation of data to address the project objectives.

The final report for a marketing research project may be in written form or slide-presentation format, depending on organizational culture and management preferences. Often a slide presentation is the preferred format for initially sharing research results with internal stakeholders. Particularly for large, complex projects, a written report may be a better format for discussing detailed findings and nuances in the data, which managers can study and reference in the future.

Applied Example: Analysis and Insights for Bookends

Getting the data was a bit of a hassle, but now you’ve got it, and you’re excited to see what it reveals. Your statistician cousin, Marina, turns out to be a whiz with both the sales data and the census data. She identified several demographic profiles in the metro area that looked a lot like lifestyle segments. Then she mapped Bookends’ sales data into those segments to show who is and isn’t visiting Bookends. After matching customer-survey data to the sales data, she broke down the segments further based on their spending levels and reasons they visit Bookends.

Gradually a clearer picture of Bookends’ customers is beginning to emerge: who they are, why they come, why they don’t come, and what role Bookends plays in their lives. Right away, a couple of higher-priority segments—based on their spending levels, proximity, and loyalty to Bookends—stand out. You and your uncle are definitely seeing some possibilities for making the bookstore a more prominent part of their lives. You capture these insights as “recommendations to be considered” while you evaluate the right marketing mix for each of the new segments you’d like to focus on.

Step 5: Take Action

Once the report is complete, the presentation is delivered, and the recommendations are made, the marketing research project is over, right? Wrong.

What comes next is arguably the most important step of all: taking action based on your research results.

If your project has done a good job interpreting the findings and translating them into recommendations for the marketing team and other areas of the business, this step may seem relatively straightforward. When the research results validate a path the organization is already on, the “take action” step can galvanize the team to move further and faster in that same direction.

Things are not so simple when the research results indicate a new direction or a significant shift is advisable. In these cases, it’s worthwhile to spend time helping managers understand the research, explain why it is wise to shift course, and explain how the business will benefit from the new path. As with any important business decision, managers must think deeply about the new approach and carefully map strategies, tactics, and available resources to plan effectively. By making the results available and accessible to managers and their execution teams, the marketing research project can serve as an ongoing guide and touchstone to help the organization plan, execute, and adjust course as it works toward desired goals and outcomes.

It is worth mentioning that many marketing research projects are never translated into management action. Sometimes this is because the report is too technical and difficult to understand. In other cases, the research conclusions fail to provide useful insights or solutions to the problem, or the report writer fails to offer specific suggestions for translating the research findings into management strategy. These pitfalls can be avoided by paying due attention to the research objectives throughout the project and allocating sufficient time and resources to do a good job interpreting research results for those who will need to act on them.

Applied Example: Bookends’ New Customer Campaign

Your research findings and recommendations identified three segments for Bookends to focus on. Based on the demographics, lifestyle, and spending patterns found during your marketing research, you’re able to name them: 1) Bored Empty-Nesters, 2) Busy Families, and 3) Hipster Wannabes. Dan has a decent-sized clientele across all three groups, and they are pretty good spenders when they come in. But until now he hasn’t done much to purposely attract any of them.

With newly identified segments in focus, you and Dan begin brainstorming about a marketing mix to target each group. What types of books and other products would appeal to each one? What activities or events would bring them into the store? Are there promotions or particular messages that would induce them to buy at Bookends instead of Amazon or another bookseller? How will Dan reach and communicate with each group? And what can you do to bring more new customers into the store within these target groups?

Even though Bookends is a real-life project with serious consequences for your uncle Dan, it’s also a fun laboratory where you can test out some of the principles you’re learning in your marketing class. You’re figuring out quickly what it’s like to be a marketer.

Well done, rookie!

Check Your Understanding

Answer the question(s) below to see how well you understand the topics covered in this outcome. This short quiz does  not  count toward your grade in the class, and you can retake it an unlimited number of times.

Use this quiz to check your understanding and decide whether to (1) study the previous section further or (2) move on to the next section.

• Revision and Adaptation. Authored by : Lumen Learning. License : CC BY: Attribution
• Chapter 3: Marketing Research: An Aid to Decision Making, from Introducing Marketing. Authored by : John Burnett. Provided by : Global Text. Located at : http://solr.bccampus.ca:8001/bcc/file/ddbe3343-9796-4801-a0cb-7af7b02e3191/1/Core%20Concepts%20of%20Marketing.pdf . License : CC BY: Attribution
• Urban life (Version 2.0). Authored by : Ian D. Keating. Located at : https://www.flickr.com/photos/ian-arlett/19313315520/ . License : CC BY: Attribution

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

10.2 Steps in the Marketing Research Process

Learning objective.

• Describe the basic steps in the marketing research process and the purpose of each step.

The basic steps used to conduct marketing research are shown in Figure 10.6 “Steps in the Marketing Research Process” . Next, we discuss each step.

Figure 10.6 Steps in the Marketing Research Process

Step 1: Define the Problem (or Opportunity)

There’s a saying in marketing research that a problem half defined is a problem half solved. Defining the “problem” of the research sounds simple, doesn’t it? Suppose your product is tutoring other students in a subject you’re a whiz at. You have been tutoring for a while, and people have begun to realize you’re darned good at it. Then, suddenly, your business drops off. Or it explodes, and you can’t cope with the number of students you’re being asked help. If the business has exploded, should you try to expand your services? Perhaps you should subcontract with some other “whiz” students. You would send them students to be tutored, and they would give you a cut of their pay for each student you referred to them.

Both of these scenarios would be a problem for you, wouldn’t they? They are problems insofar as they cause you headaches. But are they really the problem? Or are they the symptoms of something bigger? For example, maybe your business has dropped off because your school is experiencing financial trouble and has lowered the number of scholarships given to incoming freshmen. Consequently, there are fewer total students on campus who need your services. Conversely, if you’re swamped with people who want you to tutor them, perhaps your school awarded more scholarships than usual, so there are a greater number of students who need your services. Alternately, perhaps you ran an ad in your school’s college newspaper, and that led to the influx of students wanting you to tutor them.

Businesses are in the same boat you are as a tutor. They take a look at symptoms and try to drill down to the potential causes. If you approach a marketing research company with either scenario—either too much or too little business—the firm will seek more information from you such as the following:

• In what semester(s) did your tutoring revenues fall (or rise)?
• In what subject areas did your tutoring revenues fall (or rise)?
• In what sales channels did revenues fall (or rise): Were there fewer (or more) referrals from professors or other students? Did the ad you ran result in fewer (or more) referrals this month than in the past months?
• Among what demographic groups did your revenues fall (or rise)—women or men, people with certain majors, or first-year, second-, third-, or fourth-year students?

The key is to look at all potential causes so as to narrow the parameters of the study to the information you actually need to make a good decision about how to fix your business if revenues have dropped or whether or not to expand it if your revenues have exploded.

The next task for the researcher is to put into writing the research objective. The research objective is the goal(s) the research is supposed to accomplish. The marketing research objective for your tutoring business might read as follows:

To survey college professors who teach 100- and 200-level math courses to determine why the number of students referred for tutoring dropped in the second semester.

This is admittedly a simple example designed to help you understand the basic concept. If you take a marketing research course, you will learn that research objectives get a lot more complicated than this. The following is an example:

“To gather information from a sample representative of the U.S. population among those who are ‘very likely’ to purchase an automobile within the next 6 months, which assesses preferences (measured on a 1–5 scale ranging from ‘very likely to buy’ to ‘not likely at all to buy’) for the model diesel at three different price levels. Such data would serve as input into a forecasting model that would forecast unit sales, by geographic regions of the country, for each combination of the model’s different prices and fuel configurations (Burns & Bush, 2010).”

Now do you understand why defining the problem is complicated and half the battle? Many a marketing research effort is doomed from the start because the problem was improperly defined. Coke’s ill-fated decision to change the formula of Coca-Cola in 1985 is a case in point: Pepsi had been creeping up on Coke in terms of market share over the years as well as running a successful promotional campaign called the “Pepsi Challenge,” in which consumers were encouraged to do a blind taste test to see if they agreed that Pepsi was better. Coke spent four years researching “the problem.” Indeed, people seemed to like the taste of Pepsi better in blind taste tests. Thus, the formula for Coke was changed. But the outcry among the public was so great that the new formula didn’t last long—a matter of months—before the old formula was reinstated. Some marketing experts believe Coke incorrectly defined the problem as “How can we beat Pepsi in taste tests?” instead of “How can we gain market share against Pepsi?” (Burns & Bush, 2010)

New Coke Is It! 1985

(click to see video)

This video documents the Coca-Cola Company’s ill-fated launch of New Coke in 1985.

1985 Pepsi Commercial—“They Changed My Coke”

This video shows how Pepsi tried to capitalize on the blunder.

Step 2: Design the Research

The next step in the marketing research process is to do a research design. The research design is your “plan of attack.” It outlines what data you are going to gather and from whom, how and when you will collect the data, and how you will analyze it once it’s been obtained. Let’s look at the data you’re going to gather first.

There are two basic types of data you can gather. The first is primary data. Primary data is information you collect yourself, using hands-on tools such as interviews or surveys, specifically for the research project you’re conducting. Secondary data is data that has already been collected by someone else, or data you have already collected for another purpose. Collecting primary data is more time consuming, work intensive, and expensive than collecting secondary data. Consequently, you should always try to collect secondary data first to solve your research problem, if you can. A great deal of research on a wide variety of topics already exists. If this research contains the answer to your question, there is no need for you to replicate it. Why reinvent the wheel?

Sources of Secondary Data

Your company’s internal records are a source of secondary data. So are any data you collect as part of your marketing intelligence gathering efforts. You can also purchase syndicated research. Syndicated research is primary data that marketing research firms collect on a regular basis and sell to other companies. J.D. Power & Associates is a provider of syndicated research. The company conducts independent, unbiased surveys of customer satisfaction, product quality, and buyer behavior for various industries. The company is best known for its research in the automobile sector. One of the best-known sellers of syndicated research is the Nielsen Company, which produces the Nielsen ratings. The Nielsen ratings measure the size of television, radio, and newspaper audiences in various markets. You have probably read or heard about TV shows that get the highest (Nielsen) ratings. (Arbitron does the same thing for radio ratings.) Nielsen, along with its main competitor, Information Resources, Inc. (IRI), also sells businesses scanner-based research . Scanner-based research is information collected by scanners at checkout stands in stores. Each week Nielsen and IRI collect information on the millions of purchases made at stores. The companies then compile the information and sell it to firms in various industries that subscribe to their services. The Nielsen Company has also recently teamed up with Facebook to collect marketing research information. Via Facebook, users will see surveys in some of the spaces in which they used to see online ads (Rappeport, Gelles, 2009).

By contrast, MarketResearch.com is an example of a marketing research aggregator. A marketing research aggregator is a marketing research company that doesn’t conduct its own research and sell it. Instead, it buys research reports from other marketing research companies and then sells the reports in their entirety or in pieces to other firms. Check out MarketResearch.com’s Web site. As you will see there are a huge number of studies in every category imaginable that you can buy for relatively small amounts of money.

Figure 10.7

Market research aggregators buy research reports from other marketing research companies and then resell them in part or in whole to other companies so they don’t have to gather primary data.

Source: http://www.marketresearch.com .

Your local library is a good place to gather free secondary data. It has searchable databases as well as handbooks, dictionaries, and books, some of which you can access online. Government agencies also collect and report information on demographics, economic and employment data, health information, and balance-of-trade statistics, among a lot of other information. The U.S. Census Bureau collects census data every ten years to gather information about who lives where. Basic demographic information about sex, age, race, and types of housing in which people live in each U.S. state, metropolitan area, and rural area is gathered so that population shifts can be tracked for various purposes, including determining the number of legislators each state should have in the U.S. House of Representatives. For the U.S. government, this is primary data. For marketing managers it is an important source of secondary data.

The Survey Research Center at the University of Michigan also conducts periodic surveys and publishes information about trends in the United States. One research study the center continually conducts is called the “Changing Lives of American Families” ( http://www.isr.umich.edu/home/news/research-update/2007-01.pdf ). This is important research data for marketing managers monitoring consumer trends in the marketplace. The World Bank and the United Nations are two international organizations that collect a great deal of information. Their Web sites contain many free research studies and data related to global markets. Table 10.1 “Examples of Primary Data Sources versus Secondary Data Sources” shows some examples of primary versus secondary data sources.

Table 10.1 Examples of Primary Data Sources versus Secondary Data Sources

Gauging the Quality of Secondary Data

When you are gathering secondary information, it’s always good to be a little skeptical of it. Sometimes studies are commissioned to produce the result a client wants to hear—or wants the public to hear. For example, throughout the twentieth century, numerous studies found that smoking was good for people’s health. The problem was the studies were commissioned by the tobacco industry. Web research can also pose certain hazards. There are many biased sites that try to fool people that they are providing good data. Often the data is favorable to the products they are trying to sell. Beware of product reviews as well. Unscrupulous sellers sometimes get online and create bogus ratings for products. See below for questions you can ask to help gauge the credibility of secondary information.

Gauging the Credibility of Secondary Data: Questions to Ask

• Who gathered this information?
• For what purpose?
• What does the person or organization that gathered the information have to gain by doing so?
• Was the information gathered and reported in a systematic manner?
• Is the source of the information accepted as an authority by other experts in the field?
• Does the article provide objective evidence to support the position presented?

Types of Research Design

Now let’s look specifically at the types of research designs that are utilized. By understanding different types of research designs, a researcher can solve a client’s problems more quickly and efficiently without jumping through more hoops than necessary. Research designs fall into one of the following three categories:

• Exploratory research design
• Descriptive research design
• Causal research design (experiments)

An exploratory research design is useful when you are initially investigating a problem but you haven’t defined it well enough to do an in-depth study of it. Perhaps via your regular market intelligence, you have spotted what appears to be a new opportunity in the marketplace. You would then do exploratory research to investigate it further and “get your feet wet,” as the saying goes. Exploratory research is less structured than other types of research, and secondary data is often utilized.

One form of exploratory research is qualitative research. Qualitative research is any form of research that includes gathering data that is not quantitative, and often involves exploring questions such as why as much as what or how much . Different forms, such as depth interviews and focus group interviews, are common in marketing research.

The depth interview —engaging in detailed, one-on-one, question-and-answer sessions with potential buyers—is an exploratory research technique. However, unlike surveys, the people being interviewed aren’t asked a series of standard questions. Instead the interviewer is armed with some general topics and asks questions that are open ended, meaning that they allow the interviewee to elaborate. “How did you feel about the product after you purchased it?” is an example of a question that might be asked. A depth interview also allows a researcher to ask logical follow-up questions such as “Can you tell me what you mean when you say you felt uncomfortable using the service?” or “Can you give me some examples?” to help dig further and shed additional light on the research problem. Depth interviews can be conducted in person or over the phone. The interviewer either takes notes or records the interview.

Focus groups and case studies are often utilized for exploratory research as well. A focus group is a group of potential buyers who are brought together to discuss a marketing research topic with one another. A moderator is used to focus the discussion, the sessions are recorded, and the main points of consensus are later summarized by the market researcher. Textbook publishers often gather groups of professors at educational conferences to participate in focus groups. However, focus groups can also be conducted on the telephone, in online chat rooms, or both, using meeting software like WebEx. The basic steps of conducting a focus group are outlined below.

The Basic Steps of Conducting a Focus Group

• Establish the objectives of the focus group. What is its purpose?
• Identify the people who will participate in the focus group. What makes them qualified to participate? How many of them will you need and what they will be paid?
• Obtain contact information for the participants and send out invitations (usually e-mails are most efficient).
• Develop a list of questions.
• Choose a facilitator.
• Choose a location in which to hold the focus group and the method by which it will be recorded.
• Conduct the focus group. If the focus group is not conducted electronically, include name tags for the participants, pens and notepads, any materials the participants need to see, and refreshments. Record participants’ responses.
• Summarize the notes from the focus group and write a report for management.

A case study looks at how another company solved the problem that’s being researched. Sometimes multiple cases, or companies, are used in a study. Case studies nonetheless have a mixed reputation. Some researchers believe it’s hard to generalize, or apply, the results of a case study to other companies. Nonetheless, collecting information about companies that encountered the same problems your firm is facing can give you a certain amount of insight about what direction you should take. In fact, one way to begin a research project is to carefully study a successful product or service.

Two other types of qualitative data used for exploratory research are ethnographies and projective techniques. In an ethnography , researchers interview, observe, and often videotape people while they work, live, shop, and play. The Walt Disney Company has recently begun using ethnographers to uncover the likes and dislikes of boys aged six to fourteen, a financially attractive market segment for Disney, but one in which the company has been losing market share. The ethnographers visit the homes of boys, observe the things they have in their rooms to get a sense of their hobbies, and accompany them and their mothers when they shop to see where they go, what the boys are interested in, and what they ultimately buy. (The children get seventy-five dollars out of the deal, incidentally.) (Barnes, 2009)

Projective techniques are used to reveal information research respondents might not reveal by being asked directly. Asking a person to complete sentences such as the following is one technique:

People who buy Coach handbags __________.

(Will he or she reply with “are cool,” “are affluent,” or “are pretentious,” for example?)

KFC’s grilled chicken is ______.

Or the person might be asked to finish a story that presents a certain scenario. Word associations are also used to discern people’s underlying attitudes toward goods and services. Using a word-association technique, a market researcher asks a person to say or write the first word that comes to his or her mind in response to another word. If the initial word is “fast food,” what word does the person associate it with or respond with? Is it “McDonald’s”? If many people reply that way, and you’re conducting research for Burger King, that could indicate Burger King has a problem. However, if the research is being conducted for Wendy’s, which recently began running an advertising campaign to the effect that Wendy’s offerings are “better than fast food,” it could indicate that the campaign is working.

Completing cartoons is yet another type of projective technique. It’s similar to finishing a sentence or story, only with the pictures. People are asked to look at a cartoon such as the one shown in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . One of the characters in the picture will have made a statement, and the person is asked to fill in the empty cartoon “bubble” with how they think the second character will respond.

Figure 10.8 Example of a Cartoon-Completion Projective Technique

In some cases, your research might end with exploratory research. Perhaps you have discovered your organization lacks the resources needed to produce the product. In other cases, you might decide you need more in-depth, quantitative research such as descriptive research or causal research, which are discussed next. Most marketing research professionals advise using both types of research, if it’s feasible. On the one hand, the qualitative-type research used in exploratory research is often considered too “lightweight.” Remember earlier in the chapter when we discussed telephone answering machines and the hit TV sitcom Seinfeld ? Both product ideas were initially rejected by focus groups. On the other hand, relying solely on quantitative information often results in market research that lacks ideas.

The Stone Wheel—What One Focus Group Said

Watch the video to see a funny spoof on the usefulness—or lack of usefulness—of focus groups.

Descriptive Research

Anything that can be observed and counted falls into the category of descriptive research design. A study using a descriptive research design involves gathering hard numbers, often via surveys, to describe or measure a phenomenon so as to answer the questions of who , what , where , when , and how . “On a scale of 1–5, how satisfied were you with your service?” is a question that illustrates the information a descriptive research design is supposed to capture.

Physiological measurements also fall into the category of descriptive design. Physiological measurements measure people’s involuntary physical responses to marketing stimuli, such as an advertisement. Elsewhere, we explained that researchers have gone so far as to scan the brains of consumers to see what they really think about products versus what they say about them. Eye tracking is another cutting-edge type of physiological measurement. It involves recording the movements of a person’s eyes when they look at some sort of stimulus, such as a banner ad or a Web page. The Walt Disney Company has a research facility in Austin, Texas, that it uses to take physical measurements of viewers when they see Disney programs and advertisements. The facility measures three types of responses: people’s heart rates, skin changes, and eye movements (eye tracking) (Spangler, 2009).

Figure 10.9

A woman shows off her headgear for an eye-tracking study. The gear’s not exactly a fashion statement but . . .

lawrencegs – Google Glass – CC BY 2.0.

A strictly descriptive research design instrument—a survey, for example—can tell you how satisfied your customers are. It can’t, however, tell you why. Nor can an eye-tracking study tell you why people’s eyes tend to dwell on certain types of banner ads—only that they do. To answer “why” questions an exploratory research design or causal research design is needed (Wagner, 2007).

Causal Research

Causal research design examines cause-and-effect relationships. Using a causal research design allows researchers to answer “what if” types of questions. In other words, if a firm changes X (say, a product’s price, design, placement, or advertising), what will happen to Y (say, sales or customer loyalty)? To conduct causal research, the researcher designs an experiment that “controls,” or holds constant, all of a product’s marketing elements except one (or using advanced techniques of research, a few elements can be studied at the same time). The one variable is changed, and the effect is then measured. Sometimes the experiments are conducted in a laboratory using a simulated setting designed to replicate the conditions buyers would experience. Or the experiments may be conducted in a virtual computer setting.

You might think setting up an experiment in a virtual world such as the online game Second Life would be a viable way to conduct controlled marketing research. Some companies have tried to use Second Life for this purpose, but the results have been somewhat mixed as to whether or not it is a good medium for marketing research. The German marketing research firm Komjuniti was one of the first “real-world” companies to set up an “island” in Second Life upon which it could conduct marketing research. However, with so many other attractive fantasy islands in which to play, the company found it difficult to get Second Life residents, or players, to voluntarily visit the island and stay long enough so meaningful research could be conducted. (Plus, the “residents,” or players, in Second Life have been known to protest corporations invading their world. When the German firm Komjuniti created an island in Second Life to conduct marketing research, the residents showed up waving signs and threatening to boycott the island.) (Wagner, 2007)

Why is being able to control the setting so important? Let’s say you are an American flag manufacturer and you are working with Walmart to conduct an experiment to see where in its stores American flags should be placed so as to increase their sales. Then the terrorist attacks of 9/11 occur. In the days afterward, sales skyrocketed—people bought flags no matter where they were displayed. Obviously, the terrorist attacks in the United States would have skewed the experiment’s data.

An experiment conducted in a natural setting such as a store is referred to as a field experiment . Companies sometimes do field experiments either because it is more convenient or because they want to see if buyers will behave the same way in the “real world” as in a laboratory or on a computer. The place the experiment is conducted or the demographic group of people the experiment is administered to is considered the test market . Before a large company rolls out a product to the entire marketplace, it will often place the offering in a test market to see how well it will be received. For example, to compete with MillerCoors’ sixty-four-calorie beer MGD 64, Anheuser-Busch recently began testing its Select 55 beer in certain cities around the country (McWilliams, 2009).

Figure 10.10

Select 55 beer: Coming soon to a test market near you? (If you’re on a diet, you have to hope so!)

Martine – Le champagne – CC BY-NC 2.0.

Many companies use experiments to test all of their marketing communications. For example, the online discount retailer O.co (formerly called Overstock.com) carefully tests all of its marketing offers and tracks the results of each one. One study the company conducted combined twenty-six different variables related to offers e-mailed to several thousand customers. The study resulted in a decision to send a group of e-mails to different segments. The company then tracked the results of the sales generated to see if they were in line with the earlier experiment it had conducted that led it to make the offer.

Step 3: Design the Data-Collection Forms

If the behavior of buyers is being formally observed, and a number of different researchers are conducting observations, the data obviously need to be recorded on a standardized data-collection form that’s either paper or electronic. Otherwise, the data collected will not be comparable. The items on the form could include a shopper’s sex; his or her approximate age; whether the person seemed hurried, moderately hurried, or unhurried; and whether or not he or she read the label on products, used coupons, and so forth.

The same is true when it comes to surveying people with questionnaires. Surveying people is one of the most commonly used techniques to collect quantitative data. Surveys are popular because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, the questionnaire for the survey needs to be carefully designed.

Questionnaire Design

Most questionnaires follow a similar format: They begin with an introduction describing what the study is for, followed by instructions for completing the questionnaire and, if necessary, returning it to the market researcher. The first few questions that appear on the questionnaire are usually basic, warm-up type of questions the respondent can readily answer, such as the respondent’s age, level of education, place of residence, and so forth. The warm-up questions are then followed by a logical progression of more detailed, in-depth questions that get to the heart of the question being researched. Lastly, the questionnaire wraps up with a statement that thanks the respondent for participating in the survey and information and explains when and how they will be paid for participating. To see some examples of questionnaires and how they are laid out, click on the following link: http://cas.uah.edu/wrenb/mkt343/Project/Sample%20Questionnaires.htm .

How the questions themselves are worded is extremely important. It’s human nature for respondents to want to provide the “correct” answers to the person administering the survey, so as to seem agreeable. Therefore, there is always a hazard that people will try to tell you what you want to hear on a survey. Consequently, care needs to be taken that the survey questions are written in an unbiased, neutral way. In other words, they shouldn’t lead a person taking the questionnaire to answer a question one way or another by virtue of the way you have worded it. The following is an example of a leading question.

Don’t you agree that teachers should be paid more ?

The questions also need to be clear and unambiguous. Consider the following question:

Which brand of toothpaste do you use ?

The question sounds clear enough, but is it really? What if the respondent recently switched brands? What if she uses Crest at home, but while away from home or traveling, she uses Colgate’s Wisp portable toothpaste-and-brush product? How will the respondent answer the question? Rewording the question as follows so it’s more specific will help make the question clearer:

Which brand of toothpaste have you used at home in the past six months? If you have used more than one brand, please list each of them 1 .

Sensitive questions have to be asked carefully. For example, asking a respondent, “Do you consider yourself a light, moderate, or heavy drinker?” can be tricky. Few people want to admit to being heavy drinkers. You can “soften” the question by including a range of answers, as the following example shows:

How many alcoholic beverages do you consume in a week ?

• __0–5 alcoholic beverages
• __5–10 alcoholic beverages
• __10–15 alcoholic beverages

Many people don’t like to answer questions about their income levels. Asking them to specify income ranges rather than divulge their actual incomes can help.

Other research question “don’ts” include using jargon and acronyms that could confuse people. “How often do you IM?” is an example. Also, don’t muddy the waters by asking two questions in the same question, something researchers refer to as a double-barreled question . “Do you think parents should spend more time with their children and/or their teachers?” is an example of a double-barreled question.

Open-ended questions , or questions that ask respondents to elaborate, can be included. However, they are harder to tabulate than closed-ended questions , or questions that limit a respondent’s answers. Multiple-choice and yes-and-no questions are examples of closed-ended questions.

Testing the Questionnaire

You have probably heard the phrase “garbage in, garbage out.” If the questions are bad, the information gathered will be bad, too. One way to make sure you don’t end up with garbage is to test the questionnaire before sending it out to find out if there are any problems with it. Is there enough space for people to elaborate on open-ended questions? Is the font readable? To test the questionnaire, marketing research professionals first administer it to a number of respondents face to face. This gives the respondents the chance to ask the researcher about questions or instructions that are unclear or don’t make sense to them. The researcher then administers the questionnaire to a small subset of respondents in the actual way the survey is going to be disseminated, whether it’s delivered via phone, in person, by mail, or online.

Getting people to participate and complete questionnaires can be difficult. If the questionnaire is too long or hard to read, many people won’t complete it. So, by all means, eliminate any questions that aren’t necessary. Of course, including some sort of monetary incentive for completing the survey can increase the number of completed questionnaires a market researcher will receive.

Step 4: Specify the Sample

Once you have created your questionnaire or other marketing study, how do you figure out who should participate in it? Obviously, you can’t survey or observe all potential buyers in the marketplace. Instead, you must choose a sample. A sample is a subset of potential buyers that are representative of your entire target market, or population being studied. Sometimes market researchers refer to the population as the universe to reflect the fact that it includes the entire target market, whether it consists of a million people, a hundred thousand, a few hundred, or a dozen. “All unmarried people over the age of eighteen who purchased Dirt Devil steam cleaners in the United States during 2011” is an example of a population that has been defined.

Obviously, the population has to be defined correctly. Otherwise, you will be studying the wrong group of people. Not defining the population correctly can result in flawed research, or sampling error. A sampling error is any type of marketing research mistake that results because a sample was utilized. One criticism of Internet surveys is that the people who take these surveys don’t really represent the overall population. On average, Internet survey takers tend to be more educated and tech savvy. Consequently, if they solely constitute your population, even if you screen them for certain criteria, the data you collect could end up being skewed.

The next step is to put together the sampling frame , which is the list from which the sample is drawn. The sampling frame can be put together using a directory, customer list, or membership roster (Wrenn et. al., 2007). Keep in mind that the sampling frame won’t perfectly match the population. Some people will be included on the list who shouldn’t be. Other people who should be included will be inadvertently omitted. It’s no different than if you were to conduct a survey of, say, 25 percent of your friends, using friends’ names you have in your cell phone. Most of your friends’ names are likely to be programmed into your phone, but not all of them. As a result, a certain degree of sampling error always occurs.

There are two main categories of samples in terms of how they are drawn: probability samples and nonprobability samples. A probability sample is one in which each would-be participant has a known and equal chance of being selected. The chance is known because the total number of people in the sampling frame is known. For example, if every other person from the sampling frame were chosen, each person would have a 50 percent chance of being selected.

A nonprobability sample is any type of sample that’s not drawn in a systematic way. So the chances of each would-be participant being selected can’t be known. A convenience sample is one type of nonprobability sample. It is a sample a researcher draws because it’s readily available and convenient to do so. Surveying people on the street as they pass by is an example of a convenience sample. The question is, are these people representative of the target market?

For example, suppose a grocery store needed to quickly conduct some research on shoppers to get ready for an upcoming promotion. Now suppose that the researcher assigned to the project showed up between the hours of 10 a.m. and 12 p.m. on a weekday and surveyed as many shoppers as possible. The problem is that the shoppers wouldn’t be representative of the store’s entire target market. What about commuters who stop at the store before and after work? Their views wouldn’t be represented. Neither would people who work the night shift or shop at odd hours. As a result, there would be a lot of room for sampling error in this study. For this reason, studies that use nonprobability samples aren’t considered as accurate as studies that use probability samples. Nonprobability samples are more often used in exploratory research.

Lastly, the size of the sample has an effect on the amount of sampling error. Larger samples generally produce more accurate results. The larger your sample is, the more data you will have, which will give you a more complete picture of what you’re studying. However, the more people surveyed or studied, the more costly the research becomes.

Statistics can be used to determine a sample’s optimal size. If you take a marketing research or statistics class, you will learn more about how to determine the optimal size.

Of course, if you hire a marketing research company, much of this work will be taken care of for you. Many marketing research companies, like ResearchNow, maintain panels of prescreened people they draw upon for samples. In addition, the marketing research firm will be responsible for collecting the data or contracting with a company that specializes in data collection. Data collection is discussed next.

Step 5: Collect the Data

As we have explained, primary marketing research data can be gathered in a number of ways. Surveys, taking physical measurements, and observing people are just three of the ways we discussed. If you’re observing customers as part of gathering the data, keep in mind that if shoppers are aware of the fact, it can have an effect on their behavior. For example, if a customer shopping for feminine hygiene products in a supermarket aisle realizes she is being watched, she could become embarrassed and leave the aisle, which would adversely affect your data. To get around problems such as these, some companies set up cameras or two-way mirrors to observe customers. Organizations also hire mystery shoppers to work around the problem. A mystery shopper is someone who is paid to shop at a firm’s establishment or one of its competitors to observe the level of service, cleanliness of the facility, and so forth, and report his or her findings to the firm.

Make Extra Money as a Mystery Shopper

Watch the YouTube video to get an idea of how mystery shopping works.

Survey data can be collected in many different ways and combinations of ways. The following are the basic methods used:

• Face-to-face (can be computer aided)
• Telephone (can be computer aided or completely automated)
• Mail and hand delivery
• E-mail and the Web

A face-to-face survey is, of course, administered by a person. The surveys are conducted in public places such as in shopping malls, on the street, or in people’s homes if they have agreed to it. In years past, it was common for researchers in the United States to knock on people’s doors to gather survey data. However, randomly collected door-to-door interviews are less common today, partly because people are afraid of crime and are reluctant to give information to strangers (McDaniel & Gates, 1998).

Nonetheless, “beating the streets” is still a legitimate way questionnaire data is collected. When the U.S. Census Bureau collects data on the nation’s population, it hand delivers questionnaires to rural households that do not have street-name and house-number addresses. And Census Bureau workers personally survey the homeless to collect information about their numbers. Face-to-face surveys are also commonly used in third world countries to collect information from people who cannot read or lack phones and computers.

A plus of face-to-face surveys is that they allow researchers to ask lengthier, more complex questions because the people being surveyed can see and read the questionnaires. The same is true when a computer is utilized. For example, the researcher might ask the respondent to look at a list of ten retail stores and rank the stores from best to worst. The same question wouldn’t work so well over the telephone because the person couldn’t see the list. The question would have to be rewritten. Another drawback with telephone surveys is that even though federal and state “do not call” laws generally don’t prohibit companies from gathering survey information over the phone, people often screen such calls using answering machines and caller ID.

Probably the biggest drawback of both surveys conducted face-to-face and administered over the phone by a person is that they are labor intensive and therefore costly. Mailing out questionnaires is costly, too, and the response rates can be rather low. Think about why that might be so: if you receive a questionnaire in the mail, it is easy to throw it in the trash; it’s harder to tell a market researcher who approaches you on the street that you don’t want to be interviewed.

By contrast, gathering survey data collected by a computer, either over the telephone or on the Internet, can be very cost-effective and in some cases free. SurveyMonkey and Zoomerang are two Web sites that will allow you to create online questionnaires, e-mail them to up to one hundred people for free, and view the responses in real time as they come in. For larger surveys, you have to pay a subscription price of a few hundred dollars. But that still can be extremely cost-effective. The two Web sites also have a host of other features such as online-survey templates you can use to create your questionnaire, a way to set up automatic reminders sent to people who haven’t yet completed their surveys, and tools you can use to create graphics to put in your final research report. To see how easy it is to put together a survey in SurveyMonkey, click on the following link: http://help.surveymonkey.com/app/tutorials/detail/a_id/423 .

Like a face-to-face survey, an Internet survey can enable you to show buyers different visuals such as ads, pictures, and videos of products and their packaging. Web surveys are also fast, which is a major plus. Whereas face-to-face and mailed surveys often take weeks to collect, you can conduct a Web survey in a matter of days or even hours. And, of course, because the information is electronically gathered it can be automatically tabulated. You can also potentially reach a broader geographic group than you could if you had to personally interview people. The Zoomerang Web site allows you to create surveys in forty different languages.

Another plus for Web and computer surveys (and electronic phone surveys) is that there is less room for human error because the surveys are administered electronically. For instance, there’s no risk that the interviewer will ask a question wrong or use a tone of voice that could mislead the respondents. Respondents are also likely to feel more comfortable inputting the information into a computer if a question is sensitive than they would divulging the information to another person face-to-face or over the phone. Given all of these advantages, it’s not surprising that the Internet is quickly becoming the top way to collect primary data. However, like mail surveys, surveys sent to people over the Internet are easy to ignore.

Lastly, before the data collection process begins, the surveyors and observers need to be trained to look for the same things, ask questions the same way, and so forth. If they are using rankings or rating scales, they need to be “on the same page,” so to speak, as to what constitutes a high ranking or a low ranking. As an analogy, you have probably had some teachers grade your college papers harder than others. The goal of training is to avoid a wide disparity between how different observers and interviewers record the data.

Figure 10.11

Training people so they know what constitutes different ratings when they are collecting data will improve the quality of the information gathered in a marketing research study.

Ricardo Rodriquez – Satisfaction survey – CC BY-NC-ND 2.0.

For example, if an observation form asks the observers to describe whether a shopper’s behavior is hurried, moderately hurried, or unhurried, they should be given an idea of what defines each rating. Does it depend on how much time the person spends in the store or in the individual aisles? How fast they walk? In other words, the criteria and ratings need to be spelled out.

Collecting International Marketing Research Data

Gathering marketing research data in foreign countries poses special challenges. However, that doesn’t stop firms from doing so. Marketing research companies are located all across the globe, in fact. Eight of the ten largest marketing research companies in the world are headquartered in the United States. However, five of these eight firms earn more of their revenues abroad than they do in the United States. There’s a reason for this: many U.S. markets were saturated, or tapped out, long ago in terms of the amount that they can grow. Coke is an example. As you learned earlier in the book, most of the Coca-Cola Company’s revenues are earned in markets abroad. To be sure, the United States is still a huge market when it comes to the revenues marketing research firms generate by conducting research in the country: in terms of their spending, American consumers fuel the world’s economic engine. Still, emerging countries with growing middle classes, such as China, India, and Brazil, are hot new markets companies want to tap.

What kind of challenges do firms face when trying to conduct marketing research abroad? As we explained, face-to-face surveys are commonly used in third world countries to collect information from people who cannot read or lack phones and computers. However, face-to-face surveys are also common in Europe, despite the fact that phones and computers are readily available. In-home surveys are also common in parts of Europe. By contrast, in some countries, including many Asian countries, it’s considered taboo or rude to try to gather information from strangers either face-to-face or over the phone. In many Muslim countries, women are forbidden to talk to strangers.

And how do you figure out whom to research in foreign countries? That in itself is a problem. In the United States, researchers often ask if they can talk to the heads of households to conduct marketing research. But in countries in which domestic servants or employees are common, the heads of households aren’t necessarily the principal shoppers; their domestic employees are (Malhotra).

Translating surveys is also an issue. Have you ever watched the TV comedians Jay Leno and David Letterman make fun of the English translations found on ethnic menus and products? Research tools such as surveys can suffer from the same problem. Hiring someone who is bilingual to translate a survey into another language can be a disaster if the person isn’t a native speaker of the language to which the survey is being translated.

One way companies try to deal with translation problems is by using back translation. When back translation is used, a native speaker translates the survey into the foreign language and then translates it back again to the original language to determine if there were gaps in meaning—that is, if anything was lost in translation. And it’s not just the language that’s an issue. If the research involves any visual images, they, too, could be a point of confusion. Certain colors, shapes, and symbols can have negative connotations in other countries. For example, the color white represents purity in many Western cultures, but in China, it is the color of death and mourning (Zouhali-Worrall, 2008). Also, look back at the cartoon-completion exercise in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . What would women in Muslim countries who aren’t allowed to converse with male sellers think of it? Chances are, the cartoon wouldn’t provide you with the information you’re seeking if Muslim women in some countries were asked to complete it.

One way marketing research companies are dealing with the complexities of global research is by merging with or acquiring marketing research companies abroad. The Nielsen Company is the largest marketing research company in the world. The firm operates in more than a hundred countries and employs more than forty thousand people. Many of its expansions have been the result of acquisitions and mergers.

Step 6: Analyze the Data

Step 6 involves analyzing the data to ensure it’s as accurate as possible. If the research is collected by hand using a pen and pencil, it’s entered into a computer. Or respondents might have already entered the information directly into a computer. For example, when Toyota goes to an event such as a car show, the automaker’s marketing personnel ask would-be buyers to complete questionnaires directly on computers. Companies are also beginning to experiment with software that can be used to collect data using mobile phones.

Once all the data is collected, the researchers begin the data cleaning , which is the process of removing data that have accidentally been duplicated (entered twice into the computer) or correcting data that have obviously been recorded wrong. A program such as Microsoft Excel or a statistical program such as Predictive Analytics Software (PASW, which was formerly known as SPSS) is then used to tabulate, or calculate, the basic results of the research, such as the total number of participants and how collectively they answered various questions. The programs can also be used to calculate averages, such as the average age of respondents, their average satisfaction, and so forth. The same can done for percentages, and other values you learned about, or will learn about, in a statistics course, such as the standard deviation, mean, and median for each question.

The information generated by the programs can be used to draw conclusions, such as what all customers might like or not like about an offering based on what the sample group liked or did not like. The information can also be used to spot differences among groups of people. For example, the research might show that people in one area of the country like the product better than people in another area. Trends to predict what might happen in the future can also be spotted.

If there are any open-ended questions respondents have elaborated upon—for example, “Explain why you like the current brand you use better than any other brand”—the answers to each are pasted together, one on top of another, so researchers can compare and summarize the information. As we have explained, qualitative information such as this can give you a fuller picture of the results of the research.

Part of analyzing the data is to see if it seems sound. Does the way in which the research was conducted seem sound? Was the sample size large enough? Are the conclusions that become apparent from it reasonable?

The two most commonly used criteria used to test the soundness of a study are (1) validity and (2) reliability. A study is valid if it actually tested what it was designed to test. For example, did the experiment you ran in Second Life test what it was designed to test? Did it reflect what could really happen in the real world? If not, the research isn’t valid. If you were to repeat the study, and get the same results (or nearly the same results), the research is said to be reliable . If you get a drastically different result if you repeat the study, it’s not reliable. The data collected, or at least some it, can also be compared to, or reconciled with, similar data from other sources either gathered by your firm or by another organization to see if the information seems on target.

Stage 7: Write the Research Report and Present Its Findings

If you end up becoming a marketing professional and conducting a research study after you graduate, hopefully you will do a great job putting the study together. You will have defined the problem correctly, chosen the right sample, collected the data accurately, analyzed it, and your findings will be sound. At that point, you will be required to write the research report and perhaps present it to an audience of decision makers. You will do so via a written report and, in some cases, a slide or PowerPoint presentation based on your written report.

The six basic elements of a research report are as follows.

• Title Page . The title page explains what the report is about, when it was conducted and by whom, and who requested it.
• Table of Contents . The table of contents outlines the major parts of the report, as well as any graphs and charts, and the page numbers on which they can be found.
• Executive Summary . The executive summary summarizes all the details in the report in a very quick way. Many people who receive the report—both executives and nonexecutives—won’t have time to read the entire report. Instead, they will rely on the executive summary to quickly get an idea of the study’s results and what to do about those results.

Methodology and Limitations . The methodology section of the report explains the technical details of how the research was designed and conducted. The section explains, for example, how the data was collected and by whom, the size of the sample, how it was chosen, and whom or what it consisted of (e.g., the number of women versus men or children versus adults). It also includes information about the statistical techniques used to analyze the data.

Every study has errors—sampling errors, interviewer errors, and so forth. The methodology section should explain these details, so decision makers can consider their overall impact. The margin of error is the overall tendency of the study to be off kilter—that is, how far it could have gone wrong in either direction. Remember how newscasters present the presidential polls before an election? They always say, “This candidate is ahead 48 to 44 percent, plus or minus 2 percent.” That “plus or minus” is the margin of error. The larger the margin of error is, the less likely the results of the study are accurate. The margin of error needs to be included in the methodology section.

• Findings . The findings section is a longer, fleshed-out version of the executive summary that goes into more detail about the statistics uncovered by the research that bolster the study’s findings. If you have related research or secondary data on hand that back up the findings, it can be included to help show the study did what it was designed to do.
• Recommendations . The recommendations section should outline the course of action you think should be taken based on the findings of the research and the purpose of the project. For example, if you conducted a global market research study to identify new locations for stores, make a recommendation for the locations (Mersdorf, 2009).

As we have said, these are the basic sections of a marketing research report. However, additional sections can be added as needed. For example, you might need to add a section on the competition and each firm’s market share. If you’re trying to decide on different supply chain options, you will need to include a section on that topic.

As you write the research report, keep your audience in mind. Don’t use technical jargon decision makers and other people reading the report won’t understand. If technical terms must be used, explain them. Also, proofread the document to ferret out any grammatical errors and typos, and ask a couple of other people to proofread behind you to catch any mistakes you might have missed. If your research report is riddled with errors, its credibility will be undermined, even if the findings and recommendations you make are extremely accurate.

Many research reports are presented via PowerPoint. If you’re asked to create a slideshow presentation from the report, don’t try to include every detail in the report on the slides. The information will be too long and tedious for people attending the presentation to read through. And if they do go to the trouble of reading all the information, they probably won’t be listening to the speaker who is making the presentation.

Instead of including all the information from the study in the slides, boil each section of the report down to key points and add some “talking points” only the presenter will see. After or during the presentation, you can give the attendees the longer, paper version of the report so they can read the details at a convenient time, if they choose to.

Key Takeaway

Step 1 in the marketing research process is to define the problem. Businesses take a look at what they believe are symptoms and try to drill down to the potential causes so as to precisely define the problem. The next task for the researcher is to put into writing the research objective, or goal, the research is supposed to accomplish. Step 2 in the process is to design the research. The research design is the “plan of attack.” It outlines what data you are going to gather, from whom, how, and when, and how you’re going to analyze it once it has been obtained. Step 3 is to design the data-collection forms, which need to be standardized so the information gathered on each is comparable. Surveys are a popular way to gather data because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, survey questionnaires need to be carefully designed and pretested before they are used. Step 4 is drawing the sample, or a subset of potential buyers who are representative of your entire target market. If the sample is not correctly selected, the research will be flawed. Step 5 is to actually collect the data, whether it’s collected by a person face-to-face, over the phone, or with the help of computers or the Internet. The data-collection process is often different in foreign countries. Step 6 is to analyze the data collected for any obvious errors, tabulate the data, and then draw conclusions from it based on the results. The last step in the process, Step 7, is writing the research report and presenting the findings to decision makers.

Review Questions

• Explain why it’s important to carefully define the problem or opportunity a marketing research study is designed to investigate.
• Describe the different types of problems that can occur when marketing research professionals develop questions for surveys.
• How does a probability sample differ from a nonprobability sample?
• What makes a marketing research study valid? What makes a marketing research study reliable?
• What sections should be included in a marketing research report? What is each section designed to do?

1 “Questionnaire Design,” QuickMBA , http://www.quickmba.com/marketing/research/qdesign (accessed December 14, 2009).

Barnes, B., “Disney Expert Uses Science to Draw Boy Viewers,” New York Times , April 15, 2009, http://www.nytimes.com/2009/04/14/arts/television/14boys.html?pagewanted=1&_r=1 (accessed December 14, 2009).

Burns A. and Ronald Bush, Marketing Research , 6th ed. (Upper Saddle River, NJ: Prentice Hall, 2010), 85.

Malhotra, N., Marketing Research: An Applied Approach , 6th ed. (Upper Saddle River, NJ: Prentice Hall), 764.

McDaniel, C. D. and Roger H. Gates, Marketing Research Essentials , 2nd ed. (Cincinnati: South-Western College Publishing, 1998), 61.

McWilliams, J., “A-B Puts Super-Low-Calorie Beer in Ring with Miller,” St. Louis Post-Dispatch , August 16, 2009, http://www.stltoday.com/business/next-matchup-light-weights-a-b-puts-super-low-calorie/article_47511bfe-18ca-5979-bdb9-0526c97d4edf.html (accessed April 13, 2012).

Mersdorf, S., “How to Organize Your Next Survey Report,” Cvent , August 24, 2009, http://survey.cvent.com/blog/cvent-survey/0/0/how-to-organize-your-next-survey-report (accessed December 14, 2009).

Rappeport A. and David Gelles, “Facebook to Form Alliance with Nielsen,” Financial Times , September 23, 2009, 16.

Spangler, T., “Disney Lab Tracks Feelings,” Multichannel News 30, no. 30 (August 3, 2009): 26.

Wagner, J., “Marketing in Second Life Doesn’t Work…Here Is Why!” GigaOM , April 4, 2007, http://gigaom.com/2007/04/04/3-reasons-why-marketing-in-second-life-doesnt-work (accessed December 14, 2009).

Wrenn, B., Robert E. Stevens, and David L. Loudon, Marketing Research: Text and Cases , 2nd ed. (Binghamton, NY: Haworth Press, 2007), 180.

Zouhali-Worrall, M., “Found in Translation: Avoiding Multilingual Gaffes,” CNNMoney.com , July 14, 2008, http://money.cnn.com/2008/07/07/smallbusiness/language_translation.fsb/index.htm (accessed December 14, 2009).

Principles of Marketing Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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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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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

Define sampling plan.

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

The sampling plan is a part of the _________ phase.

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

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

The ___________    involves deciding the target population.  

sampling unit

The   sample size

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

What are the two types of sampling plans?

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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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

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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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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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• identify the parameters to be measured, the range of possible values, and the required resolution
• design a sampling scheme that details how and when samples will be taken
• select sample sizes
• design data storage formats
• assign roles and responsibilities

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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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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

Define sampling plan.

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

The sampling plan is a part of the _________ phase.

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

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

The ___________    involves deciding the target population.  

sampling unit

The   sample size

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

What are the two types of sampling plans?

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. 

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