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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

Related articles, what is data quality and why is it important.

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• Sampling Plan

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

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• Cell Biology

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

The researcher selects individuals at a regular interval, for example, every 15th person will be selected for the research. This is 

__________ is  used when trying to find people with traits that are difficult to trace.  

T his involves collecting information from a homogenous group.  

The ___________    involves deciding the target population.  

The   sample size

Steps of a sampling plan:

• Sample definition
• _____________
• Sample design

T his step makes sure that the samples chosen were representative enough and ensures quality data collection.

What are the two types of sampling plans?

Select the probability sampling methods:

What happens in the sample determination stage?

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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

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

Systematic sampling 

  Snowball sampling  

Quota Sampling 

sampling unit

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

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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

This involves collecting information from a homogenous group. 

The ___________  involves deciding the target population. 

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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.

Get started today.

Privacy overview.

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.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

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

© Jan 9, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

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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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How to Write a Marketing Sampling Plan

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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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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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Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

Sampling frame

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

Sample size

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

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

There are four main types of probability sample.

1. Simple random sampling

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

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

2. Systematic sampling

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

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

3. Stratified sampling

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

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

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

4. Cluster sampling

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

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

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

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

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

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

1. Convenience sampling

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

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

2. Voluntary response sampling

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

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

3. Purposive sampling

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

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

4. Snowball sampling

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

5. Quota sampling

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

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

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

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

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

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

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

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

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

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

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

for accuracy at the 95% level.

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

S.E. = 3.3%

(Standard Error)

Marketing91

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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9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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Sampling in marketing research: introduction, benefits and types.

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Introduction :

Today’s marketing research projects are large, and, sometimes, indefinite number of items are involved. Practically, it is not possible to study all the people or items under study. For less time, less money, and ease, the sample seems more practical. Most marketing research projects depend on the sample survey rather than the totality survey. Whether to use a census or a sample depends on a number of factors, such as type of census, degree of homogeneity/heterogeneity, costs, time, feasibility to study, degree of accuracy needed, and some others.

Normally, the census is preferred under following situations:

1. When population is small.

2. Variance in characteristics being measured is high.

3. The cost of error is high.

4. Fixed cost of sampling is high.

Basic Terms Related to Sampling :

Now, first, let’s define related terms:

Sample is a part of universe/population/census, which represents the characteristics of the whole universe under study. Thus, sample is a small portion of the population/universe from which it has been drawn that may represent that population.

Population:

A group from which the sample is drawn is called the population or universe.’ In different words, the universe is the entire group of items about which researcher wishes to study and about which he plans to generalize. Population may be made up of individuals, groups, associations, areas, or households. If population is not defined, it seems infinite.

Sampling is a process of selecting a few items from a given population to be investigated.

There are four basic questions related to sampling:

1. What constitutes the population?

2. Should we take sample or census?

3. What type of sample would be taken?

4. What should be the size of sample?

Benefits or Reasons for Sampling :

Sampling offers several benefits over the census.

The main benefits have been listed below:

1. Only possible method in case of a very large population.

2. It is a time-saving option.

3. Speedy assessment/investigation is possible.

4. It may be more accurate as each item under study is given more attention due to a manageable number of items.

5. It is the economic way to conduct survey. Costs of investigating sample are far low than the population.

Types of Sampling Procedures/Types of Samples:

There are several ways to draw a sample from the definite or indefinite population. Each type of sampling procedure has its merits, demerits, and applicability. Depending upon need, an appropriate sampling procedure may be followed. In real practice, not single type, but a combination of several types of sampling procedures is used.

Sampling procedures can be categories into two broad classes:

1. Probabilistic Sampling Procedure

2. Non-probabilistic Sampling Procedure

Probabilistic Sampling Procedures:

It has been developed after 1950’s. It is a bias-free method of selecting sample unit as it depends on a chance rather than a judgment. Each sample unit of the population has known chance of being selected for the sample. Sampling procedure is based on the mathematical decision, which leaves no discretion to researcher. One can estimate in advance about chance/ probability of the sample unit to be selected. Here, ‘known chance’ does not mean ‘equal chance.’ ‘Equal chance’ is possible in a special case, say, only in case of simple random sampling.

This method permits measurement of sampling error:

1. Simple Random Sampling:

It is the simplest type of probability sampling. The most fundamental feature of simple random sampling is that each sample element has known and equal chance (probability) of being selected. More specifically, we can say that every possible sample of given size drawn from a specified universe has known and equal chance of being selected. The sample is drawn by randomly (haphazardly) from the sample frame (a list of exclusive and exhaustive enumeration of all sample elements). It is used only when population under study is relatively small.

2. Stratified Sampling:

In case of the stratified random sampling, the population under study is divided into certain groups known as ‘strata’ or parts. Then, from each stratum, an appropriate sample is drawn randomly. Number of strata depends on degree of heterogeneity in the population under study.

The higher is the degree of heterogeneity, the larger the number of strata will be and vice versa. For example, if we want to know attitudes of students toward private tuitions, we divide the total number of respondents (students) of Gujarat State in various parts or strata such as college students and school students; stratification may follow level of education such as first year students, second year, third year, post graduate level, diploma level; it may be on the basis of technical and non-technical disciplines; may be city-wise or university-wise classification.

Stratification takes place in a several ways. Now, from each of the stratum, a sample of appropriate number of students is selected. Sample drawn from each of the stratum represents only that stratum. Final generalization is drawn by combining response of all the samples drawn from each of the strata.

3. Systematic Sampling:

Here, a specified system or pattern is followed to draw a sample. For example: If population consists of 100 items, every item multiple of five can be selected, such as 5, 10, 15, 20…. Sometimes, odd or even numbers are selected. In short, a system is followed to select the sample. It is possible when the population under study is well-defined and items are properly arranged, and population is definite. Sometimes, specially prepared tables are also used.

4. Cluster Sampling:

It is also known as block sampling. In the sample methods discussed so far, the units of sample are selected individually, for example, a customer. But in case of cluster sampling, each sample unit is not individual unit but cluster or a group of units. For example, a household containing 5 members constitutes a sample unit.

So, population must be divided into mutually exclusive and collectively exhaustive groups. In short, it is similar to simple random sampling with the difference of a cluster as a sample unit. It may be one-stage or two-stage sampling depending upon the procedure followed.

5. Area Sampling:

It is also a form of the specified stratified sampling. The word ‘area’ in area sampling originally refers to a piece of the land. An area sampling is actually sample of areas. It extensively used in actual practice. It suggests primary sampling of geographical area like a sample of countries, states, towns, villages, blocks, societies, apartments, or other areas of discretion. Here, people reside in particular piece of land are studied.

Area sampling is also of two types, one-stage area sampling and two-stage area sampling. One-stage area sampling involves choosing a simple random sample of n areas from population of N areas of particular region. In case of two-stage area sampling, first areas are selected and then certain households from each of the selected areas are selected. It can be multi-staged areas sampling if more stages are followed.

Non-Probabilistic Sampling Procedures :

Here, selection of sample is based on some sort of judgment of researcher. There is no chance of any particular element to be selected. In case of non-probabilistic sampling, one must rely on experience and expertise of the person drawing the sample. In case of this sampling procedure, we are unable to measure sampling error. Therefore, we are unable to say that sample estimates calculated from non-probabilistic sample are accurate.

They include:

1. Convenience Sampling

2. Judgment Sampling

3. Quota Sampling

4. Snowball Sampling.

Related Articles:

• 7 Fundamental Concepts Pertaining to Sampling
• Application of Sampling Techniques in Marketing Research

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Gen z, millennials are no longer relaxing while on vacation: here’s why.

Younger Americans think relaxing on vacation is overrated, according to new research.

A survey of 2,000 Americans who travel (evenly split by generation) looked at how people of different ages vacation and found both Gen X (51%) and baby boomers (57%) prioritize relaxation when traveling; whereas, Gen Z (54%) and millennials (45%) are more interested in making memories.

In fact, 29% of those who don’t prioritize relaxation when they’re away find it to be a waste of time — especially millennials (30%).

Conducted by Talker Research for  Apple Vacations , the survey found that these preferences may change in the future, as 59% shared their vacation priorities have shifted as they got older.

A quarter of millennials reported that sightseeing is a thing of the past and Gen Z is no longer prioritizing learning new things while traveling (28%).

Respondents from different generations recalled the average age they were when their vacation preferences changed. Gen Z preferences shift at age 18, while millennials see it at 27, Gen X at 38 and baby boomers at 54.

Something that won’t change is Americans’ love for traveling, with one in four sharing that traveling is a high priority for them these days (28%).

Although millennials are most focused on traveling right now (38%), Gen Z (35%) is most likely to surpass the average number of trips taken annually.

Looking at the differences in how they enjoy this time away, results showed that baby boomers (74%) prefer domestic travel, while Gen Z (14%) is the most likely to enjoy international travel.

Family-friendly (33%) and tropical (27%) destinations are favored across the generations, but Gen Z (25%) and millennials (24%) also share a strong love for theme parks.

Aside from their top picks, Gen X (21%) and baby boomers (24%) are also drawn to small towns.

“Relaxation looks and feels different for everyone,” said Dana Studebaker, vice president of marketing at Apple Vacations. “I feel most relaxed when reading a great book on the beach knowing that all I need is at my fingertips at an all-inclusive resort, but others feel renewed after a beautiful backpacking trip through the mountains, soaking up history in a big city. This is the beauty of travel, your vacations can evolve with your preferences.”

Before their trip, Gen Z is especially keen on creating a vacation plan (60%) and baby boomers are the likeliest to go with the flow (40%).

Gen X (53%) and baby boomers (65%) who prefer going with the flow agree that this helps them avoid feeling rushed or tied to a plan.

Planners find comfort in mapping their day (56%) and say it helps them make the most of their time (54%).

Gen Z embraces the safety they feel when planning things out (46%), while baby boomers who prefer to plan ahead also enjoy the feeling of checking off things they want to do (50%). 

When travel planning, millennials are most likely to use a travel agent (18%), while Gen Z leans heavily on social media for planning (55%) and inspiration (66%).

Older generations prefer to keep things more classic, with Gen X trusting word of mouth (44%) and baby boomers referencing travel magazines or websites for advice (34%).

Before vacationing, millennials are the likeliest to read restaurant menus (34%); instead, Gen X looks at pictures others have posted (36%).

Gen Z respondents are most likely to look at how much things cost and the currency exchange (49%), as well as the transportation options (43%).

Looking ahead, half of respondents plan to travel more as they get older.

Gen Z will take advantage of this the most (73%), planning to travel with their friends (43%), while baby boomers will travel to connect with other family members (20%).

Family is top of mind for those who traveled with their parents when growing up, with 77% planning to continue the tradition of traveling with their kids to bond (66%) and make new memories (65%).

“Getting out of the house and spending quality time with family while traveling is the best way to build memories,” said Michael Lowery, senior vice president and global head of consumer business units at Apple Vacations. “When traveling with a larger group with many ages to keep in mind, I always go for an all-inclusive resort because any worries about entertaining different ages are taken care of. Some guests may enjoy a day at the spa and others can safely be thrilled with resort excursions and activities.”

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DESTINATIONS AMERICANS WOULD HAVE LIKED TO VISIT WHEN THEY WERE YOUNGER

• New Orleans
• The Caribbean
• Disney World
• Los Angeles

Survey methodology:

Talker Research surveyed 2,000 Americans who travel evenly split by generation; the survey was commissioned by Apply Vacations and administered and conducted online by Talker Research between June 20 and June 27, 2024.

We are sourcing from a non-probability frame and the two main sources we use are:

• Traditional online access panels — where respondents opt-in to take part in online market research for an incentive
• Programmatic — where respondents are online and are given the option to take part in a survey to receive a virtual incentive usually related to the online activity they are engaging in

Those who did not fit the specified sample were terminated from the survey. As the survey is fielded, dynamic online sampling is used, adjusting targeting to achieve the quotas specified as part of the sampling plan.

Regardless of which sources a respondent came from, they were directed to an Online Survey, where the survey was conducted in English; a link to the questionnaire can be shared upon request. Respondents were awarded points for completing the survey. These points have a small cash-equivalent monetary value.

Cells are only reported on for analysis if they have a minimum of 80 respondents, and statistical significance is calculated at the 95% level. Data is not weighted, but quotas and other parameters are put in place to reach the desired sample.

Interviews are excluded from the final analysis if they failed quality-checking measures. This includes:

• Speeders: Respondents who complete the survey in a time that is quicker than one-third of the median length of interview are disqualified as speeders
• Open ends: All verbatim responses (full open-ended questions as well as other please specify options) are checked for inappropriate or irrelevant text
• Bots: Captcha is enabled on surveys, which allows the research team to identify and disqualify bots
• Duplicates: Survey software has “deduping” based on digital fingerprinting, which ensures nobody is allowed to take the survey more than once

It is worth noting that this survey was only available to individuals with internet access, and the results may not be generalizable to those without internet access.

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How our relationships are changing in the age of “artificial intimacy"

Early adopters are flocking to AI bots for therapy, friendship, even love. How will these relationships impact us? MIT sociologist Sherry Turkle delves into her new research on "artificial intimacy."

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MIT sociologist Sherry Turkle on the psychological impacts of bot relationships

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