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Market Research

What Is Systematic Sampling?

Systematic Sampling

Data runs the business world these days. It’s great to always use data to back everything from major business decisions to website tweaks. But data is only as good as the survey that pumped it out. If you put bad information into your survey, your data isn’t reliable to base your business decisions on. How do you prevent this from happening? After all, you don’t want to have bad data at the helm of your decisions. 

One way researchers try to ensure their information is reliable is to use random sampling, specifically systematic sampling. By adding the element of randomness, results are more representative of the population you’re trying to study. 

Consider a retail business that wants to understand customer satisfaction with recent in-store experiences to pinpoint specific aspects needing attention, such as staff friendliness, store cleanliness, and product availability. They can use systematic sampling to gather feedback from every 15th customer that enters the store until they reach the desired sample size. Since these customers were chosen at random, the results can be used to represent and measure the entire customer base. 

What Is Systematic Sampling? 

Systematic sampling is a type of probability sampling that uses a specific interval to select participants. Probability sampling is when every member of the population (the entire group you want to study) has an equal chance of being selected. It’s the foundation of good data collection. However with systematic sampling, you choose a regular interval and select your participants that way. 

Imagine you have a list of 100 people in your population, and you want to use systematic sampling to select your sample. You decide on an interval of five. The best way to use systematic sampling is to choose a random place to start on your list. Maybe you start at the second name listed. From there, you would choose every fifth name to be a participant. That’s systematic sampling. 

The key features of systematic sampling are that it’s probability-based and that there’s a specific number interval used to select your sample. 

Types of Systematic Sampling

The basics of systematic sampling are the same, but there are a few different ways you can perform a systematic sample. The three types of systematic sampling are systematic random sampling, circular systematic sampling, and linear systematic sampling.

Systematic Random Sampling

Systematic random sampling is the classic way to use systematic sampling. It involves choosing a particular interval that is used to randomly select participants. But how do researchers choose effective intervals? Most use their population size and figure out how many they want in a sample. For example, if you have 100 people in your population, and you know you want to survey 20 of them, you know your interval will be 5.

Circular Systematic Sampling

Circular systematic sampling is most useful if you know you want to sample your entire population, but you still want the element of randomness in your sampling methods. Circular systematic sampling works the same as classic systematic sampling at first. But instead of stopping selection after you reach the end of the population list, you start again and keep selecting using your numeric interval until you’ve sampled everyone in your population. 

For example, let’s return to our list of 100 people. You choose the interval of 5 for your sampling and randomly select starting on the fourth name. You sample every fourth name until you reach the end of the list. But instead of stopping there, circular systematic sampling has you keep sampling every fourth name until you’ve gone through the whole list. It’s a great way to continue to select randomly while sampling your whole population. Circular systematic sampling isn’t a great choice if you have a very large population, and you only need a small sample and a way to whittle the list down. 

Linear Systematic Sampling

Linear systematic sampling is another variation, but it’s different from circular sampling. Linear systematic sampling doesn’t repeat and continues until the whole population is sampled. Instead, linear sampling uses a form of skip logic to select. Skip logic is something you might use to send participants in a survey to a different spot in the survey based on their answers. The researcher uses skip logic to select where to start on the survey and the interval to use. If you think of the list of 100 population members, here you would use skip logic to determine where the sampling starts and who is chosen, and it doesn’t repeat at the end.

When Is Systematic Sampling Used? 

When do researchers choose to use systematic sampling? Systematic sampling provides a unique way to use random sampling without having to have a lot of details on your population or on a tight budget or timeline. These are some of the scenarios when systematic sampling is commonly used: 

  • Budget or timing restrictions: If you have an upcoming deadline or a small budget for sampling, systematic sampling is simple to implement and quick for picking a sample under a time crunch. You can very quickly number a population list and randomly select an interval and starting point and choose participants quickly. This same process also requires few resources, and you don’t have to know much about your population to get started with systematic sampling. 
  • Simple outcomes: Systematic sampling is a simple way to choose a sample, so it’s best when used when the outcomes are simple. A complicated survey isn’t the place for a simple systematic sample. 
  • Absence of data patterns: Systematic sampling is used when the data aren’t arranged or have an obvious pattern or opportunity for data manipulation. That could lead to problems. But without that, systematic sampling is a great choice. 

How to Conduct Systematic Sampling

Performing systematic sampling involves a series of steps to ensure randomness and representation. Here’s a step-by-step guide:

1. Define Your Population

Clearly identify the entire population you want to study. This could be a list of customers, employees, or any group relevant to your research. 

If you do not have a list readily available, you can go into the field to survey the intended group. Take the retail example from earlier, an employee at the register could ask every 15th customer “Did you find everything you were looking for today?” This way of surveying customers mirrors the randomization process that a formal population list would give you. 

2. Determine Your Sample Size

Decide how many participants you want in your sample. This should be a reasonable fraction of your population and is often based on your research objectives and available resources.

3. Calculate the Sampling Interval

Divide the total population size by the desired sample size to determine the sampling interval. For example, if you have 100 people and want a sample of 20, your interval is 5 (100/20).

4. Randomly Choose a Starting Point

Begin at a randomly selected point in your population. This could involve using a random number generator or another method to ensure true randomness.

5. Select Participants Systematically

Starting from your randomly chosen point, select every nth individual, where n is the sampling interval. For instance, if your interval is 5, select every fifth person until you reach your desired sample size.

6. Avoid Biases

Ensure your list is randomly ordered at the outset to prevent bias. If there’s a discernible pattern in your population list, it could compromise the randomness of your sample.

7. Record Your Methodology

Document the steps you took in selecting your sample. This transparency aids in replicability and allows others to assess the validity of your sampling method.

Examples of Systematic Sampling

Let’s explore a couple of real-world examples to illustrate how systematic sampling works:

Example 1: Customer Satisfaction Surveys

Imagine you run a business with a customer database of 500 clients, and you want to gauge overall satisfaction. You decide to systematically sample 100 customers. Here’s how:

  • Define Population: Your population is the entire customer database.
  • Determine Sample Size: You decide on a sample size of 100 customers.
  • Calculate Sampling Interval: Divide 500 (total customers) by 100 (desired sample size) to get an interval of 5.
  • Random Starting Point: Choose a random starting point in your customer list.
  • Select Participants: Systematically survey every 5th customer from your starting point until you reach 100 responses.

Example 2: Employee Training Evaluation

In a company with 200 employees, the HR department wants to assess the effectiveness of a recent training program. They opt for systematic sampling:

  • Define Population: The population is all 200 employees who underwent the training.
  • Determine Sample Size: The HR team decides on a sample size of 40 employees.
  • Calculate Sampling Interval: Divide 200 (total employees) by 40 (desired sample size) to get an interval of 5.
  • Random Starting Point: Choose a random starting point in the list of trained employees.
  • Select Participants: Systematically evaluate the performance of every 5th employee until they reach 40 responses.

These examples demonstrate how systematic sampling can be applied in different scenarios, providing a structured and representative approach to data collection.

Common Mistakes in Implementing Systematic Sampling

While systematic sampling is a relatively straightforward method, certain pitfalls can compromise the integrity of your results. Avoiding these common mistakes is essential to ensure the accuracy and representativeness of your sample.

1. Non-Randomized Starting Point:

  • Mistake: Choosing a starting point that is not truly random can introduce bias. If the starting point follows a pattern or is influenced by external factors, the entire sample may not be representative of the population.
  • Solution: Use a randomization method, such as a random number generator, to select the initial participant. This helps eliminate any unintentional biases at the starting point.

2. Incorrect Calculation of Sampling Interval

  • Mistake: Calculating the sampling interval incorrectly can lead to an unrepresentative sample. Errors in determining the interval may result in oversampling or undersampling certain segments of the population.
  • Solution: Double-check your calculations to ensure the sampling interval is accurate. Verify that it aligns with your desired sample size and the total population.

3. Failure to Randomly Order the Population List

  • Mistake: Neglecting to randomize the order of the population list before implementing systematic sampling can introduce systematic biases. If there is an existing order or pattern, it may carry through to the sample.
  • Solution: Randomly order the population list before starting the systematic sampling process. This helps ensure that each individual has an equal chance of being selected.

4. Misinterpretation of Results

  • Mistake: Misinterpreting the results or drawing conclusions beyond the scope of the study can lead to inaccurate insights. Failing to recognize the limitations of systematic sampling may result in unwarranted generalizations.
  • Solution: Clearly define the objectives of your study and acknowledge the limitations of systematic sampling. Present the results with a clear understanding of what the sample can and cannot represent.

5. Ignoring Population Changes

  • Mistake: Assuming that the population remains static throughout the study without accounting for potential changes can lead to inaccurate results. Population dynamics, such as growth or decline, should be considered.
  • Solution: Periodically reassess the population characteristics and adjust the sampling process if there are significant changes. This ensures that your sample remains representative of the current population.

By being aware of these common mistakes and taking proactive measures to address them, researchers can enhance the reliability and validity of their systematic sampling approach. Regular checks, documentation, and attention to randomization principles are key to a successful implementation.

Advantages of Systematic Sampling

Systematic sampling offers several advantages that make it a preferred choice in certain situations:

  • Simple to understand: Not every method of sampling randomly is easy for researchers to understand—especially not with an extensive background in statistics. Systematic sampling is easy to grasp and easy to get started with.
  • Easy to implement: Nothing’s worse than having extremely difficult sampling requirements to grapple with that take forever to actually get started. Systematic sampling avoids that. Instead, it’s easy to get started quickly. 
  • Organized method of sampling: Organization with your data is key to making analysis simple and doable down the road. Systematic sampling is exactly what it sounds like: systematic. And systematic is organized and can help you keep track of what’s going on. 
  • Low risk for bias or contamination when done well: Data contamination and bias can leave you with bad results and bad data to base your decisions on. Systematic sampling can be a way to combat that. So long as the population list can be ordered randomly, there’s a low risk for bias or data contamination when you use this sampling method. 

Disadvantages of Systematic Sampling

While systematic sampling has its advantages, it also comes with certain limitations and challenges. Researchers have to plan for and make sure to avoid these when using systematic sampling: 

  • Risk of bias: While there’s a low risk of bias, there’s still a risk that has to be managed. The list of population members must be ordered randomly, or there’s sampling bias. That’s easy when you have a list of names and can order that randomly but could be complicated for other populations.
  • Risk of data manipulation: When you use systematic sampling, you’re setting up a system to use. There’s a risk that researchers might set up a system to intentionally give them the results they want, introducing a world of problems into their data. 
  • Requires population size: The other risks can be controlled for, but this disadvantage is an inherent one. To effectively use systematic sampling, you have to be able to number your population. That means you have to know your population size exactly before you can sample. It might be easy when you’re sampling the employees at your company, but it might be more difficult when you have a potentially massive population that you aren’t sure what it entails.  

Maximize the Potential of Systematic Sampling with Pearl-Plaza

Overall, systematic sampling is a form of probability sampling and can be incredibly valuable on a tight timeline or budget with simple populations. The key is to make sure you’re using the right sampling method for your surveys. Pearl-Plaza integrated CX approach gives you the power to combine data from multiple sources and discover value insights that drive better business decisions. Whether your sample size is in the hundreds or millions, the XI Platform can be changed to fit your business needs and help you make the most of your data—from sampling methods to analysis. Schedule a demo to see how Pearl-Plaza can help you!

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Understanding the Sample Size Formula

Sample Size Formula

There’s a lot that goes into creating statistically sound research, but few elements are as important as getting the right sample size. This is because the size of your sample can have a direct impact on your findings. If your chosen sample is too small, your results will likely be inconclusive. On the other hand, overly large samples can make minor differences appear statistically significant while also increasing the time and resource demands of collecting and cleaning the data.

Unfortunately, understanding the need for correct sample sizes and understanding how to select the right sample sizes are two different issues. For effective sample size determination, many researchers rely on the sample size formula. 

Here, we’ll walk you through the sample size formula and how to apply it. But first, let’s take a look at what “sample size” means.

What Is a Sample Size? 

Sample size is a term used in research and statistics that defines the total number of subjects, samples, or observations included in a survey or experiment. 

For example, if you were to interview 50 travelers about their air-travel experience, then your sample size would be 50. Similarly, an experiment that makes daily observations regarding soil content over the space of one full year would have a sample size of 365. And if an online survey were to return 11,328 completed questionnaire forms, then that’s a sample size of 11,328. Simply put, the sample size is the number of samples you’re interacting with.

Sampling allows researchers to select a representative portion of an entire population; to expand on one of the examples provided above, an airline that chooses the right sample group can hopefully draw meaningful and accurate conclusions from interviewing 50 travelers, instead of having to interview every traveler who flies on a plane. 

As previously addressed, sample size plays a key role in any statistical setting—from lab experiments to employee surveys—and is a vital factor in any research project.

What Is the Sample Size Formula?

The sample size formula is a calculation for determining what sample size is appropriate to ensure that the test has a specified power. To do this, we must first calculate the sample size for an infinite (or unknown) population, after which we will adjust our sample size to fit the required finite (or known) population. 

Sample Size Formula: Infinite Population

S = Z² x P x (1 – P)M²

Adjusted Sample Size Formula: Finite Population

Adjusted sample size = (S)1 + (S – 1)Population

In these formulas, the variables are expressed as follows:

  • S = The sample size for an infinite population
  • Z = The Z-score (determined based on the confidence level)
  • P = The population proportion (assumed as 50%)
  • M = The margin of error (typically taken as 5%)

Applying the Sample Size Formula

The formulas presented above may be used to correctly determine viable sample sizes, but before that can be put to work, the values of the various variables need to be defined. 

To correctly apply the formula, follow these steps:

  1. Determine Your Key Values
    The primary key value in this equation is the total population size within your target demographic. Where possible, be as accurate as you can in determining the population number—this will allow for greater statistical impact, particularly when dealing with a small population size. That said, larger populations may allow for some approximation (such as rounding to the nearest hundred or thousand).
  2. Determine Your Margin for Error/Confidence Interval
    Although you want your study to be as precise as possible, it will never be completely accurate. Understanding how much error can be allowed in the study is essential to correctly presenting your findings. This is called the margin for error and is usually represented as a percentage detailing how close the sample results should be to the true value. Smaller margins of error produce more accurate results, but also require larger sample sizes. The margin for error is typically expressed in results as a +/- followed by the percentage.
  3. Set Your Confidence Level
    Similar to the margin of error, the confidence level describes and measures how certain the study is about the accuracy of the sample’s representation of the total population. This is expressed as a percentage, with a higher percentage indicating greater confidence; most studies try to operate within the 95%–99% confidence range—less than that could cast doubt on the validity of the results.
  4. Specify the Standard of Deviation
    The standard of deviation details how much variation you can expect from the results of your study. Will the results be very similar, or are they likely to be spread out? Extreme answers where there is a high deviation are often more accurate. It’s generally accepted that a standard deviation of 50% will help ensure a large enough sample size to correctly represent the population within the margin for error and confidence level.
  5. Determine Your Z-Score
    Finally, the Z-score is a constant value showing the number of standard deviations between the average/mean of the population and any specific value. The Z-score corresponds directly to the confidence level, with the most common confidence levels corresponding to the following Z-scores:
Systematic Sampling confidence level to Z score conversion.

Conclusion

Determining your sample size is the first step in any market research project. Whether you decide to use systematic sampling, simple random sampling, or are looking to alleviate voluntary response bias, you’ll need to identify your sample size before you can take those actions—and improve experiences!

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CX 101: What Is a Cluster Analysis? 

Cluster Analysis

Math and numbers are the ultimate in ‘exact science.’ When we work within the confines of mathematics, we can expect absolute precision in our results. In data analysis terms, this can be a real advantage, giving us clear, definite numbers on which to base future decisions. Unfortunately, sometimes the real world being represented by the data is anything but exact. And when it comes to grouping objects based on a somewhat nebulous idea of similarity, traditional statistical tools may fall short. 

Cluster analysis is an answer to this problem. With cluster analysis, data analysts can construct data groups (or clusters) based on a range of similarities and differences. The end goal is to distinguish data points in such a way that those within a group are as similar as possible and completely distinct from the data points belonging to separate groups.

Here, we take a closer look at cluster analysis, how to perform one, how to interpret the data, and what potential disadvantages you should be aware of before you get started. But first, let’s define the term itself.

What Is Cluster Analysis?

At its most basic, cluster analysis is a statistical methodology designed to allow analysts to process data by organizing individual objects into groups defined by their similarity or association. Also called segmentation analysis or taxonomy analysis, cluster analysis exists to help identify homogenous groups with a range of items when the grouping is not already known or defined. In other words, cluster analysis is exploratory; data scientists who apply cluster analysis don’t begin with any predefined classes or expectations.

Instead, cluster analysis takes a collection of data items and attempts to organize them based on how closely associated each one is with the others. Visually, this is often represented using a multi-axis graph to more accurately identify which data points are similar and which are not.

One common example of clustering is the arrangement of items within a grocery store—products are classified and grouped based on how similar they are in purpose.

Cluster analysis is an essential aspect of modern artificial intelligence (AI) and data mining, and businesses often rely on clustering to segment customer populations into different marketing or user groups. Cluster analysis may be used in a range of business and non-business applications.

Steps for Making a Cluster Analysis

There are nearly as many ways to cluster data points as there are groups to segment them into. As such, there is no single process that represents the standard mechanism of cluster analysis. The following process, however, is a reliable set of steps you can use when clustering data:

1. Confirm the Metricality of the Data

For effective clustering, your data needs to have actual numerical values. This is because you will need to define the ‘distance’ between data points. So even if you are working with non-metric data (such as people’s names), you still need to define the similarities in a numerical way (such as by saying that individuals with the same name have a distance defined as 0 and those with different names have a distance defined as 1). 

2. Select Variables

Selecting the right variables is essential to producing relevant, usable cluster data. Perform exploratory research beforehand so that you have a clear idea of which variables to use. 

3. Define Similarities

As with selecting your variables, choosing and defining similarity measures to chart the ‘distances’ between your observations is key to producing a usable cluster analysis. You can define similarities in hundreds of different ways, so be aware of your options as you work with your data. 

4. Visualize Pairwise Distances

With the correct variables in place and your similarities fully defined, you can now begin to visualize your cluster analysis data. You can plot individual attributes as well as the pairwise distances on a histogram chart, with your classes represented as columns on the horizontal axis. Peaks within those columns may represent potential segments.

5. Choose a Method and Number of Segments

Again, there are many different methods one may use to cluster data. You may wish to try a variety of approaches until you find one that clearly represents actionable information in a clear and robust way. Cluster analysis is iterative, so be willing to work with the data until it starts to work for you.

6. Interpret the Segments

With your chosen method and number of segments, your next step is to get a clearer idea of the data points themselves and how they relate to one another. Make note of how the segments differ based on your variables. It can be extremely helpful to visualize these clusters using graphing techniques. 

7. Perform Ongoing Analysis 

With your core data visually represented and your individual data points more fully understood, the final step is to dig down deeper with increasingly robust cluster analysis. This may include subjecting your data to different subsets, distance metrics, segmentation attributes, segmentation methods, or numbers of clusters. By exploring multiple variations, you should be able to see how well your data holds up, how much overlap you have between your clusters, and how similar your segment profiles are across different approaches.

How to Interpret and Measure Clustering

Cluster analysis is based on the assumption that the lower the numerically-represented distance between items, the higher the similarity level—provided that you have a reasonable number of clusters to work with. You can use a silhouette coefficient score to calculate how healthy your clusters are by determining the average silhouette coefficient value of each of the objects in the data set. 

Measuring your clusters also heavily depends on the questions you ask regarding your initial data. Important cluster analysis questions include:

  • How will you measure the similarity between objects?
  • How will similarity variables be weighted?
  • Once similarities are established how will classes be formed?
  • How will clusters be defined?
  • What conclusions can you draw regarding the clusters’ statistical significance?

Advantages and Disadvantages of Cluster Analysis in Sampling

A key application of cluster analysis in cluster sampling. Cluster sampling divides an entire study population into externally homogeneous but internally heterogeneous groups, with each cluster acting as a miniature representation of the whole. The groups must be divided randomly, and then individual groups are randomly selected and every individual in that group is sampled. 

For example, cluster sampling allows researchers to study certain types of communities within the country without having to acquire subjects from hundreds or thousands of different locations. Instead, these communities are divided into similar groups and a random sample of communities is assessed. In this case, the randomly-selected subset represents the whole population. Another example might be an airline that chooses to survey all of the passengers on several randomly-selected flights every day to infer conclusions about their passengers as a whole population. 

Cluster analysis as a sampling methodology offers some clear advantages over more traditional random or stratified sampling. For one, cluster sampling tends to demand fewer resources and is more cost-effective. For another, cluster analysis may be more feasible while still providing a comprehensive view of an entire population. 

That said, there are also certain disadvantages that you should be aware of. Perhaps the biggest drawback is that cluster sampling is prone to higher error rates than many other sampling techniques; the results obtained are not always fully reflective of the population as a whole. Additionally, unconscious biases may seep into this sampling methodology creating biased inferences about the entire population.

Better Analysis with Pearl-Plaza

If you’re interested in getting a clear picture of the similarities and differences across a data set, then cluster analysis may be the answer. But ensuring that your cluster data accurately represents your sample group and clearly expresses valuable information can be difficult. Understanding cluster analysis and cluster sampling methodologies and how best to interpret the resultant data will provide you with the insight you need to understand the associations between your objects. 

Pearl-Plaza, the leader in people-oriented text analytics, can help. Built on industry-recognized metrics and real-time intelligence, Pearl-Plaza provides the tools and support you need to find hidden insights in your data. For more information on data gathering and analysis, visit our Learning Hub.

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The 2024 Consumer Trends Report

This isn't your average trends report – read on to see how your business can lead the charge into a new era of CX

10 Steps to Easily Build An Integrated Customer Experience Program

A game-changing guide for your team on how to build & implement a competitive, fully-stacked integrated CX program.

What Is the Difference Between Voice of Customer and Market Research?

What is the Difference Between Voice of Customer and Market Research

A lot of folks believe that voice of customer (VoC) programs and market research mean the same thing—but they’re actually quite different! In fact, each discipline differs in purpose, design, analysis and outcomes.

However, even though they’re different, it’s important to point out that one isn’t necessarily better than the other—and brands need both if they want their customer experience (CX) programs to reach their potential.

So, with that in mind, let’s get into a quick primer!

Breaking Down the Difference Between Voice of Customer & Market Research

What Is the Definition of Voice of Customer (VoC)?

Voice of the Customer (VoC) is the process of gathering vital information regarding what customers think and feel about their experiences with a business.

How Does VoC Fit into Your CX Strategy

VoC programs are an essential part of any CX toolkit. They’re designed to fulfill many critical functions for your overall customer experience program, including, as their name implies, understanding customer needs. They’re also useful for understanding customer expectations, as well as what those individuals may want from you before even they know. This information can then be used to adjust operations, inform marketing efforts, and help your organization create both short- and long-term Experience Improvement (XI).

Not all VoC feedback comes from typical listening methods like surveys and focus groups, either. A lot of it comes from unsolicited feedback (website reviews, social media comments, etc). Unsolicited feedback is helpful because it gives customers a chance to express themselves entirely in their own terms, which may alert brands to problems and journey breakages that they weren’t aware of.

All of this boils down to the ability to not just capture individual and collective customer feedback, but act upon it. Taking action is crucial to Experience Improvement and building connective relationships.

What Is the Definition of Market Research?

Market research explores hidden relationships within industry data, collected by a market research firm, in order to predict and forecast future events and behavior within the market.

What Is the Role of Market Research in Your Business?

While Voice of Customer is all about feedback, market research takes a slightly wider lens by focusing on understanding the trends around your business.

Primary research is useful for testing new communications and services that your company wants to put out there, while secondary research looks at the dynamics and sizing of the marketplace around you. Conducting these types of research can help your company identify your target market, segment your customers, and identify growth opportunities.

Your company can supercharge its market research efforts by defining the population you want to target with a survey, then creating samples that ensure you’ll have a match. We’ve found that surveys like these are most effective when they’re blind, meaning that the customer or individual stays anonymous while taking them, and challenge you to do the same! This method is great for reducing response bias.

The Difference between Voice of Customer (VoC) and Market Research
This handy chart breaks down the differences between these two methods

So, Why Do You Need Both?

VoC and market research aren’t the same, but your CX program and your organization need both in order to truly understand your customers as people. That fundamental, holistic understanding fuels unforgettable experiences that build loyalty while also creating additional revenue! So be bold in your strategy and use both VoC and market research. Your customers will feel heard, your C-suite will be impressed, and the experiences you provide will be meaningfully transformed.

Click here to read our full-length white paper on why your brand needs both VoC and market research. Our very own Eric Smuda has spent decades in both fields and provides an in-depth look not just at why these disciplines are important, but how your organization can wield them effectively.

Watch the Video – Get More Out of Your CX Program

Watch this demo video to see how Pearl-Plaza’s award-winning AI tech does the heavy lifting on analysis so you can spend more time lifting CX results.

The 2024 Consumer Trends Report

This isn't your average trends report – read on to see how your business can lead the charge into a new era of CX

10 Steps to Easily Build An Integrated Customer Experience Program

A game-changing guide for your team on how to build & implement a competitive, fully-stacked integrated CX program.

CX 101: What Is Primary Research?

Primary Research

If you want to get to know someone, the best way to get an accurate assessment is to ask them questions yourself. You may want to know what they like and don’t like, what makes them happy, sad, or angry, how they feel about specific topics, or anything else that gives you greater insight into their personality.

The same is true when you’re trying to understand market research; having an accurate assessment of your audience and their buying patterns will make all the difference when it comes to customer satisfaction and business sales. You need to know what makes them excited or frustrated, how they respond to specific issues or perspectives, and topics that are relevant to them, all so that you can better understand and help them. This also provides a better grasp of industry trends and challenges so that organizations can offer memorable and fulfilling content, experiences, and products.

The best way to get accurate data, though, is to gather it yourself whenever possible. That’s where primary research comes in. This guide will cover everything you need to know about primary research and how you can capitalize on the many opportunities it provides.

What Is Primary Research?

Primary research is a methodology of research that requires you to collect data yourself (or commission someone else to conduct the research), meaning you are not using someone else’s research or data. From detailed surveys to intensive focus groups, primary research allows you to directly examine, explore, and record how your audience responds to or feels about certain subjects.

Businesses use primary research for a variety of reasons, such as discovering what their clients or customers need from their product or the kind of language that speaks to their target audience. Organizations also use this kind of primary research to improve the experience of both customers and employees and optimize their service. 

Primary Research Methods and Examples

The more accurate data you can gather, the better prepared you will be for the data-driven world that businesses run on. Here are some methods of primary research to help you discover the kind of data you want to gather and how you should go about your research.

Surveys

Surveys are versatile and, when well-made, incredibly useful ways to gather quantitative information. They are a quick way to get honest opinions or preferences from both customers and employees without demanding too much work from them. Plus, online and automated surveys make it extra easy for both the participants and the researchers—they can be conveniently sent via email and accessed on all sorts of devices.

Surveys should have a variety of open-ended and closed questions so that participants have the opportunity to give greater details for their answers while also providing more numerical data. You want to keep your survey short and focused. That said, you can use a rating scale, multiple choice, ranking, and drop-down questions, among many other types of questions.

Make sure you have a predetermined theme that you can tie each question back to, as well as a target audience that will give you the most relevant responses. For example, let’s say you just launched a new navigation layout on your online store website and want to know how it’s being received. You would need to create questions all related to the usability, convenience, and flow of the navigation. You could do this by sending the survey to someone who just made a purchase and asking them to rate their experience or specific elements of your online store.

Interviews

Interviews are a much broader way to collect information, and unlike the quantitative nature of most surveys, interviews are more qualitative since they are usually created with open-ended questions. You can do interviews in person and face-to-face, or you can do them over the phone. You can usually get more in-depth answers with interviews depending on the interviewer and their experience with interviewing. Great interviewers can especially get great results for in-person interviews.

If you need large amounts of information for a relatively short list of participants, interviewers are an excellent option. They can last 10-30 minutes or sometimes longer depending on the nature of the work you’re doing. Just know that in-person interviews can influence the comfort level and the responses of interviewees, so an expert interviewer will be able to create the right environment and read the room to accurately record the responses.

An example of a good interview opportunity would be if you need to gather information about a certain subject from experts. Imagine a company that develops equipment for a specific medical condition and needs to improve its current model. This company could interview both the specialists and doctors who assist patients as well as the patients themselves to get concrete answers directly from the source.

Observations

If you need to collect data but don’t want to directly interact with “respondents” in order to protect the accuracy of the data, then observations may be the best route for you. While it can be difficult to create these scenarios, observation primary research is a method where there is no direct interaction between the researcher and the person being observed. All the researcher does is observe and record how the target group or person reacts or responds.

There are usually either trained specialists who know what to look for while observing or people who use cameras to document the experience. This is especially good for removing as much bias as possible.

Let’s say a restaurant offers customers a complimentary appetizer if they have to wait longer than an hour. That restaurant could install cameras and study how many people wait, how long they last if they decide to leave, the size of groups that are willing to wait, etc.

Focus Groups

Focus groups are small groups of people, usually no larger than 6-10 individuals, who come together and discuss questions that are provided by a moderator. This is ideal for a group of experts who can discuss a topic at length or even a group of customers who can offer greater insight into your product. This is a good opportunity to identify pain points, niche parts of an industry, or how well people respond to a new idea or product.

Maybe you want to improve your employee incentive program—you could put together a small focus group within the company to start a discussion about potential benefits and see what people react well to. Or, let’s say you’re launching a new product but aren’t sure how your target audience will receive it. A small focus group could interact with or use your product and then offer feedback in a focus group.

Research Services & Data Analysis

It’s one thing to conduct a survey or an interview, but it’s another thing to process, analyze, and act upon that data you gather. Even putting an effective research project together can be overwhelming for businesses, which is why research services are another method of primary research. You can hire a team of experts or find a program that quickly compiles your data into usable statistics.

This is also best if you need help with data analysis, which is the process of inspecting and screening your reports for objective, accurate, and insightful data. This can be a huge project, which is why experts who know how to categorize and analyze data are particularly helpful for businesses and organizations.

Advantages and Disadvantages of Primary Research

Here are some major benefits of primary research as well as certain challenges to be aware of.

Advantages

  • Accuracy and Relevance: When you conduct the research yourself, you don’t have to worry about the work or bias of other researchers—you account for everything, which means you gather exactly what you need. 
  • Control: Every step of your primary research is intentional, meaning you have more control over the kind of results you get. Decisions are made at your own discretion, and you don’t have to worry about citations or relying on other sources.
  • Up to date: You don’t have to rely on outdated sources or statistics. Instead, you get just what you need for your specific goals at the time you need it.
  • Improved customer experiences: Primary research is also particularly beneficial for your customers and clients since you are directly improving the customer experience with your business offering. You can conduct market surveys in-house by using the Pearl-Plaza platform to make the most of your survey analysis.

Disadvantages

  • Resources: Not everyone has the time or the money to conduct their own research. Planning, executing, reporting, and analyzing the data you gather is expensive and demanding. This can prevent both the quality and the accuracy of work if you use poor resources, cut corners, or rush the process. 
  • Feasibility: Especially when poorly designed, not all primary research projects are realistic. Interviewing every member of your staff of 500, for example, isn’t a reasonable goal.
  • Research Bias: Even though you get to customize your research, you also have to be especially careful when it comes to objectivity. Your opinions, assumptions, and preferences must not get in the way of accurate research, which isn’t always easy. Sometimes, an unbiased third party can help businesses accurately gather and analyze their data.

Primary Research Vs. Secondary Research

There are two core types of research: primary and secondary. Primary research is a powerful tool for businesses; however, secondary research, which is research that you don’t conduct yourself but gather from other sources, shouldn’t be dismissed. In fact, the best scholars and businesses use a combination of primary and secondary research to round out their perspectives.

Using both primary and secondary research is what gives you a comprehensive report of your findings—the more reliable sources that support your findings, the more credible and usable your data is. Sometimes, primary research is mainly done to supplement or confirm findings done in secondary research.

For example, a tech company may want to update its work-from-home policy. They may find secondary research that offers some insight into what employees prefer, but they could also do their own primary research to get specific information from their own employees that is accurate and up to date.

The bottom line is that primary research and secondary research are both more rewarding and useful when used in conjunction with each other, not in competition. Primary research will give you data or information specific to your concerns, company, customers, industry, etc. Secondary research may offer applicable insights into your questions and concerns as well, though there are limits to how directly the information relates to your niche goals.

Primary Research with Pearl-Plaza

If you want to be competitive in your field, encourage honesty and authenticity among your employees, and produce effective market research, it’s time to use Pearl-Plaza. You don’t have to be an analyst, a scholar, or a mathematician to do your own primary research—you can trust the expertise and advanced technology of Pearl-Plaza platforms to simply but powerfully compile your needed data and take your business to the next level.

Primary research is invaluable when it comes to market research and giving customers the best possible experience with your brand. Learn how Pearl-Plaza can help you gain the most insight out of your primary research surveys!

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CX 101: Sampling Methods

Sampling Methods

When you want to get information from customers, it might seem nice to be able to ask every single customer. To make that happen, you would need every customer to agree to be surveyed, and it would take an extreme amount of time, effort, and money to then ask every customer your survey questions. Even then, you would have an inordinate amount of data to sift through. It’s true that you could definitively make claims about what your customers are saying, but it’s not actually necessary to go through this level of work. In fact, most likely, it’s not possible to survey every single customer.

Instead of surveying every single person you want feedback from, most people use a concept called sampling instead and rely on sampling methods to research a group. Sampling allows you to get information from a group of people, and when done correctly, the information is also generalizable and usable. We’ll walk you through sampling, types of sampling methods, and how to begin using some of these techniques. 

What Is Sampling?

Sampling is using a group of your population to understand the population as a whole. Think of sampling as you would with sampling a cake. To see if a whole cake is delicious, you can usually tell by eating a slice of the cake. That slice of the cake can tell you a lot about the taste, texture, consistency, and overall balance of the cake—and it’s much easier to eat just a slice instead of an entire cake. Sampling for surveys works much the same way. 

You take a group of your population and survey just them. It’s typically much more manageable and affordable to do so when you’re doing large scale research. From there, your data team will be able to analyze the data from the sample—which is typically a smaller amount that’s easier to glean important insights from. The insights from sampling—if your sampling is done correctly—can then tell you about the whole group you’re researching. And it can help you gather these insights at a fraction of the cost and much less effort than it would take to survey the entire group. 

Difference Between Population and Sample

To better understand sampling methods, it’s important to distinguish between the population and the sample. The population is the entire group of people you want to learn about and to be able to draw conclusions about. For example, if you wanted to determine how your customers felt about a new product, your population would be every single customer that’s purchased the new product from you. If you wanted to research the grocery shopping habits of single mothers, your population would be every single mother. 

The sample is a representative group of your population that will be participating in your research or survey. The key is that the sample has to be an accurate representation of your population. For example, if you were researching the grocery shopping habits of single mothers, you couldn’t go to a local grocery store and survey every person who walked in. You would get data, but it wouldn’t be data about the population you’re trying to study. As with the cake analogy, the sample or slice has to accurately represent the entire cake. 

It’s important to remember that population doesn’t necessarily mean “big” and sample means “small.” Populations can be defined by so many factors: geography, age, gender, income, and so many more factors. You can have a tiny population of just a particular set of customers or a large population like the entire adult population of North America. The larger, more dispersed, or more diverse your population is, the harder it will be to sample. 

What Are Sampling Methods?

When you want to do a survey or perform research, you’ll need to use sampling methods to determine who will be a part of your sample and how it will be related to your population. Carefully consider how you will select a sample that is as representative of your population as possible. In general, there are two categories of sampling methods: probability sampling and non-probability sampling. 

Probability sampling is when each member of the population has an equal chance of being selected to be included in the sample. The sample participants are chosen randomly, and the results from the survey are generalizable to the population as a whole. Probability sampling methods are typically more accurate than others, but they are also more time consuming and expensive to make possible. 

On the other hand, non-probability sampling is when each member of the population does not have a chance of being selected. With these sampling methods, you could choose your sample based on convenience or other limiting criteria that make it so that every person isn’t eligible to be selected.

For example, if you wanted to study all of your customers, it would be a non-probability approach to then just select a sample of customers who have subscribed to an email list. In this situation, you would be limiting who could be selected to those on a list, which may or may not be accurate to your entire population. With non-probability sampling, it’s generally much more affordable and easier to do research, but you do run the risk of accumulating higher amounts of sampling error and reducing the likelihood of having a generalizable sample. 

Probability Sampling Methods

To perform a probability sampling survey, there are several methods that are commonly used. These are some of the most commonly used probability sampling methods: 

Simple Random Sampling

Simple random sampling is the simplest way to get a sample where every member of the population had an equal chance of being selected. To do a simple random sample, you will choose a way to randomly select a certain number of people from your population to survey. Some common methods include using a random number generator, drawing a name out of a hat or bowl, or any other type of chance. 

For example, you could number each customer you’ve had and use a random number generator to determine who will be a part of your sample. You could use a list generator to select certain customers from a list of names or emails. However you do it, the key is that it’s random. 

Systematic Sampling

Using simple random sampling can be extremely time consuming with a large population, so many will instead use systematic sampling. Systematic sampling is using some sort of designated system to choose randomly. For example, you could number all of your customers and choose the tenth individual. Choosing systematically saves you time and effort but still provides you with a random sample. 

Stratified Sampling

Stratified sampling is most useful when you have groups of people who should be sampled from equally. First, you divide your population into groups that don’t overlap (i.e. people from one group can’t be in another group). From there, you’ll randomly select a sample from each group. 

For example, if you were looking at your customers, you might want to break them up by annual income to see if that affects what you’re researching. Your stratified groups would then be done by income, and you would select a small sample from within each group. 

Cluster Sampling

Cluster sampling also involves splitting your population into groups, but these groups should be split randomly if possible. Then, instead of selecting from each group, you will randomly select groups and sample everyone in the group. For example, an airline might randomly select a certain number of flights each day and survey every passenger on those flights. 

Non-Probability Sampling Methods

Since probability sampling can be time consuming, some people will use non-probability sampling methods instead. These methods are generally not generalizable to the whole population as they may or may not be an accurate representation of the population. 

Convenience Sampling

Convenience sampling is choosing a sample based on ease of access. Instead of choosing from a population randomly, you choose from a population based on who is easy to communicate with. For example, standing in front of a grocery store and surveying everyone who walks past is convenience sampling. Not every member of your population has an equal chance to be chosen, and your data will only represent one day at one grocery store.

Choosing customers based on being subscribed to newsletters or who follow your company on Instagram could also be convenience sampling (if your population is larger than just “those who follow us on Instagram”) because it’s all about ease of access. 

Voluntary Response Sampling

Voluntary response sampling is when you select a sample based on who wants to be a part of the sample. The individuals can voluntarily choose to respond or not respond based on a general call for responses. For example, you could send out an email to every customer and ask them to join the study. Those with strong opinions or interest would be the most likely to join, which could mean your population isn’t representative. 

Purposive sampling

Purposive sampling selects a sample based on what a researcher decides. Essentially a researcher will be the one to determine if someone is in the sample or not. For example, you could put out a survey, and the researcher would then only look at the surveys for people who they decided met a certain criteria: like having purchased the most recent product. 

Snowball Sampling

Snowball sampling is used when a population is hard to reach. For example, if your research requires data from shelterless people, you may have a hard time reaching them for a survey. Snowball sampling is when you use just a few individuals you can find from this group or even choose participants based on whose family or associates you can contact. While snowball sampling isn’t random, it can be useful for certain populations that you may not be able to survey in another way. 

The Bottom Line

Overall, there are many sampling methods to choose from when planning your surveys. The end goal is to try to get your sample to be as representative as possible of your overall population, so you can use the results to generalize about the population and make conclusions. Poor sampling will give poor results. After all, as we all know, if we put crappy data in, we get crappy results, which don’t benefit anyone. Choose a representative sample instead for beneficial results. 
See how Pearl-Plaza can help you with your sampling and survey efforts to help you choose the right sampling methods to get a representative sample.

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The 2024 Consumer Trends Report

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A game-changing guide for your team on how to build & implement a competitive, fully-stacked integrated CX program.

3 Ways Market Research Supercharges Experience Programs

customer experience research

Market research is seen by a lot of companies and organizations as a nice-to-have. The reality, though, is that it’s a necessity. Market research provides the mapping tools you can use to chart your business landscape, understand its various features, and more importantly, get to know the groups and audiences that populate it. 

This goal is especially important within the context of experience programs, especially if you want to achieve real Experience Improvement (XI) for your customers, employees, and overall marketplace. Today’s conversation covers three specific ways market experience (MX) and its research can make a difference for you when it comes to becoming a leader in your vertical:

  1. Identifying Audience Segments
  2. Understanding Unsolicited Data
  3. Identifying Emerging Trends

#1. Identifying Audience Segments

There are many experience vendors out there whose approach revolves around two audience groups: new customers and existing customers. Those two audiences are important, of course, but so too are the litany of other customer and non-customer groups that populate your marketplace landscape. Identifying each segment and the amount of business it does with your brand is key to understanding both your vertical and how to become its leader.

More specifically, MX tools and platforms enable you to identify not ‘just’ your loyal customers, but also non-customers, past customers, and individuals who cross-shop with you and with competitors. You can then use your tools to research why these segments shop with you as often as they do (or don’t) and, more to the point, what you can do to turn them into loyal, invested customers.

#2. Understanding Unsolicited Data

Audience segments aren’t the only experience program element that market experience and market research can dramatically broaden. Another key component of understanding your marketplace is looking for and understanding unsolicited data, which MX tools can also help you achieve. Solicited data is important, but as with audience segments, understanding all of the data out there, solicited and otherwise, is the only way to truly understand your vertical.

As a quick reminder, unsolicited data refers to sources of information like social media and what you can infer from your market research. You can find this data once you’ve identified where the audience segments in your marketplace like to shop, which will lead you to more of the data you need to understand them. In other words, adding unsolicited data to the mix helps turn your picture of your marketplace from a snapshot to a landscape portrait.

#3. Identifying Emerging Trends

One of the secrets to building a truly remarkable customer experience isn’t ‘just’ being able to quickly react to problems or listen intently to your existing customers—it’s the ability to understand what all of your customers and audiences will want before they themselves even know. That’s the true power of market research, and it’s a culmination of understanding your vertical from both an audience segment viewpoint and a data one.

Once you’ve nailed down how to identify and capitalize on emerging trends, you’ll be well on your way to marketplace leadership if you’re not there already. Customers respond to experiences that make them feel seen and known as humans, and an MX-fueled initiative will create that Experience Improvement for them.

Taking a Closer Look

Knowing that these MX tools, methodologies, and possibilities is one thing—what’s the next step for understanding more about them and how to leverage them to your organization’s advantage? Click here to read my full-length Point of View on this subject to learn more about what market experience, market research, and all their tools can accomplish for you!

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Pearl-Plaza’s Modern Market Research and Data Analytics Approach Ranks in Top 50

Pearl-Plaza Market Research Solution

In the latest 2021 Insights Association Top 50 Market Research and Data Analytics report, Pearl-Plaza ranks in the top 20 established industry reports and market research or market experience (MX) brands, alongside other powerhouse brands such as JD Power, Gartner Research, and Forrester Research Services. 

About Pearl-Plaza’s Market Research and Data Analytics Approach

With the help of our industry-leading data and research science capabilities, we’re able to help brands go beyond collecting data to reveal actionable intelligence that leads to Experience Improvement (XI). Our “full-service professional CX approach designed to continuously improve the customer experience and deliver business outcomes to an impressive list of clients that includes 90 percent of the world’s automotive companies: 8 out of 10 of the top banks, nearly 20 percent of the top 50 retailers, 40 percent of the top hospitality companies, and 4 out of 5 of the top insurers.”

And this isn’t the first time we’ve been ranked on this list. In fact, Pearl-Plaza has endured the test of time to be ranked on this report regularly over the past two decades. Here’s why: Pearl-Plaza goes beyond a traditional approach to pursue what we call modern market research. So what’s the difference and what does it entail? Keep reading to find out.

The Difference Between Traditional & Modern Market Research

The difference between traditional market research and Pearl-Plaza’s modern approach is that we focus on providing brands with access not only to insights, but to actionable intelligence that opens the door to concrete change. Too often traditional approaches fail to go beyond observing and reporting trends. How can brands expect experiences to improve if the research insights aren’t being used to create actual organizational progress?

How Pearl-Plaza’s Approach Enables Action

So if part of modern market research is taking action, what does that look like? By focusing on stakeholder engagement and journey mapping, businesses can become more proactive about utilizing their research. Having buy in from your executive boardroom allows research teams to develop projects related to organizational goals and drive their insights into action. And understanding how the customer and employee journeys interrelate can guide that collaborative process into a more honed business strategy.

What Modern Market Research Strategy Looks Like

But what about the research strategy itself? Modern market research combines marketing science and research consultancy to make the most out of data. After journey mapping and capturing customer insights, Pearl-Plaza supplements that data with financial, operational, employee, social media, etc. data. This new approach means reaching for multiple sources of insights and synthesizing that information to allow organizations to take practical action.

Pearl-Plaza is dedicated to continuing to be a leader in this space because we believe these initiatives are essential to creating deeper experiences between our clients and their customers. 

Learn more about the Pearl-Plaza XI difference and our market research and data analytics solutions here.

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Three Elements That Make Traditional Market Research Inadequate for Experience Improvement

Market Research

What do you think of when you hear the term “market research”? For many brands, it brings to mind putting together scorecards, reporting numbers, and a heavy emphasis on looking to the past. Those things are certainly important, but they’re insufficient for creating true Experience Improvement (XI) and getting your organization to the top of your market. Let’s get into greater detail about why those three elements alone don’t cut it for modern market research.

  1. Numbers
  2. Scorecard Reporting
  3. Living in the Past

Element 1: Numbers

Right up top, I don’t want to give you the impression that numbers are unimportant. They fuel your data and, of course, quantify shifts in the trends most important to your experience initiative. However, that’s about the only story numbers can tell. They can reveal whether a trend has fluctuated and in what direction… but they can’t tell you why. Therefore, relying solely on numbers means that your research is only telling part of the story. Identifying the root causes of the trends you’re seeing is the only way to actually make a difference with your experience program.

Element 2: Scorecard Reporting

This element dovetails somewhat with focusing solely on numbers, but it speaks to the importance of how you present your research findings to other teams and the C-suite. Scorecards can be a  handy way to quickly present your findings (especially to an executive with a more quantitative thinking style), but what about the C-suite members who are qualitative? What about the chance to present the impact of your research and your program in a more connective, human light? Illustrating your program with this kind of storytelling, not just scorecards, makes a huge difference.

Element 3: Living in the Past

Ideally, market research should be a GPS, not a rearview mirror. Unfortunately, a lot of research teams view their work solely in the context of the past. Being mindful of the challenges your organization has endured is helpful for looking to the future, but what if there was a way for research to be proactive about the future in real time? That sort of research process is incredibly powerful, and it can help you create the bolder, more human experiences you need to stay on top of your market.

The Path to Modern Market Research

If the elements I’ve discussed are insufficient for tapping into the true power of market research, what can brands do to supercharge their research efforts and make it an invaluable part of their overall experience strategy? Click here to read a full-length point of view document on the subject, where I discuss how to modernize your market research program for the Experience Improvement age. I challenge you to read on even if your research initiative differs from what I’ve discussed—some of my insights may still surprise you!

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