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!

Two people and a dog inside of a business showing customer loyalty.

Many companies underestimate the value of customer trust and loyalty when it comes to driving higher revenue growth. It might sound counterintuitive, but convincing existing customers to return is more important than gaining new ones. This is because the cost of finding new customers is far higher than the cost of selling to existing customers. In fact, returning customers spend 67% more than first-time buyers.

It’s clear that executives need to put customer loyalty at the center of their company’s values, but how do you actually go about doing building customer trust and loyalty? Let’s jump right in!

3 Ways to Build Customer Trust and Customer Loyalty

  1. Create Personalized Experiences to Build Trust
  2. Go the Extra Mile to Listen and Understand
  3. Quality, Quality, Quality

Action #1: Create Personalized Experiences to Build Trust

Throughout the customer journey, your brand should meet customers where they are. The more personal you make the customer experience, the more trust you’ll cultivate. 

For instance, in the pre-purchase stage, in-store employees should have substantial knowledge about products and understand what customers need. Employees should be trained to create positive interactions from the beginning all the way up to the final moment of purchase. Asking small questions like if a customer found everything they needed—and stepping in if they didn’t—can make a huge impact. Little actions like that help add a nice personal touch to a customer’s experience—and lead to a stronger level of trust!

Action #2: Go the Extra Mile to Listen and Understand 

Trust often leads to loyalty, but your brand has to make the first move. To cement a longstanding relationship of trust, your business needs to show loyalty to customers first. 

An effective approach here would be to engage with and respond to customers, because engaged customers are more likely to promote your company than unengaged customers. Actively responding to customer questions, comments, and complaints can grow loyalty by putting a human voice to a brand. 

One best practice for engaging with customers in this way is to design an open communication and feedback channel. Of course, we recommend utilizing not just a help center as a method to reach out, but any adequate resource, from employees on the front line to digital surveys. Additionally, you should look to other indirect forms of feedback to understand your customers such as review site data and social media mentions.

Action #3:  Quality, Quality, Quality

At the end of the day, even if their customer experience was amazing, if the product doesn’t meet a customer’s expectations, all that work you did to build trust and loyalty is in vain. Customers expect value for what they pay for and no amount of sales gimmicks can hide the truth of your product, so it’s key to know customers’ expectations and develop your product/service to meet or exceed that. After all, loyal customers are coming back for a quality purchase; the positive customer experience is an additional element encouraging that return. 

Your customer experience platform is essential to identifying friction points and remedying them to improve customer trust and customer loyalty. We discussed in the section above how it’s important to keep tabs on what your customers are saying about their experience. Once you’ve collected all this feedback data across every channel, you can leverage your customer experience (CX) platform to analyze all that customer feedback and identify the areas in your business that need some attention. Check out this video below to learn how global banking giant, Virgin Money, worked with InMoment to understand the most impactful moments in the customer journey.

Now that you’ve learned how to build customer trust and loyalty, read our eBook to learn about how that trust and loyalty can drive cross-sell and upsell opportunities!

CSAT Score

A CSAT score is a commonly used customer experience (CX) metric that helps a company build a relationship of trust and understanding with its customers. A successful organization knows that a key element of success is a loyal foundation built within its customer base.

On paper, it may seem simple, but in reality, many companies struggle with building a customer-centric foundation within loyal customer relationships. One way to build and maintain solid relationships is to establish a channel for customers to communicate experience feedback to the company. 

The goal of a successful organization is to fully grasp what customers value—as well as dislike—regarding their experience with a business. A proven channel to understand customer sentiment is by implementing customer satisfaction surveys.

What Is a Customer Satisfaction or CSAT Score?

CSAT is short for customer satisfaction score. It’s a commonly used measurement tool that acts as a key performance indicator for customer service and product quality. CSAT is a general term understood, recognized by, and beneficial to a wide variety of industries. 

While customer satisfaction as an idea is a general one, CSAT is a more defined and specific metric that is expressed as a percentage. These more defined metrics can present as any of the four types of CSAT surveys:

#1: Customer Satisfaction Score (CSAT)

#2:  Net Promoter Score (NPS®)

#3:  Customer Effort Score (CES)

#4:  Milestone Surveys

In this blog, the spotlight stays on what CSAT is, but it’s beneficial to have at least a brief understanding of the three other types of CSAT surveys to have a full grasp of the entire concept and how customer satisfaction can play a major role in organizational decision-making processes.  

Net Promoter Score:

Net Promoter Score, or NPS, is a metric that indicates the willingness of customers to promote a company’s product or services and separates customers into promoters, detractors, and passives. While NPS is a valuable indicator of customer satisfaction, it also can surface pain points that can lead a company to revise, shift or fix friction points in the customer journey to encourage positive customer experience that leads to customer promotion. 

Customer Effort Score:

Customer Effort Score, or CES, is a specific customer experience survey metric that enables companies to track the ease of or effort required within the customer interaction with a product or service within the duration of the customer relationship with the company. In measuring CES, organizations gain insights and data that enable business improvements in and around the customer experience to uncover laborious friction points that act as roadblocks to customer interactions.

A Milestone Survey: 

A Milestone Survey is a questionnaire sent out at specific moments throughout the customer journey to help organizations better understand the customer experience over time. A milestone can either be triggered by time (after 30 days) or by experience (after onboarding).

What Types of Questions Would a Customer Satisfaction Survey Include?

When drafting a CSAT survey, it is wise to consider including a variety of questions that explore the different aspects and relationships surrounding the customer’s journey with a product or service. 

The five most beneficial and key question types that provide a wide and valuable range of insight that will likely benefit any organization are:

  • Product Usage Questions
  • Demographics Questions
  • Psychographics Questions
  • Satisfaction Scale Questions
  • Open Ended Text Questions

Product Usage Questions:

Product Usage Questions explore when and how, and why customers are interacting with your product; they will be critical in identifying customer expectations and needs that lead to product engagement. Product usage questions point to the value or lack thereof that a product provides. Listening to the voice of the customer regarding our product will provide clarity and insight beyond what controlled studies can provide. With product usage data, companies can get a more complete picture regarding functionality and value its product provides. 

Demographic Questions:

Demographic Questions can help you determine a variety of components within a population’s characteristics that may influence a customer’s decisions surrounding your product. It’s common within demographic questions to gather information about customers’ income level, marital status, gender,  identity, and geographical location. With product usage segmented data, companies are better informed to make wise business decisions on how to target a specific demographic by utilizing the data from their customer feedback. This data-based information may help you develop products and or services that relate more specifically to customers.

Psychographic Questions: 

Psychographic Questions will give valuable insight into the customer lifecycle by providing you with information about unique customer specifics. Psychographics describe human traits that could include: hobbies, interests, likes and dislikes, goals, values, lifestyle, or physiological tendencies. Psychographic survey questions may help measure the personality traits and tendencies of a customer’s preferences regarding a company’s products and services.

Satisfaction Scale Questions:

Satisfaction Scale Questions may promote feedback and curb survey fatigue as the structure of the question can be an easy way for customers to give an overall sentiment of their experience with the company or product using a single click. The satisfaction scale asks, on a scale of 1-5, or 1-10, how satisfied a customer is. This scale can be as general or specific as the feedback a company would like to receive and can provide companies direction on who their promoter and detractor customers are and allow them to target them according to their overall satisfaction sentiments. 

Open-Text Questions:

Open-Text Questions allow customers more freedom to share and expound upon unique experiences and assist in gathering highly valuable responses not available in other forms of questioning. Open-text questions are relatively harder to analyze as data sets and require text analysis, but they help capture subjective and individualized experiences regarding the customer journey better than other forms of questioning. 

How Do You Calculate and Measure a CSAT Score?

CSAT score is a customer feedback metric measured via one or more variations of this question: “How would you rate your overall satisfaction with the products or service you received?” Respondents use the following or similar 1 to 5 scale:

  1. Very Unsatisfied: ( Where a customer would be considered a detractor)
  2. Unsatisfied: (Where a customer would be considered at risk of churn)
  3. Neutral: (Where a customer would be considered a slight risk of churn)
  4. Satisfied: ( Where a customer would be considered a promoter)
  5. Very Satisfied: ( Where a customer would be considered a promoter)

CSAT scores are usually developed into a percentage scale, with 100 percent representing complete customer satisfaction. The results of a CSAT survey can be averaged out to give a Composite Customer Satisfaction Score by taking the number of satisfied customers and dividing them by the number of surveys received. CSAT Surveys remain the most successful way to elicit customer feedback and come in a variety of forms and templates. Customers may be accustomed to a traditional questionnaire or website form, but more and more, the less traditional format, such as a popup, an app, via text message, or other unconventional methods, are being adopted as users can easily navigate these methods within the comfort of their touchscreen devices.  

Why Are Customer Satisfaction Surveys Important?

When customers feel their voice is being heard, they are more likely to communicate their unique and personal experiences with a product or service. In the event of negative experience feedback, customers are more likely to stay if they can see their experience remedied. Unsatisfied customers have the potential to become loyal promoters after being retained from negative feedback, and a well-timed customer satisfaction survey can hone the customer journey and improve satisfaction, retention, loyalty, and likelihood to repurchase.

Moving Forward with CSAT Surveys

Occasionally customer satisfaction feedback will provide a descriptive experience, especially if open-ended questions are included, but most customer feedback will be brief, concise, and to the point. Whatever form the feedback takes, the notion that any feedback is better than none remains, and the CSAT score is the best way to receive,  sort, and take data-based action to improve. 

Utilizing CSAT scores and insights wisely can lead to a positive influx of new customers and contribute to well-maintained relationships with existing ones. 

Sign up for a demo today with InMoment!

Simple Random Sampling

If you are an organization with thousands of customers, surveying your entire customer base can seem like a daunting task. The likelihood of all of your customers (whether they number in the thousands or in the hundreds) answering a survey is slim to none. But, customer feedback is critical to driving Experience Improvement and growing your CX program. So, with such slim odds, how are you supposed to trust the customer feedback you do get? 

Simple random sampling is the perfect solution to this problem. Sampling methods such as simple random sampling allow businesses to get the data they need to make decisions without having to go through any unnecessary work. The goal of using simple random sampling is to get a small group that is unbiased representative of a larger population.

What is Simple Random Sampling and Why Is It Important?

A simple random sample is a selection of participants from a population. What makes this sampling method different is that each participant has an equal probability of being selected.   

Simple random sampling is important for many reasons. First, the subgroups from your population will be unbiased. Since they were chosen at random, they do not have any predisposed bias as participants, and they do not suffer from researcher bias. 

It is also important because these unbiased groups allow businesses to get data that is reflective of the entire population, without actually having to survey the entire population. 

What Are the Advantages and Disadvantages of Simple Random Sampling?

Many businesses prefer simple random sampling for its simplicity and lack of bias. It is also the easiest form of sampling. You don’t need to be a data analyst in order to perform this sampling method; and the data you receive can be applied to the whole population. 

The biggest disadvantage to be aware of is researcher bias. Researcher bias occurs when the researcher conducting the sampling selects participants to be in a subgroup based on their personal biases. This can be easily avoided by including multiple forms of random selection in your sampling. 

How Do You Perform Simple Random Sampling?

Simple random sampling is the perfect tool for a company looking to get an idea of how its entire customer base feels about a certain subject. Let’s say that you work for a nationwide retailer, and are interested in finding out how your loyalty members feel about a new selection of loyalty perks that you are considering to develop. 

You could go into the database that has a list of all of your loyalty members or, in this case, the list of your population. After assigning each member a number, you could use a random number generator to randomly select the number of participants you have chosen for your sample size. 

This subgroup of members, chosen at random, can now be surveyed. Their responses can be analyzed and can also be viewed as being representative of your entire member base. 

Sampling With InMoment

If you’re interested in understanding your customer base without having to survey each and every customer, then simple random sampling may be your answer. But understanding that data, what it means, and what to do with it moving forward can be difficult. 

InMoment, the leader in people-oriented text analytics, can help. Built on industry-recognized metrics and real-time intelligence, InMoment 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 and look at how our data studios can be beneficial to your business. 

ROI Questions

Imagine if you were still operating your business in the same way you were in 2019. Total nightmare, right? Your customer experience (CX) program, like your business, needs to be able to grow and evolve to prove a return on investment. If you’re like the majority of CX practitioners (CX Network’s “Global State of CX” report shows that it is the second highest concern for CX practitioners), you likely have quite a few ROI questions.

In our over two decades of experience helping the world’s best brands positively impact their bottom line with Experience Improvement, we’ve heard quite a few of these ROI questions, and have determined the strategies at the heart of a profitable program. In order to achieve true ROI, you need to take an integrated approach to experience by breaking down data silos and creating one ecosystem of data. 

All of your customer data needs to exist in one place, where it can be accessed from anywhere in the organization, meaning that the game changing insights you need to acquire new customers, keep the old ones, expand customer lifetime value, and cut inefficient or costly processes are all in one place.

InMoment recently held a webinar with representatives from Forrester, an independent market research firm, to give you the answers you need about your top CX ROI questions. InMoment’s Principal CX Strategist Jim Katzman and Forrester’s Senior Analyst Judy Weader discuss showing the value of your CX program, designing digital experiences that make your business stand out, and setting your brand up for success. Let’s dive in! 

Your Top 3 ROI Questions

ROI Question #1: Why Is Showing the Value of Customer Experience So Difficult?

Showing the value of your CX program can be a daunting task. How are you supposed to link improving experiences back to financial gain? Well, the truth is, most CX professionals don’t know the right mechanics they need to perform in order to show ROI through their CX program. 

In order to showcase the ROI of your CX program, there are going to be calculations involved. But, don’t be intimidated by that. It isn’t as complicated as it may seem. 

Let’s take a look at a call center for an example. At every call center, there is an average cost per call. For the sake of simplicity, let’s say our call center has an average cost per call of $5. If this call center receives 100 calls per day about an identified pain point (let’s say it’s a confusing process), you would be able to take that customer feedback and turn it into an actionable insight which would clarify the process, thus relieving the pain point. 

By taking action, you may be able to turn 100 calls per day into 80 calls per day. 20 less calls per day at $5 average cost per call is a $100 of daily savings. Just like that, we have proved that having a CX program that creates actionable insights provides a return on investment to the organization. 

Showing the value of your CX program is easiest when you are able to turn actions into numbers. By making decisions based on customer data, are you increasing revenue? Decreasing costs?

ROI Question #2: How Are Business Designing Digital Experiences That Make a Difference?

When developing a digital product or service, it’s important to think about the context that your offering will be used in. Think of your favorite airline, or an airline that has developed a “good” mobile application. The reason these apps succeed are because they were designed with the knowledge that when you check-in for a flight, you aren’t going to haul out your computer. These airlines knew it would be easier and more convenient for their customers to be able to check-in for a flight when they were on the go. 

When you develop your products and services around your end customer, you’ll be able to create digital experiences that enrich peoples’ lives and generate more adoption, engagement, and advocacy.  

When designing and developing your products, you also need to remember to design for accessibility. If you aren’t thinking about accessibility in your products, you are missing out on a huge opportunity. There are over a billion people in the world that are disabled. Whether it be different font sizes, text-to-speech options, or modified touchscreen shortcuts, designing for accessibility is something that needs to be done throughout the design process. It is not something that can be bolted on after the launch date. 

ROI Question #3: What Three Things I Can Do to Set My CX Program Up for Success?

  • Have a Good CX Vision

The optimal CX vision for your organization should be derived from your brand vision. Your brand vision should answer the question “What do I want my brand to be for the market?” Consequently, your CX vision should answer the question “What do I want my CX program to be for my customers in order to support my brand vision?” 

  • Build Out a CX Strategy

Developing a CX strategy can seem like a long, intimidating process. But, it is important to remember that the goal of your CX strategy is to bring your CX vision to life. If we take one more step back, your CX vision should reflect your brand vision. So, at its core, your CX strategy should align with your business goals in order to bring that brand vision to life. Using your business goals as a base, you will be able to develop an effective, focused CX strategy. Your motto should be to “design with the end in mind.

  • Align Your Priorities

You want to make decisions that are grounded in customer understanding and current business initiatives. When thinking about which initiatives to go after first, take a moment to think. What matters most to the business? What are the goals that your business is trying to achieve now versus what they hope to achieve later? By prioritizing your CX initiatives with your business goals, you will create an effective CX program. 

Moving Forward

As you look forward and adopt these principles into your own ROI strategy, don’t stress about being perfect. CX programs are ongoing, ever-changing, and constantly evolving assets to your business. There is no set “right” way to utilize your CX program. 

Our recommendation? Start with a quick win—a straightforward project you can measure the success of. 

An ROI Example from an InMoment Client

A few years ago, one of our clients, a national chain restaurant, was looking for a new way to get in touch with their customers. They already had an internal assessment system that was used as a comprehensive assessment of performance in front- and back-of-house operations and policies related to Food Safety, company standards, and guest experience (e.g., quality, order accuracy, speed of service, staff friendliness, cleanliness, and team engagement). 

With more than 13,000 of these assessments completed each year, the results helped drive continuous improvement in quality, operations, and brand standards—but they lacked a view into the guest perspective. 

This company chose InMoment as its CX optimization partner based on its ability to interface audit data with CX data. By bringing audit and guest feedback data together, InMoment’s prescriptive analytics automatically generated two improvement priorities for each location. The integration model takes into consideration both guest experience and audit score, and creates priorities tied to the greatest return on investment: where this organization should put more time, energy, and effort. 

After implementing these data-driven improvements using the InMoment and internal audit correlated system, the organization’s restaurants saw a significant increase in all key metrics in just eight months:

  • 34% in OSAT
  • 33% in Friendliness
  • 22% in Product Quality
  • 22% in Cleanliness and Facility 
  • 19% in Speed of Service
  • 12% in “Make it Right” (if an order had a mistake, was it corrected?)
  • 3% in Order Accuracy 

By focusing on just two improvement priorities at each location, this organization was able to completely transform its relationship with its customers. Starting with small, measurable initiatives is a great way to kickstart your CX program. These small initiatives might even have results that expand further across the organization than you would expect. 

If you want to learn more about extracting ROI from your CX program, watch the full webinar here! 

What is CX design?

If you’ve ever heard the terms “CX” or “customer experience” before, you probably know that they and similar phrases refer to organizations’ attempts to scour every interaction for feedback and insights. You might also know that customer experience is generally considered to be a more specific subset of user experience (UX). But how do those organizations design CX programs? What goes into deciding which interactions to scrutinize, the goals formed around that work, and so much more? If you’re curious about CX design and how organizations wield it, you’ve come to the right place!

Today’s discussion breaks down the wider universe of CX design, but we’re also going to talk about the best ways for organizations to leverage this discipline. Many brands consider merely “managing” experiences to be the finish line of CX design, but there’s a lot more that organizations can accomplish, including genuine Experience Improvement (XI). Let’s get into it!

What Is CX Design?

The idea of CX design was relatively simple for many years. Basically, when an organization’s leadership decided to engage in customer experience design, they would deploy surveys (sometimes physically) for customers to fill out and hand back. We’ve all seen the little surveys that sometimes pop up after buying a sandwich, getting a car serviced, visiting a retailer, and the like. For a long time, this was considered cutting-edge CX technology and a valuable means of soliciting feedback. Other methods, like mystery shopping, were often used as well.

However, as technology has grown more sophisticated, so too has our understanding of not just how to manage customer experience, but how to improve it. Our understanding of what customers truly seek in experiences has broadened as well, and so the job of a CX designer (and the role of CX strategy in general) became more about understanding customers’ minds, not just getting their feedback. It became about optimizing journey touchpoints to delight customers and nurture relationships!

Unfortunately, even though technology and new learnings have made CX design so much more powerful than in years past, many organizations seem content to use it to gather numbers and react to problems only as they arise. This principle is reflected in how these companies’ programs are designed; CX teams don’t deploy to fix a problem until it’s been submitted by a customer, and discussions on program progress are centered around how today’s metrics compare to yesterday’s.

The issue with this sort of CX design is that while it can give you a superficial temperature reading of how customers like your brand… that’s about all you’re getting from it. Organizations that take this tack with their CX design miss opportunities to, yes, fix glaring flaws, but also meaningfully improve experiences and get to know their customers on a more human level. That is where the true power of CX design comes into play, and the brands that wield it are, more often than not, at the top of their vertical because of it.

What Does the CX Design Process Look Like?

If CX design can really help organizations achieve such dramatic transformations and high goals, what’s the first step toward actually doing that? 

As we mentioned earlier, many brands start their CX design process by looking at a handful of channels their customers might use to communicate (contact centers, social media, etc.) and deciding to pay more attention to those channels. However, while going about CX design this way might net you a few insights here and there, it’s actually much more effective to design with the end in mind.

In other words, before you even turn any of your listening posts on, take some time to consider which goals you actually want to achieve with your CX program. The sky’s the limit, too! Don’t be afraid to stake out some truly ambitious goals in your design. Experience programs can be used to achieve some pretty amazing things.

What those things are depends on what your business needs. For example, if your customers are churning a lot more than you’d like, you can build your CX design around better customer retention. Or, perhaps you’d like to use your experience program to improve workplace culture or get a better sense of what’s going on in the marketplace around your organization.

Whatever your Experience Improvement goal is, just having that as a guiding ethos will make a world of difference for your CX design. However, while your goal should be aspirational, it also needs to be quantifiable—to revisit the churn example, make sure that you tie a specific percentage or number to your goal. That will further define the finish line you want your CX program to help you cross, and it will also make your initiative’s value more tangible and therefore more visible!

Why Is CX Design Important?

Once organizations establish the goal(s) they want to achieve with their CX design and attach some sort of target number to it, it’s time to take the next step and consider which CX tools to use. This is another reason why setting a goal before doing anything else is so helpful, because it helps you determine not just which tools and channels to focus on, but also which groups of customers.

For example, if you see a chance to improve new customer acquisition, it doesn’t make sense to deploy tools geared for that goal toward all of your customer segments. Rather, you can free yourself and your CX design to focus all your efforts towards new customers. And if you have the bandwidth for additional CX goals, you can deploy your remaining tools where they most make sense. This more deliberate and thoughtful approach to design goes a long way toward achieving those goals!

This approach will also help you find and listen to the channels most relevant to your CX goals. To revisit the customer acquisition example, a CX team that sets that goal might research the areas where new customers most talk about your brand, then set up surveys and other listening tools there to capture that customer intelligence at its most organic. The team will have honed in on the channel most relevant to its goals and successfully filtered out noise from customer segments that are less relevant to those goals.

You don’t have to be a CX expert to know that brands and organizations love data. In fact, some companies love data so much that they gather it just for the sake of having it! Having data is certainly important, of course, but much like the CX metrics we talked about earlier, just having it won’t create Experience Improvement for your customers. The only way to do that is to actually take that data and mine it for valuable insights.

Data hygiene is yet another area that conventional CX design struggles with. A lot of programs are designed to gather lots of data very efficiently, but that creates a whole new challenge for the people running that program—namely, how much time does it take to sift through a mountain of data? What insights should CX teams look for and which ones should they disregard? And how can those teams make progress finding insights when their program is gathering data from every corner at every moment?

This is yet another reason why a deliberate, more targeted approach to CX design is the way to go. If you build your CX programs around very specific goals, the data you gather will be much more specific as well. Your data mountain will be smaller and more manageable, and your CX team won’t have to go near as far to find the insights most helpful to their goals.

One final note about data is that it doesn’t have to come from the CX team alone. Pulling other employees and groups into your CX design process can help you get a complete, 360-degree picture of your customer, further cementing both your goals and your future success. Including others also lets them know that, no matter how far away their job may be from the front lines, their work still matters and is relevant to creating a great experience for customers.

Realizing Experience Improvement Through CX Design

This is the hardest part of customer experience design, but it’s also what the process that we’ve been talking about is designed to simplify for you, your CX team, and your entire brand. You’ve used this CX design process to learn what you need to change to create Experience Improvement for your customers; now it’s time to reach out to the right teams and work together to actually make those alignments.

Once you do, continue to track and quantify those changes. Quantifiable goals are like receipts for a CX program; they give you something to prove its worth when you go back to the board for additional funding (as a quick aside, if you hear any positive customer stories that come about as a result of your changes, save those too! Execs love them). Some changes take a while to blossom, but they’ll be worth the wait once they come through.

Ultimately, what is the point of great CX design? A stronger bottom line for your brand? Yes. Eliminating problems with your journey touchpoints? Absolutely! But there’s an even more ambitious goal that comes about as a result of great CX design, and that’s true Experience Improvement. In this context, Experience Improvement doesn’t just refer to individual interactions better and fixing journey touchpoints (though those are certainly important). It means aligning brand and CX design to achieve seamless experiences. It means understanding who your customers are on a deeply fundamental, human level.

Once organizations achieve that close-up understanding of their customers and thus a close bond with them, there’s almost nothing that can break that relationship. Customers who feel understood as human beings, not just clients, won’t ditch you for a competitor. They’ll evangelize your brand to anyone who will listen. And it’ll all be thanks to your stellar CX design and the Experience Improvement opportunities it created!

We hope that this conversation has given you more insights into what CX design is and how fundamentally it can transform your experiences and brand ecosystem. If you want to learn more about how Experience Improvement can help your organization, feel free to download this e-book!

We’re always up for chatting about all things experience, and our mission is to help you own the moments that matter.

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 InMoment

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. 

InMoment, the leader in people-oriented text analytics, can help. Built on industry-recognized metrics and real-time intelligence, InMoment 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.

competitor analysis

As business professionals, our lives often involve one or more reports packed with market research data every week, if not every day, providing an onslaught of facts and insights. Most of us have experienced the fatigue and boredom brought about by too many facts and too little learning.

So, how can we deliver effective market research data reporting and communication of information and insights in a way that captures the imagination and garners interest and, more importantly, inspires action? Storytelling.

The Importance of Storytelling

The most critical ingredient of effective market research reporting comes in the shape of stories. Storytelling is rarely given the attention it deserves. If research is both art and science, we need to blow the dust off the art elements including writing, presenting, persuading, visual arts, theatrical arts, and the art of storytelling.

For the last few decades, with the dramatic increase of data and information availability, the users of information frequently find themselves in a tough place to make any meaningful conclusions. Especially in the marketing research industry, the research buyers have been identifying the lack of “a story” in the delivered reports by research suppliers. The users of research cry that they do not want just scores and statistics, rather they want a story that tells “what happened” or “why is that important” based on those scores and statistics.

4 Steps to Telling a Story with Market Research Data:

It is necessary to use a set of principles to find insights and then outline steps to successful communication of market research data and insights to business end-users. Consider this method:

Step #1: Understanding

Context is everything. Before designing a research study, it is critical to understand the business’s objectives, current environment and situation, pain points, the stakeholders’ interests, and the use of information. Researchers gather context about the business and the research needs through the clients, their organization, and other outside sources before planning and designing the study.

Step #2: Planning

Design a research study with the “end in mind” and look at the process from the end to the beginning. First, focus on the business objectives, then design the way to deliver the information to meet those objectives, then design the analytic plan to provide this information, and finally design the survey instrument and the sampling frame to collect the data to be analyzed.

Step #3: Discovery

When the data is collected, an essential step is to review the data and discover the story hidden in it. It is a common failure of many market research studies that the researchers deliver a long report of results from the study, basically a dump of information, question by question, without focusing on a story to answer the specific research and business objectives. Instead, a discovery phase needs to occur, where the data is reduced to a coherent story that will answer businesses’ research questions.

Step #4: Communication

Finally, now that the data is reduced to a story, how do you tell that story in the most effective way? This is the last and most important element of delivering research results, as only effective communication of results will accomplish the goal of meeting the business’s research objectives.

There are many ways of making the communication of research story effective, but we will focus on three of those ways here to share some best practices we implement for effective business reporting: Use of visuals, colors, and dashboards.

Use Graphics and Charts

When reporting on market research data, visual components are the centerpiece. A busy reader will often flip through and look at the main diagrams and charts in a report, much the same way that someone flips through a magazine or newspaper and looks at the pictures (and maybe reads the captions). To get your point across in a report, make sure that the visuals are conveying the point—don’t hide your conclusions in the accompanying text. Moreover, neuroscience tells us that recall is also better when accompanied by visual elements—something to which the reader can attach ideas.

Visuals in research reporting are generally either “graphics” or “charts.” Generally, charts visually plot the size of market research data, while graphics show the relationship between concepts/objects or the ow in a process. This distinction matters because graphics are useful for helping show the structure of the story we are telling, while charts are useful for clearly showing the evidence that backs our story. We utilize these graphics to tell a clear and concise story.

Graphics “show” in one complete picture these connections, whether it is a chronological order, a cause-and- effect relationship, or an organizational structure, in a simple pictorial way, making it easier to comprehend and recall. While graphics narrate the story and provide a way to visualize the market research data, charts fill in the details and support the points we are making.

The traditional style of research reporting often fails to engage the attention and therefore the brain of the reader, resulting
in a lack of processing, memory and recall. There are ways to combat this problem, using editing and data density.

  • Editing refers to the process of cutting distracting content. Review the chart for repetition, for non-results, for unnecessary text that does not provide additional information, and for relevance to the main objectives. The editing step reduces long, repetitive charts.
  • Data Density refers to the quantity of market research data points that are shown in a given space. Instead of using repetitive charts to display multiple data series, we can use the idea of data density by combining multiple series on the same chart to improve the flow and interpretive richness of the report. When data elements are far apart in the report, insights can be missed. The human eye and mind are more adept at noticing patterns than we give it credit for, so dense data displays play to this strength of the brain and keeps it engaged.

An important step in creating better charts is focusing on the key elements and deleting the rest so as not to clutter the charts. Another way to increase data density is through interactivity. Interactivity means allowing the user (either a reader or a presenter) to interact with the data by making choices about what they want to see.

Convey the Story Quickly and Accurately

With increased amount of information available through various sources, it has become a major challenge for marketing research professionals to reduce the vast amount of data into meaningful messages for audiences. Many audiences find themselves flooded with just ‘data’ and ‘information’, overwhelmed with statistics and facts, and left without true insights that should inform marketing and strategic decisions.

To overcome this challenge, the key is to tell a story from the data. A coherent and clear story that is relatable to the audience will be more successful in capturing the audience’s attention, and will succeed in communicating the message by creating curiosity in the audience’s mind and engaging them in thinking.

Use of visuals is critical in presenting a successful story, but visuals need to be selected and constructed carefully to create the best effect on the audience. Editing and data density are key ways to improve the readability and effectiveness of reports. Interactivity uses the reader’s working memory to help them see localized patterns in the data.

These tools together make the evidence provided by the charts more powerful and relevant to the story. Understanding how people perceive and compare shapes in charts allows us to construct visuals that accurately and quickly convey the story.

Seamless Retail Experiences

It is no secret that today’s retailers are faced with unique challenges. The rapidly-changing, ever-evolving retail landscape continues to present questions, roadblocks, and pain points that retailers need to address. These tribulations can take many forms; defining customer loyalty in emerging consumers, creating seamless retail experiences across channels, tracking a customer base that seems to be in multiple places at once, and keeping up with a digital landscape that changes as frequently as the Cleveland Browns change quarterbacks. 

In such a fast-paced environment, how are retail brands expected to succeed? The keys lie in your customer data—and how you leverage it. 

3 Necessities for Stand-Out, Seamless Customer Experiences in Retail

  1. Integrate Data From Everywhere Into Your CX Platform
  2. Increase Experience Awareness
  3. Encourage a Culture of Commitment 

#1: Integrate Data From Everywhere Into Your CX Platform

One of the most important keys to deliver seamless customer experiences is to have seamless data integration from everywhere into your CX platform. In order to form a holistic view of your customer’s experience, you need to be able to analyze every data point you can. 

Your customer’s data comes in many different forms (you can learn more about customer data in this article from InMoment Customer Insights Expert Jessica Petrie). Whether it be surveys, review sites, or social media. If you only look at one or two of those data sources, your view of the customer is incomplete, and it may cause you to make decisions for a customer base that you don’t fully understand. 

To continue to provide stand-out experiences, you need to view the customer experience from every angle, and across every channel. This is done by making sure your CX platform is capable of ingesting all of your data and displaying it in an easily accessible, centralized location so that you can access holistic customer insights whenever you need. 

#2: Increase Experience Awareness

Across the hundreds of brands and partners we’ve worked with here at InMoment, we have learned what works, formed a cohesive and proven approach, and can now guide our clients toward a successful CX governance strategy. This strategy will look different depending on the size and structure of your organization. 

Regardless of what you call it or where it lives, you need to have a plan for how you will make your CX program an organization-wide, customer-centric initiative—and keep it that way. It has to be more than just saying you are customer-centric, or having the word “customer” in your mission statement. 

Every department should have a window into the insights you gain from your CX program—and be able to leverage them in their decision making. The information you receive from customers needs to be shared with all other departments and teams, not siloed in different departments, otherwise, you could be sitting on insights that could make a huge difference in your bottomline. When you break down those silos and create channels of communication across departments, your business will see more success in the areas that matter most!

The first step to creating that kind of organizational support and buy-in for your CX program is to create a cross-functional council. This council, made up of representatives from every part of the organization, should be chaired by the CEO or a high-level CX champion. 

This council should aim to manage the activities of the tactical working teams that are striving to improve the customer experience as well as communicate expectations throughout the company and particularly to the customer-facing associates. 

For example, many large organizations have a Chief Customer Officer, an executive professional in charge of the company’s relationship with the customer, who reports to the CEO.   

Truly best in class CX companies will often have what we call CX Champions, Ambassadors or Champions scattered throughout the company that are championing or spearheading efforts within each of the silos we discussed.

#3: Encourage a Culture of Commitment 

A “Culture of Commitment” is the ultimate goal of any customer experience program. In a company with a true Culture of Commitment, every single employee is invested in making experiences better for customers. Whether it be in store, over the phone, or online, these employees are the face of your CX program, and they understand the impact they are making on customer experiences every day. 

When your employees are engaged in the experience, your organization will benefit. Did you know that 70% of the time, a person will become a repeat customer when their complaint is resolved? And that engaged employees can increase an organization’s sales by up to 20%? 

By having engaged, customer-centric employees, you will see an increase in the frontline metrics that matter to your organization. Frontline employees are the biggest customer facing assets your organization has. While executive sponsorship is important, your CX program needs buy-in from everyone in the organization in order to be successful. 

How a Global Footwear Retailer & InMoment Client Started with Customer Data, Fostered CX Governance, and Inspired a Culture of Customer Commitment

One of our clients, a global footwear retailer, leveraged all three of these strategies to move toward a fully customer-centric approach to business. 

It started a few years ago, when an operations team leader, who was passionate about his team being customer-centric, started using customer data points as supporting points in conversations with his team. 

These conversations would look like “Did you know that when our associates offer additional merchandise at the point of purchase, there is a 17% average transaction size uplift.” or “Did you know when our associates are successful helping a customer try on a shoe, they are 3x more likely to make a purchase.”  

This CX champion was able to leverage these customer insights to socialize this information, and make other departments and employees aware of how they could improve the customer experience. Through these actions, a small cross-functional CX governance committee was formed. 

This team was able to get the attention of the executives with their data-driven decision making and were therefore able to help the c-suite realize that factors such as employee behavior, customer behavior, and customer insights are all important factors that drive sales and increase the bottom line. 

After the C-suite executives realized the importance of a CX program, they invested more into it. The CX program adapted and started to utilize an integrated approach to customer experience, where they combined insights from different areas of the organization. And with that approach, they are set to set off the same cycle of success over and over again!

So What? 

Based on our expertise and the lessons we have learned from all the CX programs we have helped grow, we have formulated a list of next steps that will help you make progress towards integrated CX!

Step #1: Go Beyond Surveys

Integrated CX isn’t just about surveys. Find other signals in your organization, and integrate them into your program. 

Step #2: Understand Emerging Customers

Continue to understand your customers. But, you also need to listen to the non-purchaser. Having a deep understanding of your future or potential customers will help you make business decisions. 

Step #3: Get Ahead, Stay Ahead

Having a plan in place is key to your CX success. At InMoment, we often talk about designing with the end in mind. Knowing where you want your CX program to go and what you want to accomplish is key for starting out in a CX program. 

Step #4: Action, Action, Action

Go to work. Identify the initiatives that will have an economic impact. All action taken should be tied back to a specific outcome. 

If you want to learn more about leveraging customer data to craft seamless, differentiated experiences in store & online, watch the full webinar here!

Survey Methodology

Survey Methodology

When it comes to collecting data, one of the best ways to do so is a survey. Most companies put out surveys of some kind for customers and employees at different points. But there’s more to a survey than just a series of questions. In fact, surveys typically have a method behind them to gather specific types of data and to make them as effective as possible. But what is a survey method? What is survey methodology? Read on to learn about survey methodology and why that matters.  

What Is Survey Methodology? 

What is survey methodology? To begin, it’s important to distinguish between a survey methodology and a survey method. A survey method is the process or tool you use to gather information via a survey. For example, you might create an online survey with multiple choice questions, and that would be your survey method. A survey method can be qualitative or quantitative. We’ll talk more about survey method options and their pros and cons later on. 

Survey methodology, on the other hand, is the study of survey methods. It’s looking at all of the survey methods available and using applied statistical information to determine what methods give certain errors and where accuracy can be improved. Essentially, survey methodology studies sampling techniques and practices and determines the accuracy, so researchers of all kinds can improve their methods and get more accurate results. 

What Is the Purpose of Survey Methodology? 

So what is the purpose of survey methodology? Why do we have an entire field of applied statistics working on surveys? It’s important to understand why survey methods matter first. Survey methods are designed to help researchers and companies get information as accurately as possible. After all, the data you gather isn’t worth much if it’s completely inaccurate or riddled with errors that make it difficult to use. Survey methods are how you get data. 

Survey methodology exists to support survey methods. Survey methodology is all about studying the ways to improve the accuracy of survey methods, so researchers and companies can get the most accurate results from their surveys. It’s a field that exists to minimize errors—any deviations from your desired outcome—and help create data that’s as accurate to a population as possible. 

Think about it this way. The common stats phrase for setting up a survey is, “Garbage in, garbage out.” That means that if your method gathers bad data, you’re going to get bad results. The bad data can come from a variety of sources, but one major source is that your tool for gathering the data isn’t very accurate. Survey methodology’s purpose is to make those tools as accurate as possible. It’s what helps researchers and companies get great tools or methods to gather reliable data and get accurate results. 

Types of Survey Methods

Now that it’s clear what the difference between survey methods and survey methodology are, we can look at common types of survey methods available. 

Quantitative and Qualitative

Methods can include both qualitative and quantitative data, but what’s the difference? Qualitative data is descriptive data and more conceptual data. For example, if your survey is gathering qualitative data, you would want to collect quotes from respondents and try to look at the emotions and sentiments of your potential customers, rather than performing a statistical analysis. Qualitative data is the heart of data. 

Quantitative data is data that’s numerical—or quantifiable. When you perform a quantitative survey, you’re gathering information you can do a statistical analysis on; you want to know numbers. While qualitative data is the heart of your data, quantitative data is the bones and muscles; it’s what gives your data structure and support. 

Both quantitative and qualitative data are incredibly important. When you’re choosing to collect data, think about what you hope to accomplish with your data and whether you’re collecting qualitative data or quantitative data. That’s an important part of your survey methods. 

Structured and Unstructured

Another important part of your methods is the structure you choose. Some surveys are very particularly structured while some or more unstructured and allow respondents more liberty with how they answer and where the conversation goes. To determine how much structure you want, think about what kind of data you want at the end. If you want very specific types of data and quantitative data, you would probably choose a structured method that has people responding to exactly what you’re exploring. 

If you’re looking more at qualitative data, you might find it beneficial to take either route. On paper, a structured survey might be easier and get you the information you need. In an interview survey, you could go either way—or even strike a balance between the two—depending on if you’re interested in seeing where the conversation ends up going or in gathering data on something specific. 

Open Ended or Closed Ended Questions

Now it’s time to think of the methods for questions. In general, you can gather information from open ended or closed ended questions. Open ended questions are ones without answer options, a yes or no response, or a true and false response. These kinds of questions are typically geared toward qualitative data (but can be flexible, of course). Closed ended questions typically have respondents choose from some kind of option or require a one-word or one-number kind of answer. These questions are common for quantitative methods. 

Ultimately, a great survey may combine both open ended and closed ended questions to get a variety of data. 

Survey Collection Methods

The final aspect of your survey methods is the method of collection. There are many ways to collect data, but these are a few common ways with their advantages and disadvantages: 

  • Face-to-face
    • Pros: very personal, allows you to see non-verbal nuance, flexible for both structured and unstructured questions
    • Cons: can be time consuming to set up and takes resources to make happen
  • Online
    • Pros: easy to organize, can be easy to get large amounts of data at once, digital responses that are easy to analyze
    • Cons: could be subject to survey response bias, respondents may not complete the entire survey
  • Observations
    • Pros: simple to do and doesn’t require expert design, great for testing hypotheses
    • Cons: could affect the accuracy, no controlled variables
  • Focus groups
    • Pros: easy for qualitative and unstructured data gathering, get a variety of perspectives, may lead to salient ideas you haven’t considered
    • Cons: participants might not reveal their true thoughts, opinions of the respondents could be influenced by other participants

As you can see, there are so many survey methods to choose from to consider. And survey methodology is all about how to make these methods more effective. 

How to Write a Survey Methodology

When you’re going to use a survey, you can write out your methodology—or all the components of your methods and how effective they may be. Here are the steps to writing a survey methodology: 

  • Define your sample group and size (evaluate for accuracy against the population)
  • Decide on your methods and data collection method (while evaluating the effectiveness of those choices)
  • Design your survey questions and remember to keep in mind: 
    • The approach
    • Your time frame
    • Your method of collection
    • The wording of questions
    • Biases
    • (Evaluating each of these helps determine the accuracy of your methods)
  • Collect data
  • Organize and analyze your results

At the end of it, your methodology is all about thinking about and evaluating your accuracy with your chosen survey methods. 

The Bottom Line

Surveying can be a lot—especially when you not only have to consider your methods but also your methodology. There’s a lot to consider for data collection and analysis. But you don’t have to do it alone. InMoment—a leader in survey creation, collection, and analysis—is here to support you. Contact us today to see how we can help you with your survey methodology. 

EMEA Customer Experience Expert

After nine EMEA customer experience experts, 200+ delegates, eight workshops, and hours of fun and networking at the colourful evening reception, it’s safe to say the XI Forum Europe was a success! 

We heard from award-winning CX speakers from some of Europe’s biggest brands—TRUMPF, ASOS, Brakes, Primark, Solus, BD Medical, NatWest and Euro Car Parts, as well as thought-provoking keynotes from InMoment Global Leader, CMO Kristi Knight, and Stan Swinford, CEO and Founder of NPSx by Bain & Company. Hosted over two days, attendees learned practical tips and best practices they can implement immediately into their experience programmes to elevate their experience programmes.

If you missed out on the event, don’t worry—here are five key takeaways you can use to apply to your experience programme today! 

5 Pieces of Advice from Our EMEA Customer Experience Experts

#1: Managing Experiences Is Not Enough—The Future Is Experience Improvement

Customer experience started out in the golden age of advertising, market research, and understanding consumers. Then, the internet was born, and online surveys were created to collect customer feedback in a timely manner. Next, we started managing experiences, and we recognised that the total experience a customer has is a collection of moments and interactions along their journey. 

The idea of simply “managing” metrics tells your business where you are and where you’ve been, not necessarily where you’re going. The future of customer experience is moving past managing experiences, to actually improving them through Experience Improvement (XI). 

#2: Create a Culture of Customer-Centricity by Adopting a Customer-First Mindset

EMEA customer experience experts from Brakes, Solus, BD Medical, and NatWest all noted the importance of building an internal culture within your company to educate your employees on the importance of putting the customer first. Providing colleagues with valuable insight and giving them recognition leads to better employee experience and engagement, which in turn leads to a greater customer experience. Frontline employees need strategic communication.

Changing cultures and mindsets to be more people focused can be tricky in certain industries, however, it’s important to keep everyone in the business updated on your CX efforts so they can truly see the difference you are making in the wider business. Through a customer-first mindset, cross-functional collaboration and silos can be broken down. Customer experience doesn’t belong to one team, it’s an organisational initiative that needs an aligned vision.

#3: Actions Speak Louder Than Words—Data Means Nothing Without Outcomes

The importance of closing the loop with customers and gaining actionable feedback was mentioned in nearly every presentation we heard! Put simply, “closing the loop” means following up with each dissatisfied customer to try and mitigate their negative experience. 

A closed feedback loop is not only important to the business, but also your customers; to the customer, not only are you letting them know you are listening, but you are also building a better relationship with them. For the business, you are resolving real-time issues by taking immediate action and creating positive change. 

#4: Understand and Predict Your Customers’ Behaviour by Utilising the Right Data, in the Right Way

Primark, Euro Car Parts, and TRUMPF touched on the subject of knowing your customer beyond just a score. In an omnichannel world, this can become increasingly difficult. However, by pulling in data from everywhere—such as social reviews, survey feedback, or demographical data—into one place, you are able to gain a clear picture of who your customer really is and how they feel about their experience with your brand.

And with this clear picture comes actionability—you will clearly understand which stages of the journey need improvement. Journey mapping allows you to identify all the behaviours and feelings throughout the entire customer journey. Not only does this allow you to identify areas for improvement, but also understand what’s working well to make experiences become more seamless and consistent in every touchpoint.

#5: Enrich the Lives of Your Customers—Great CX Doesn’t Need to be Complicated

In the last keynote of the day, EMEA customer experience expert Stan Swinton of Bain & Company left us with insightful advice. He noted that in today’s world, the best companies are creating shareholder value, delighting customers, and energising employees through a strategy of enriching customer lives. 

Great experiences are memorable ones which create an impact. When someone has a memorable experience, they will want to share it with others, and it will also stick with them when it comes time to purchase again. 

Think about your value proposition and what you can offer. Is there anything different that makes you special in the lives of my customers? Get to know your customer, understand what they like, what they don’t like, their history and who they are buying for. This way, you can also anticipate their needs. Spend time with your customers and experience what they experience for yourself.  

Great customer experience doesn’t need to be complicated. Every aspect of your business should reflect the purpose of your organisation and what you are trying to accomplish and solve for your customers.

Price Increases

A great deal of customers and the brands that serve them are facing unprecedented uncertainties; understanding them is key to organizational success, delivering Experience Improvement (XI), and ensuring that your customer experience (CX) program is operating optimally. All of these obstacles affect both customers and brands to some extent. Because half the Experience Improvement process is knowing what these challenges are, today’s conversation addresses five of the most prominent ones that I’ve observed in my work as an experience program designer:

  1. Price Increases
  2. Efficiencies
  3. Off-Price/Discounts
  4. Shrinkflation
  5. Skimpflation

 Challenge #1: Price Increases

A plethora of international events, crises, and phenomena has produced steep inflation throughout much of the world, and no part of the supply chain has been spared these upticking costs. Brands have had to pay more to produce, transport, and store their wares, which means passing that cost burden onto their customers. I’m sure you can guess how that’s affecting customer experiences the world over.

Challenge #2: Efficiencies

This is another customer experience woe that affects both parties in CX interactions. Many companies have struggled to stay fully staffed amid the job market churn often called The Great Resignation, which translates directly to everything from longer customer wait times to reduced item availability. For brands, this has become an employee experience (EX) challenge that is formidable, but not insurmountable.

Challenge #3: Off-Price/Discounts

The events of the last few years (and their lingering effects) have resulted in a consumer demand collapse for many goods and services. This is an especially adverse problem for the brands that sell these goods, as they now have to offload them at a significant discount. For example, I discussed in a prior article how reduced demand for patio furniture left these goods gummed up in the supply chain. About a year on, a combination of sluggish demand and eventual delivery has resulted in a deluge of patio furniture that brands, frankly, have no room for. Steep discounts can benefit customers, of course, but on this scale they leave a brand’s bottom line weakened.

Challenge #4: Shrinkflation

You’ve probably seen this word plastered all over your local news recently; if not, it’s the media’s new favorite term for paying the same price for a good that is reduced in size or amount. Multiple brands have taken this approach to their customer experiences as a means of attempting to prevent cost increases, but as you can imagine, most customers aren’t thrilled that the same amount they paid yesterday yields less for them today.

Challenge #5: Skimpflation

This one is similar to both shrinkflation and the staffing woes I mentioned earlier. In essence, even when brands manage to keep employee churn low, they must still contend with the possibility that the service their retained employees provide has lost some of its quality. This results in an underwhelming customer experience much like the amount reductions discussed with shrinkflation. 

The Path Forward for Customers and Brands

It’s helpful to understand the territory your brand operates in, and these challenges constitute just that for many organizations in these strange times. However, there is more to the story: what are customers doing to cope with these and other challenges, and just as pertinently, what can brands do to respond in a meaningful way? How can they ensure that they’re displaying that empathy mentioned at the beginning of this discussion?

Click here to read my full-length point of view article on customer coping strategies and what your organization can do to address them. I’ve spent a great deal of time researching both of these CX elements over the last few years and am confident that those learnings can help you on your own Experience Improvement journey.

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