My name is Ton Luijten, Customer Success Director + Data Science Lead in APAC—and in this post I’ll help you unlock a new take on ROI—through failure demand.

When we manage client programs at InMoment, return on investment (ROI) is always top of mind. We strongly believe this should be a top priority for any team trying to improve customer or employee experiences to show that they are positively contributing to the financial outcomes of their business. 

Most people will instinctively believe that by improving experiences we will improve retention, reduce customer churn, and lower business costs, but proving this is the hardest and most important part of proving your experience program is actually moving the needle at your organization. 

Let’s take a look at how considering failure demand can help you prove ROI.

First Up, What Is Failure Demand?

Failure demand is when an organization falls short of servicing customers on the channels they are seeking, which then causes demand for services in other channels. 

A classic example is a customer that wants to find information on a brand’s website, but they fail to find the information they need—this usually ends up with a call to the call center. 

Why Is Failure Demand Such a Fail? 

Failure demand is problematic for brands as it means the customer experience is not optimized and the customer cannot get the service where and when they want it—not to mention, there’s a cost to the organization to service this additional demand. 

By reducing failure demand, brands have an opportunity to both improve the customer experience, but also create positive financial outcomes for the organization. 

Where Do We Start When Reducing Failure Demand? 

To reduce failure demand, we first need to measure it. Ideally you would be able to use operational data for this, but there are a few problems with this method. If we revisit the earlier example—how does the organization measure that the customer visited the website before they called into the call center? If the customer mentioned this on the call, the agent could take a note of this for their file, but we know these notes are typically inconsistent and hard to analyze at scale. 

When I work with clients at InMoment, we’ve built custom text analytics sets to analyze call center notes—all with the hopes of understanding what customers are calling about. Then, brands can identify topics that attract large call volumes and work out which ones have the potential to be moved to cheaper channels (most likely online). While this is really exciting work, it does take an investment of time and money.

Another option for monitoring failure demand is to use web intercept technology with session recording to understand which journeys across the website cause the most dissatisfaction. 

However, with this option we’re already going into the space of asking customers for their feedback and not just relying on operational data. It also doesn’t allow us to find out what customers did after the failed journey, so limits our visibility on the impact on other channels.

So, What Can We Do To Reduce Failure Demand? 

I’m proposing an alternative option that’s simpler and leverages a solution that most organizations already have in place—post interaction surveys. These four questions will give you the information you need to measure failure demand and prioritize areas for improvement. 

Here are the questions to ask in your post-interaction survey: 

Question #1: Was Your Issue Resolved?

Most CX professionals won’t be surprised by this question as post interaction surveys typically include something of this nature. It’s particularly important for measuring failure demand because you don’t want to cause repeat calls. Of course, it’s also a poor experience for the customer.

Question #2: How Many Times Have You Contacted Us to Resolve That Issue?

It’s important to ask this when measuring failure demand because we want to avoid repeat calls and try to close out issues as quickly as we can.

Question #3:  What Channels Did You Use to Resolve This Issue?

This is a multiple choice question, so customers can select all the channels they have used. This is really important as it allows us to understand which channels they used and how many they used. The latter is critical for failure demand.

Question #4: What Is Your Preferred Channel to Resolve This Issue?

The list of answer options is dependent on the selection made at the previous question. By combining this question with the previous one, you can figure out which channels the customer has actually used and which one they would have preferred to use. This insight allows you to prioritise which improvements you need to implement for the different channels and reduce failure demand, as you will be able to resolve customers’ issues via their preferred channel, which means they no longer need to use multiple channels.

Wrapping Up

Of course, these four questions are contingent on the organization understanding what the original issue is. If we don’t have that information, we should also work that question into the post-interaction survey.

By combining all this information, your brand will have the magic formula—you’ll be able to understand the current state of failure demand, identify key areas for improvement to take action, and measure progress over time. 

After you do a study or research, you will probably want to know if the results you got mean anything. More specifically, you want to know if there are statistically significant differences between the groups you studied. After all, statistically significant results help you know that what you studied—the variables you chose—are having an impact on the results. 

For example, let’s say you did research on your customer base, and you wanted to determine if certain age groups bought your product more than others. Once you have your data, you need to determine if there’s any statistically significant difference between the age groups. How do you do that? 

One way to determine if there are statistical differences between groups is to do an analysis of variance also called ANOVA. What is analysis of variance? Read on to learn more about ANOVA tests and how to use them for your own analyses. 

What Is Analysis of Variance? 

Analysis of variance or ANOVA is a statistical test developed by Ronald Fisher in 1918, and it’s been used by statisticians and researchers ever since. The analysis of variance test is a way to compare means from different groups and determine if the differences in those means are statistically significant. If they’re statistically significant, that means the variable for that group is having an impact on what you’re researching. If they’re not statistically significant, then your variables aren’t affecting what you’re studying. 

Let’s revisit the example from earlier. You wanted to determine whether 18–24 years old, 25–35 years old, or 36–45 years old buy your products more. You gather all of your data about how much people are buying based on a random sampling. You determine the mean for each age group on product purchases. ANOVA is then how you can determine if there’s a statistically significant difference between those means. 

An analysis of variance test will take into account the sample size and differences between means to give you an F value. The F value can then be analyzed to give you a probability or P value—or the probability that there’s a statistically significant difference. Let’s say you get a P value of 0.03. That would mean your results are statistically significant, and you can reject the null hypothesis. Most likely, that would mean you can determine that age is a significant variable in who buys your product, and you could consider making marketing decisions based on that. That’s the power of ANOVA. 

How Does ANOVA Help? 

At the core of it all, ANOVA helps you determine what variables have statistical differences and what variables are important to look at in more depth. Even more importantly, ANOVA can give you a glimpse into the motivation behind behavior. What’s driving customers to click on a link? ANOVA might help you determine that. Essentially, ANOVA helps by giving meaning to numbers, direction to actions. 

Types of ANOVA

ANOVA is a broad category for several types of tests. The big two to discuss are one-way and two-way ANOVA tests. A one-way ANOVA test is the simplest form of ANOVA. For this test, you’ll need one independent variable and two or more levels. For example, you could use the months of the year as levels but still only test one variable. Two-way ANOVA or full factorial ANOVA is when you have two or more independent variables to test. Two-way ANOVA measures independent variables against each other and if independent variables affect each other. 

There are a couple of other types of ANOVA to consider: 

• Welch’s F Test ANOVA. The Welch’s F Test doesn’t assume the variances between groups are equal, which can be beneficial for some data sets.  This type of unranked ANOVA test works when there are two assumptions that are true about the data: 
  • The sample size is 10 times greater than the calculation group (satisfying the Central Limit Theorem)
  • There are few or not outliers in the data distribution
• Ranked ANOVA. If these assumptions above don’t hold, you can instead use a ranked ANOVA test. Ranked ANOVA tests can hold up against outliers and non-normal distributions because the values are replaced with a rank ordering. 
• Games-Howell Pairwise Test. This ANOVA test doesn’t work with the assumption that variations between distributions are equal, and it’s a test when there’s a higher likelihood of finding statistically significant results. 

ANOVA Terms to Know

There are some important terms to be familiar with to work with an analysis of variance test. Here’s ANOVA terminology that may be important for you: 

• Independent variable. This is the variable that you choose to change, and you’re studying how it will affect the dependent variable. 
• Dependent variable. Variables that don’t change and are instead affected by the independent variable. 
• The null hypothesis. When you do an analysis of variance, you will have a pair of hypotheses. The null hypothesis will be the one that says there is no difference between the groups you’re looking at. If your p value is less than 0.05, you can reject the null. If not, you fail to reject the null hypothesis. 
• The alternative hypothesis. Your other hypothesis is that there is a difference between the groups. 

The Formula for Analysis of Variance

The formula for ANOVA is F = MST/MSE. That may look simple, but it involves a few more numbers. The MST is your total sum of squares (all of your means put through a formula) divided by the population total minus one. That gives you the top value for the ANOVA formula. MSE is your sum of squares with error divided by the number of observations minus one. Once you have both variables, you divide the MST by the MSE, and that gives you the F value. With your F value, you can use an ANOVA chart to determine the p value, which will tell you if you can reject the null hypothesis or if you fail to reject the null hypothesis. 

How to Run an ANOVA

That formula can get tricky when a lot of numbers are involved, so not every statistician, researcher, or analyst does ANOVA by hand—though it can be calculated by hand, thanks to creation before computer programs. Instead, most researchers use computer software and programming to perform the test. R, Stata, SPSS, and Minitab are great choices for running an ANOVA test accurately and quickly. 

Drawbacks to an Analysis of Variance Test

An ANOVA test will tell you that there is a difference between means and if that’s statistically significant. But ANOVA won’t tell you where that difference lies. For example, let’s return to our earlier example. You’re testing three age groups for your product purchases. The ANOVA test will tell you that age is statistically significant, but it won’t tell you which group is the one with the biggest difference. You’ll need to do additional statistical tests to determine which age group is most interested in your product. 

Analysis Is Easy with InMoment

Statistical tests can be overwhelming, including ANOVA. While it’s always possible to do an analysis of variance on your own, it’s usually easier and more accurate with some support. InMoment is here to help. Whether you’re conducting a survey, research, or analysis of variance, the process just got a lot easier with InMoment. Book your demo today and see how you can simplify your processes.

Just discovered InMoment? Curious to know a little more about us and our differentiated Experience Improvement (XI)? Well allow us to introduce ourselves! 

Own the Moments That Matter

At InMoment, we have this saying: “Own the Moments That Matter.” This is fundamental to our mission, because those moments—packed full of emotions, judgements, learnings, and more—shape the world we live in. And with every moment, there is an opportunity to make a positive impact; to leave a mark.

But when it comes to your business, there are simply some moments that matter more, to your customer, employees, and beyond. 

Our goal is to empower you with the data, technology, and human expertise necessary to identify the moments that matter, understand what’s working (and what might need improvement), take informed action to solve business problems, and ultimately provide a truly differentiated experience for your business. 

Our CEO John Lewis said it best: “InMoment’s unique combination of world-class technology and expert service enables clients to integrate growing and disparate customer signals and separate predictive understanding from the noise.” 

What Is Experience Improvement (XI)?

Despite increased investment, experience management programs have plateaued. Why? 

Because experiences don’t need to be managed or measured, they need to be improved.

The truth is that monitoring services and D.I.Y. approaches aren’t enough for today’s businesses; they cause program stagnation and make meaningful return on investment (ROI) impossible. Instead, what’s required for success is a new approach: an Experience Improvement (XI) initiative that solves the biggest business challenges, like retention, growth, and cost savings. 

The Moments That Matter

Improving experiences begins with sifting out the noise from experience data and identifying the moments that matter: where customer, employee, and business needs meet. This allows businesses to prioritize their focus on high-emotion, high-impact areas and connect with their most valued customers. Additionally, businesses can empower their employees to recognize and take action in these moments, ultimately culminating in organization-wide transformation from the boardroom to the break room. 

Data, Technology, and Human Expertise

Experience Improvement is made possible through our industry-leading Experience Intelligence XI technology and our in-house Experience Improvement (XI) services teams. With our ability to collect and gather data from anywhere and in any form with our Integrated CX approach, industry-leading technology, and decades of experience in key industries, InMoment can help you craft an experience initiative that truly meets the unique needs of your business. We are dedicated to being more than just a vendor to our clients—instead we take the role of a dedicated partner committed to a businesses’ short- and long-term success.

The Intersection of Value

Our mission is to help our clients improve experiences at the intersection of value—where customer, employee, and business needs come together.  Ultimately, our clients are able to move the needle and go beyond managing their experience to actually improving it. With the right intelligence, businesses can empower the right people to take transformative, informed action in the most effective ways and drive value across four key areas: acquisition, retention, cross-sell & upsell, and cost reduction. In other words, better results for the business and better experiences for their customers and employees.

The Continuous Improvement Framework

The key to taking an experience program beyond metrics is to move beyond monitoring customer feedback and stories and focus on the formation of actionable plans for changes informed by them. Customer narratives contain meaning that companies can use to diagnose both superficial and deep-seated problems, define remedies to those problems, positively impact the bottom line, and create more meaningful experiences. We help our clients  achieve all of this by sticking to a simple, five-step framework that we call the Continuous Improvement Framework: define, listen, understand, transform, realize. (You can read all about it here!)

What Third Party Analysts & Customers Say About InMoment

And don’t just take it from us—InMoment has received third-party validation from multiple third-party research firms. We have been named Leaders in the Forrester Text Analytics Wave, Forrester Customer Feedback Wave, the Gartner Magic Quadrant for Voice of the Customer, and more! 

Forrester Text Analytics Wave

InMoment received the highest possible score and was named Leader in the Text Analytics Wave report. The Forrester Wave says, “InMoment XI is a solid choice for customers who want a platform with a well-balanced mix of knowledge and ML-based AI, the ability to deploy OOTB solutions quickly, and deep custom application development capabilities.” As mentioned in the report, “InMoment’s people-oriented text analytics capabilities…enable it to address all relevant use cases beyond just VOC or CX analytics.” 

Forrester Customer Feedback Wave

InMoment was also named a Leader in Forrester’s Customer Feedback Wave Report. The Forrester Wave  says, “InMoment is a good fit for organizations looking for a ROI-focused technology and services partner.” As mentioned in the report, “reference customers say they selected InMoment for its technology capabilities and value citing the vendor’s pricing as reasonable and transparent. They also praise the vendor’s partnership and focus on delivering outcomes. References appreciate that not everything is “tool driven;” instead, the vendor  provides strategic guidance, helping them innovate their approach to surveys or embrace new forms of feedback. 

Gartner Magic Quadrant for Voice of the Customer

Furthermore, InMoment was named a Leader in the Gartner Magic Quadrant for Voice of the Customer. The report says,  “InMoment is a Leader in this Magic Quadrant. The company’s Experience Improvement (XI) Platform combines a broad set of VoC technologies as part of an  integrated offering that focuses on blending services and  software to help fulfill its clients’ evolving CX ambitions.  The company has established operations around the world,  with a notable presence in Asia/Pacific.” Continuously, the report mentions,”InMoment is investing in intelligent self-service and workflow automation to help simplify the user experience (UX), while  furthering its vision to support four tailored XI clouds for  CX, employee experience (EX), product product experience  (PX) and market experience (MX).”  

“InMoment has an impressive ability to deliver business value through its consulting-led methods and programs.” – Gartner Magic Quadrant for Voice of the Customer

Industry Dominance

Not only have we been recognized by multiple market research firms, but our success is also shown in our reach across multiple industries. InMoment is currently improving experiences with: 

• 90% of the world’s leading automotive brands
• 8/10 of leading banks
• 4/5 of the top insurers

And one more thing. 100% of our clients would recommend us. 

Recognized as a leader and innovator in our sector, we collaborate with the world’s leading brands to attract, engage and retain their customers. We are fiercely proud that our clients continually tell us they love the experience of working with our company, as we constantly stretch to exceed their expectations. 

Our Experience Improvement (not management) approach provides context to feedback at the intersection of value—identifying what’s important to customers, employees, and the business. Our expertise enables brands to align their CX and EX programs with business goals, prioritize actions, determine and monitor impact of change, address issues, and celebrate successes—all leading to true Experience Improvement.

Does this Experience Improvement (XI) mission align with your vision? You can learn more here!