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It’s a much-dreaded customer feedback question but it should be asked. The information you will gather is immensely powerful and can become the basis of a strategic revenue shift. The question, while seeming innocuous, is laden with possibility – “What has been your most negative experience with our product or service?” More simply, “What don’t you like?”

Companies that ask this question, listen to the answer and then use that data to make strategic decisions are better positioned to retain more customers than those companies that don’t ask for feedback or rely solely on surface level feedback like quick surveys, NPS or CSAT.

Companies that leverage this deep feedback data are at an incredible advantage.

Strategically, they use this knowledge to lower customer acquisition costs, increased customer lifetime value, and reduced churn. Odds are, you are missing large revenue advantages by not going deep enough in the feedback you are collecting.

In this article you’ll learn how to fully capitalize on the incredibly invaluable customer data you’re currently missing—and how to mine the significant revenue opportunities that are currently sitting untapped.

Not All Customer Feedback Data Is Equal

 You’re likely well aware of the vast number of ways information about customers is collected — by phone, by digital communications (email, in-app, live chat, web) and in person (video or face to face). Needless to say, a huge amount of information about customers is collected every day. By some estimates, it’s as much as 7.5 septillion gigabytes collected worldwide daily.

Most companies face 3 issues around their customer data:

  1. There’s a lot of it
  2. They don’t always know which data is valuable
  3. They don’t always know which data is unimportant or even harmful

 Understanding the types of data your company collects can help you make strong decisions that will drive the company forward.

The power in making strong business decisions comes in choosing to leverage the data which is the most valuable and the most beneficial to your company while ignoring data that is unimportant or even harmful.

But first you’ll need to understand the different data types, their strengths and their weaknesses. This information is what will help you decide which data is valuable to your company so you can avoid costly mistakes and make good decisions to increase profits and reduce churn.

The Data Types

Customer data collected by the Customer Support team falls into 3 categories:

General
Medium
Deep

General Data is broad in nature. Medium Data goes deeper than general but is less specific than Deep. And Deep Data provides the most detailed information of all of the categories.

The benefit is that each of these categories has data which is valuable to businesses. The downfall is that each of these categories has data which can mislead or appear to be valuable when in reality it’s not.

General Data

General Data is information that is collected, viewed and analyzed at the population level. What General Data is good at is showing trends over a period of time. Given its power to show trends over time, one of the advantages of general data is that it can be used for benchmarking and trend analysis.

Benchmarking is the process of measuring a company’s products or services against an industry standard. It’s useful to see how your company performs relative to your competition as well as how it does against the industry standard. Benchmarking also allows for trends to become apparent over time. Trend analysis is an incredibly useful tool when making large-scale strategic decisions such as retiring products and making crucial pivots.

NPS & CX Benchmarks Report Rates top industries

There are three types of General Data Customer Success teams typically collect:

NPS (Net Promoter Score)
CES (Customer Effort Score)
CSAT (Customer Satisfaction)

NPS, CES and CSAT all look at different aspects of customer behavior and opinions. They are powerful tools to get an overall measure of whether customers, as a whole, are satisfied and loyal. They each use a different time measure. Those time periods are long-term, medium-term and short-term.

While these General Data scores are useful on their own, they are very incomplete. Companies that focus too much on one of these measures as a KPI can be easily misled into thinking that all is well, when in reality there may be issues that are being overlooked by these measurement tools.

General Data scores diagram 1.1

General Data: NPS and Long-Term Loyalty

You’re well aware of NPS and its benefits as a KPI. It is a useful measure of long-term loyalty. But NPS, like all General Data tools, also has its limitations.

NPS (Net Promoter Score) uses a 10-point scale to measure long-term loyalty. By asking someone the question as to whether they would recommend the company to a family member or friend, NPS gives some insight into whether a customer will stay loyal based on their numerical response. ‘Promoters’ (those who give a ‘9 or 10’ response on the scale) are assumed to be the ‘most loyal’ given their high numerical response.

NPS example

The challenge with using a single NPS measurement is that singularly focused and doesn’t factor in that there are different types of loyalty. Retention loyalty, advocacy loyalty and purchasing loyalty are all different measures of customer loyalty. For example, retention loyalty questions are the best predictors of churn. Advocacy loyalty questions are a good predictor of new customer growth. And purchasing loyalty questions are the best predictors of ARPU (Annual Revenue Per User) growth.

Using NPS is great for benchmarking against competitors but it is an incomplete measure of loyalty. 

Knowing this limitation of NPS means you can choose to add more targeted measures of loyalty or simply supplement with other types of data measurement tools such as Medium and Deep Data which are discussed below.

Relying too heavily on NPS as a KPI on which to make strategic decisions means overlooking other information which may yield better data and give better insights for decision making.

General Data: CES and Medium-Term Loyalty

Developed in the late 2000s, CES (Customer Effort Score) uses either a 7, 5 or 3 point scale using numbers or emoticons to measure how much effort a customer has to exert to get an issue resolved, a request fulfilled, a product purchased/returned or a question answered.

CES example

Studies have found that when customer effort is high, customers are more likely to churn. Customer service is the area that often has the highest effort to achieve a goal. Customers may have to contact customer service multiple times, speak to multiple agents, and repeat the same information several times in order to get an issue resolved or a question answered. It leads to frustration for customers and increases the desire to find better service from one of your competitors.

Unlike NPS, CES is a snapshot of a customer’s last experience and focuses attention there. CES can be useful for trend analysis to see if changes in procedures or new initiatives improve the experience of the customer, as a whole.

Like NPS, CES as a KPI can only provide broad information of the CX as a whole. Individual experiences will vary. Other measurement tools (see below) will offer a more complete picture of what customers are thinking and, more importantly, what they are feeling when they have interactions with customer service and customer support.

General Data: CSAT and Short-Term Satisfaction

The last General Data tool is CSAT (Customer Satisfaction Score) which uses a 5-point scale to measure how satisfied customers are with a company’s products or services. CSAT is more similar to CES as it focuses on the last customer support interaction or key moment (like completing onboarding).

CSAT example

Of the 3 General Data collection tools (NPS, CES and CSAT), CSAT is the one that has a unique focus. It focuses on a feeling the customer has while NPS and CES reflect on the behavior a customer might take in the future (NPS) or has taken in the past (CES).

While behaviors are subjective to the individual, feelings are even more subjective. One of the challenges with CSAT is that one customer might interpret their feeling of satisfaction differently than another customer. Or the exact same customer might rate their satisfaction higher or lower depending on their mood that day.

Much like NPS and CES, CSAT is a general measure and gives indication of what customers are feeling in general. Individuals, even day to day, will have different feelings which are highly impacted and influenced by externals like what happened immediately before they contacted Customer Success.

Coupling CSAT with other data collection methods like Medium and Deep Data will give a better picture of other factors which may affect the CSAT score customers, thereby giving a more insightful view of the customer experience. 

Medium Data

Medium Data is in the middle between very broad General Data and very specific Deep Data. Unlike General Data which consists of quick questions which can be answered in less than 10 seconds, Medium Data takes longer to answer and gives greater insights into what individuals are thinking.

Medium Data is also used to follow trends if some of the questions asked are closed ended (for example, questions that have predefined answers like a list of options). Otherwise, the greatest advantage in Medium Data over General Data is the respondent is able to answer the open-ended questions in their own words.

The more opportunities for someone to respond in their own words, the deeper the insights.

If analyzed correctly, responses to open-ended questions can also be categorized for themes which can be tracked over time. Unlike General Data which is numerical and can be easily plotted on graphs, changes in themes over time require monitoring which need to be added and deleted as patterns are noticed. Changing themes are a good indication of how customer needs, desires and wants are shifting, and can be very useful in making strategic decisions.

Surveys are the most common type of Medium Data. Surveys can be created for a number of reasons such as gathering feedback on new product features or services, or for giving clarity on a specific issue.

The most powerful survey that Customer Support teams use that is directly tied to revenue is the Cancellation Survey.

Medium Data: Cancellation Survey

One of the easiest ways to protect revenue from being lost through a churned customer is to find out why the customer decided to cancel. Cancellation surveys are extremely powerful in the information they provide as they give indication of areas of opportunity to improve products, services and the overall customer experience.

cancellation survey

Churn is a fact of any business. At some point, customers simply stop doing business with your company. But knowing the reasons behind why they leave is incredibly valuable. The more a company understands why its customers leave, the more changes they can make to help them to stay.

Customer Success teams that execute cancellation surveys gather exact knowledge from customers to make changes upstream which allows them to keep customers longer.

The cancellation survey serves two purposes tied directly to revenue:

  1. It informs the Customer Success team that a customer is planning to leave. This provides a golden opportunity for a CSM to reach out to the customer before they churn.
  2. By providing the reason as to why the customer is cancelling, which the CSM sees in the survey responses, the CSM is able to suggest a brand new offer or provide a more appropriate offer based on the reasons the customer chose to leave. These more targeted offers can entice the customer to stay, thereby reducing churn.

Surveys, in general, are a great balance between General Data’s broad and surface-level information and Deep Data’s time-consuming and detailed information. They are inexpensive to execute, provide more information than General Data (NPS, CES, CSAT) and help to shape the outline for Voice of Customer or Ideal Customer Profile which are heavily used by marketing, sales and customer success teams.

Deep Data

Deep Data is by far the most valuable and useful information to companies. Because it goes so deep into their thoughts, feelings, goals and desires, Deep Data yields information not readily accessible by both General and Medium Data methods. In many ways, Deep Data is the true Voice of the Customer.

Without question, Deep Data is the most expensive, time consuming and labor-intensive way to gather critical customer data. General Data, on the other hand, is the cheapest, easiest, and requires the least amount of effort to interpret the results. While Medium Data sits in between these extremes.

It’s because of the time and effort involved in collecting Deep Data that the information gathered is the most insightful and the most valuable to any business.

“I’ve built three successful businesses which follow the same pattern. When we spend more time than we’re comfortable with talking to customers, we inevitably end up building a product people love.” Hiten Shah, Founder CrazyEgg, KISSmetrics and FYI

Deep Data: Customer Feedback Focus Groups

Most businesses don’t use focus groups as a way to gather information about their customers. Reasons for this are related to financial cost and time spent in facilitating the group. Other reasons include a lack of knowledge on how to properly run a focus group and not seeing the business value in holding a focus group.

Companies that are located primarily or solely online easily discount focus groups as a useful method of data collection because their customers are geographically dispersed. They assume that focus groups require people to be physically gathered together, which is simply false. Video conferencing software allows for customers to gather in a virtual focus group environment. While it is not the same as being physically proximal, virtual focus groups still provide an open environment where key questions can be raised, answered and discussed.

Netflix, one of the world’s largest online streaming companies, used focus groups as a way to discover and uncover key information they sought to help them become an industry leader. Using a combination of focus groups, surveys and NPS, Netflix was able to make strategic business decisions that allowed them to become an industry leader, all based on the powerful information they gathered.

One critical focus group overlooked by companies is that of their best customers (top 20%). The most critical data for understanding what makes this group so unique is found only by speaking with them to discover what makes them different from the other 80% of your customer base.

While speaking individually to your top 20% best customers in an interview (see below) is highly beneficial, speaking to them as a group will yield more valuable insights that any interviews or surveys ever will.

The reason for this is that when people are congregated together, ideas and discussions will emerge simply because one person mentions something, and others chime in sharing their views, feelings and experiences. Just think of when you’ve been in a group talking and how the conversation evolves, and new discoveries are made. There’s a synergy that happens when people gather (in person or online), that allows for freedom of thought and discussion and new ideas to emerge.

When this heightened point of discussion happens, you will be absolutely surprised by the information you discover about your best customers.

Deep Data: Customer Feedback Interviews

The Deepest Data collection method is the interview. Whether done in person, on the phone or via video call, interviews give the precise type of information you want on any topic. By interacting directly, one-on-one, the interview allows for deeper data collection by being able to ask customers to expand on their answers.

Unlike General and Medium Data collection methods (NPS, CES, CSAT and surveys) which allow for only one way flow of data–from the customer to the business–Deep Data collection methods (focus groups and interviews) allow for information to flow two ways between the company and the customer.

A big disadvantage of General and Medium Data collection methods is that they only collect whatever information is given by the respondent. Nothing more. By their nature, General and Medium Data tools are quick and easy to administer. They provide fast information that is easy to analyze and surface level. This is why they’re so widely used.

Interviews, on the other hand, allow the interviewer to ask probing questions to get at the ‘real’ data they’re seeking. Humans have been taught to think of the other person’s feelings when giving responses. This means the first answer to a question is often inaccurate because–consciously or not–we tend to hold back how we really think and feel.

Interviews, by nature of being a one-on-one interaction, allow for trust to be built. Even in a short 15-minute interview.

The one-on-one interaction allows the interviewer to gain enough trust that they can ask probing questions like:

“Tell me more”

or

“How does that affect you?”

By having established trust, the customer feels they can reveal their ‘true’ feelings and thoughts, rather than providing censored answers which are considered more socially acceptable.

Consciously or not, many of us give answers that make us look good or appear to be helpful. Humans will also provide answers that protect the feelings of the interviewer. While sometimes difficult to hear, the responses from a well-conducted interview yields those incredibly powerful positive and negative answers businesses need to make strong and strategic decisions.

It truly is worth the time and effort to get good at interviewing, especially to gain previously unknown information about your customers.

For survey and interview questions on product-market fit, creating customer profiles, customer success and cancellation flows download

The Comprehensive List of Customer Feedback Questions. (no email required)

 

title page of customer feedback questions

Conclusion

Making strong business decisions is challenging when faced with mountains of data and KPIs derived from very general and superficial inquiries to your customers. The information from NPS, CES and CSAT is very broad and often isn’t representative of your customer base. While noticing changes in trends in these KPIs is helpful, making strategic decisions on such surface level data is dangerous and potentially harmful when used to make the wrong decisions.

It’s critical to understand that not all data is created equally, and that some data is more valuable to your company than other data. Data gathered from surveys, focus groups and interviews is far more powerful than any data from NPS, CES or CSAT because they are the true Voice of the Customer.

The more intimate your knowledge of your customers gained through interviews, focus groups and surveys, the greater your confidence is in making strategic decisions that will retain customers, and ultimately increase revenue and profit.

Companies that create strong data collection methods relying on General, Medium and Deep Data make more confident strategic decisions with better outcomes. These companies retain their customers longer. And they avoid making costly decisions that are ill informed by poor data.

Download this article as a guide so you can refer to it when you need it. Download immediately. No email required. 👍👍
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