Outlier is automated data insights for your entire business.
Book a Demo
Outlier is automated data insights for your entire business.
Book a Demo
Outlier is automated data insights for your entire business.
Book a Demo
Outlier is automated data insights for your entire business.
Book a Demo

Happy Customers

Happy Customers

This is part 1 of a 5 part series on Happy Customers.

What makes people happy?

We all love happy customers! Happy customers generate revenue, provide useful feedback and are the best evangelists for your products and services. But what makes a happy customer? What is the deciding factor between happy customers and lost customers?

It might surprise you that data is a great way to understand happiness, a core human emotion. In fact, using data to understand happiness is extremely important because different customers will express happiness in different ways!

One of the easiest traps to fall into is to assume that the customers you speak with represent the majority of your customers. For example, the customers you take out to dinner every few months may love your product or they might love the fact that you take them to dinner. If most of your customers that call customer support are angry, remember that they only call customer support if they have problems. You need a better way to measure happiness than to rely on occasional customer conversations or customer support requests.

This week we will cover happy customers—how to identify them and how to use data to make more of them.

Tomorrow we get started with NPS scores which are the easiest way to quantify customer happiness!

“Clap along if you feel like a room without a roof, Because I’m happy” 
– from Happy by Pharrell Williams

Happy Customers: Net Promoter Score

This is part 2 of a 5 part series on Happy Customers.

Are your customers happy?

Such a simple question is remarkably difficult to answer. You could ask them, but rarely will someone tell you their honest opinion of you. You could wait and see if they remain customers (unhappy customers will leave) but by then it’s too late to change their mind.

Ideally, you would have a way to measure customer satisfaction that:

  • Is a simple metric (a single number).
  • Fast enough that you can measure it on a regular basis.
  • Does not require a lot of analysis.

The great news is that this simple measurement exists and it is called the Net Promoter Score. It allows you to ask your customers a single question to tell you everything you need to know. That question is:

How likely are you to recommend our company/product/service to your friends and colleagues?

The answer takes the form of a score, from 0 to 10, with 0 being not at all and 10 being extremely likely. You then group your customers into three groups based on their response:

  • Promoters (9-10): Customers who love your product and will recommend it to others.
  • Passives (7-8): Customers who are ambivalent.
  • Detractors (0-6): Customers who are unhappy and may advise against working with you.

At first, this seems rather aggressive since you need to score a nine or higher to be considered a promoter. However, most people have an inherent ratings bias where they avoid giving very low ratings. This scale is designed to better capture the customer intent with that bias in mind.

To calculate your Net Promoter Score (NPS) you simply subtract the percentage of customers who are Detractors from the percentage of customers who are Promoters:

Net Promoter Score = (% who are Promoters) – (% who are Detractors)

Your NPS can be anywhere in the range of -100 (very bad) to 100 (very good). In most cases it will be in between, with a positive value better than a negative value. For example, in 2013 the Apple iPhone had an NPS of 70, Costco had an NPS of 78 and Southwest Airlines had 66 (source). There are many sites which provide NPS benchmarks for different industries, so it should be easy to find one for yours.

The NPS is not a perfect measure of customer happiness, but it gives you a quick way to classify customer satisfaction. Tomorrow we’ll talk about how to apply the NPS to segmentation to help improve overall happiness!

“Happiness in this world, when it comes, comes incidentally. Make it the object of pursuit, and it leads us a wild-goose chase, and is never attained. Follow some other object, and very possibly we may find that we have caught happiness without dreaming of it.”

Happy Customers: Segmentation

This is part 3 of a 5 part series on Happy Customers.

What makes customers happy?

Knowing how many of your customers are happy is a good place to start, but understanding why they are happy is invaluable. Often, happy and unhappy customers actually use the same product differently! Identifying the behaviors of happy customers and designing the product to lean into those is a powerful way to improve overall satisfaction.

The great news is that, thanks to your NPS survey, you know which customers are happy and unhappy. You can, and should, use this information to segment your customers the same way you use location, demographics, cohorts, etc.

Once you have segments for promoters and detractors, you can do many different kinds of analysis to compare how their behavior differs. For example, put together a table of all the features of your product and which of those features are used by each segment. In the following sample you can easily see a difference in product usage between promoters and detractors:

Feature 134%20%
Feature 275%17%
Feature 320%80%
Feature 467%50%
Feature 523%25%

Once you have identified those differences, the next question is whether they are causes or effects. Do happy users use certain features because they are happy or does those features actually make them happy? The easiest way to do that is to run some tests! Experiment with ways to get your detractors to use the features that promoters love and see if it changes their perception.

This works well for features that are already available and hence have data behind them, but what about new features you are considering adding? We’ll cover that tomorrow!

“And so it turned out that only a life similar to the life of those around us, merging with it without a ripple, is genuine life, and that an unshared happiness is not happiness…And this was most vexing of all.”

– Boris Pasternak in Doctor Zhivago

Happy Customers: Qualitative Feedback

This is part 4 of a 5 part series on Happy Customers.

By now you know how to measure happiness and use it as a segmentation tool to understand what makes your current customers happy today. But what about tomorrow? What about the improvements you are considering making that none of your customers have seen yet? No segmentation can help you evaluate these options since you have no data.

The only way to understand how the future will affect your customers’ happiness is to speak with them. That might not sound very data driven, but you need to treat customer discussions like data collection exercises! If you don’t, you may unwittingly introduce bias into the feedback you collect which will drive you to false conclusions.

When getting ready to talk to customers about new features and updates, make sure to always do the following:

  • Randomly Select Customers. Don’t just talk to the customers you already know well, or the ones that are the easiest to reach. Always randomize the customers you collect feedback from to ensure they are a good sample. Remember that not all customers will have time to talk to you so choose a large enough group that even though not all will participate you’ll still have enough interviews.
  • Always ask the same Questions. It can be tempting to simply sit down with customers to have a discussion about prototypes and designs. However, such unstructured discussions give you uneven feedback and make it difficult to avoid bias. Be sure to have the same set of questions for all customers.
  • Focus on Feedback, not Imagination. Customers often have a hard time imagining ideal solutions to their problems, but they are great at providing feedback about potential solutions you put in front of them. Instead of asking them how they would like you to solve something, show them some options and allow them to choose.

Those of you familiar with survey design will find these familiar! That’s because the same rules of good survey design apply to customer interviews as well. There is a great detailed breakdown of survey design available from the Pew Research Center.

Even after doing these things it might seem difficult to turn qualitative feedback into data driven decisions, so tomorrow we’ll cover more sophisticated models of processing feedback (known as Customer Satisfaction models). They will help you turn your customer interviews into a highly data driven exercise!

“Never let the future disturb you. You will meet it, if you have to, with the same weapons of reason which today arm you against the present.” 

– Marcus Aurelius Antoninus in Meditations.

Happy Customers: Kano

This is part 5 of a 5 part series on Happy Customers.

Getting Satisfaction

Yesterday we covered gathering feedback from your customers about potential product and service improvements, but we didn’t get into specifics. Today we’ll get specific by reviewing the Kano model, which was developed in the 1980s to organize qualitative feedback and make decisions about product features. It’s a great way to be data driven when conducting customer interviews.

There are three stages to applying the Kano Model:

Step 1. Questions

For each of the potential features you might implement, you will ask the customer two questions:

  • How do you feel if the product has this feature?
  • How do you feel if the product does not have this feature?

The customer needs to choose between five possible answers for each question: “I like it”, ”I expect it”, “I am neutral”, “I can tolerate it” or “I dislike it”.

Step 2. Organizing the Data

For each feature, you will classify it into one of the following categories:

  • Must-Be (M) are features the user expects but do not excite them.
  • Performance (P) are features the user likes the more you have them.
  • Attractive (A) are features that excite the user and might be unexpected.
  • Indifferent (I) are features where the customer doesn’t care either way.
  • Reverse (R) are features the customer dislikes.
  • Questionable (Q) are features where the customer gave conflicting answers.

How do you classify features into these categories? You take all of the responses from the questions and tally them up. Then, you choose the feature category based on the highest tally (or tallies) in the following table:

If there are features where the tallies are unclear, you will have to use your judgement OR consider segmenting your customers to understand why they do not agree. But what do we do with the classifications once we have them?

Step 3. Analyzing the Data

The Kano Model provides a great framework for considering the different classifications of features along two axes: Satisfaction and Functionality. Satisfaction represents how happy it makes customers (ranging from “Frustrated” to “Delighted”) and Functionality represents how much of the feature you have in your product (ranging from “Missing” to “Complete”). The model then maps the classifications as follows:

This mapping helps you understand where to invest your effort in implementing Functionality to achieve the highest return on customer satisfaction. As you can see, Attractive features make customers much happier the more you implement them so they would always be the top priority. Must-Be features have a very quick diminishing returns so investing more into them is unlikely to change customer satisfaction. Performance features are fairly linear, so whenever possible investing in them will have a direct (if not huge) impact on customer satisfaction. Questionable, Indifferent and Reverse features are ignored since they have zero to negative impact on satisfaction.

An important part of the Kano Model (and products in general) is that Attractive features will, over time, become Performance features and eventually Must-Be features. This happens as customers become used to having a new feature and it no longer is new and exciting but part of what they expect.

This is a brief overview, you can read a great in-depth explanation and instruction manual for using the model here: Read More about the Kano Model.

Next Week: Over the past two weeks we touched briefly on how some uncommon customer segmentations can greatly improve your business strategy. Next week we’ll focus in on customer segmentation and how you can use some advanced segmentations to build a competitive advantage!

“I can’t get no satisfaction” 
– The Rolling Stones

Sign up for a single idea in your inbox every Monday, to help you make better decisions using data.

Share this Post