The No. 1 Misconception in Customer Insights

Frank Buckler, PhD.
6 min readMar 16, 2021

There are 2 types of insights: The “famous” type of insights is delivered in 99% of cases. The “stepchild” type of insights is what businesses unknowingly looking for — but not getting.

“What’s an insight?” did I asked the audience at the INSIGHTS conference, beginning my keynote with an engaging question. It was surprisingly silent given that the conference’s name was “Insights”. I insisted and got some vague responses like “learn new things about the customer”.

Sure you can answer the question and categorize “insights” in many different ways. I am doing it in one particular way with one intention: to set the spotlight on a widespread misbelief.

Facts measure things that you CAN observe. People try to analyze ANYTHING by looking at facts, comparing or correlating them.

Facts are everything you can see, measure, quantify, and therefore describe. It is also known as “Descriptives”. It’s obvious and it’s needed — for example, if you want to know the market share of a brand. The fact answers.

But facts are also used to answer questions on things you can not observe.

Let’s take this: “What drives NPS”? Professionals look at topics promoters mention to explain their ratings and compare them with what detractors mentioning.

It seems more than plausible that this will give you an answer. But it does not.

It’s like comparing the shoe size of your C-Suite with the shoe size of all other employees. As most C-Suites are male in contrast to the rest of employees they have therefore larger shoes. Nobody would think of shoe size as driving carrier success.

Not a good example? Too theoretical?

Imagine people praise the friendliness of the staff and the great service. Sometimes both together. It’s fair to assume that people who like the friendliness will therefore also praise the service in general.

If now just the friendliness is the key driver, still “great service” will correlate too with the overall loyalty expressed in the NPS rating.

The question about the Why, is a question to learn about relationships between cause and effect. This is not a fact. It can NOT be observed.

Businesses not just are looking for facts. They do not just want to know the market share, how big a segment is, how you can profile this segment, and all other descriptive things.

What businesses mostly want to know is: What do I need to DO to improve outcomes.

The hard truth is: You can NOT see the answer by just looking at facts.

This is astonishing as this is how we as humans do it every day. We did it since the beginning of mankind and it served us well. We tried different stones to light a fire and the stone that work best was the way to go.

This way of retrieving insights (to look at correlations of actions and outcomes) works well if the outcome is happening immediately.

If there are several other factors influencing the outcome it becomes already difficult. Firestones may not work when it’s raining, or you don’t use the right straw.

Business life and particularly the field of marketing is even worse. They are many context factors moderating outcomes. On top of this, you don’t see effects right away. You may need to way weeks, months, or years.

Because of this, it’s the rare exception that looking at facts, will tell you something meaningful about what to DO to drive outcomes.

The insight type 2 is “relationships”. The question how fact one (facts about what you DO) results into fact 2 (facts about outcomes). This type of insight is always asking a cause-effect question.

To learn the WHY from data takes the art and science of causal modeling

“One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten when entering business life.” is a famous quote from Thomas Sowell

Why is it forgetting? Because people do not get proper tools to discover causation. They get stuck and are forced to use the best they have: “correlation”.

Step back.

The most often and most important question we have in businesses are cause-effect questions. But the method that we use day in day out is some kind of correlation exercise. This is dangerous, risky and unknowingly cost businesses trillions every year or even month.

Why did nobody take notice of it? Trillion worse industries are built on this.

Answer 1: Its, not a secret, many smart professionals know it but don’t get heard, science knows this since “ever”.

Answer 2: “Nobody,” notices it because you can not observe cause-effect insights. You can only observe facts and try to correlate them back to actions. If you build your theory on correlations you will find a theory that is supported by facts.

This is a useless theory because it’s not very predictive nor prescriptive.

To arrive at prescriptive (cause-effect) insights, there is no other way than doing modeling — causal modeling. You can not observe cause-effect, you can only induce it from facts. It is an art and science to do this right.

It will take another article to carve out the pillars of causal modeling. For now: Machine Learning has helped a lot to make this exercise very practical.

Here is a recording of talk I held at the University of Aachen to explain the pillars of modern causal modeling:

Doing it right does not guarantee arriving at the truth. It only guarantees to arrive (on average) at insights that will be closer to the truth. It will improve your effectiveness and reduce risk.

In the past, businesses needed to bypass causal modeling as it was clunky, complicated, expensive and unpractical. With the advent of Causal Machine Learning this has changed.

Here an example that just stands for the mistakes that we are doing EVERY DAY, that can be prevented by some proper model.

The picture shows data points of customers with on the horizontal axis the NPS rating and on the Y-axis there later cross- and upselling.

Overall both data do not correlate. That’s what we actually see in most datasets. NPS has a hard time correlating with Cross- & Upselling as well as Churn. But not because it doesn’t work.

Often there are high-value segments that tend to be more critical when rating. When the rating improves, the cross&upselling increases even more so as these are high-income segments.

Within each segment, the NPS rating correlates, overall it does not correlate. You unearth true effects by causal modeling — nothing else.

Qualitative research is no substitute

“You talked a lot about quantitative analysis but how about talking to people, understanding them, finding the stories behind what is happening?” you might say.

I am a big believer in the value of qualitative research. But it’s mostly applied wrongly. Its mostly taken as a substitute for causal modeling. This is very dangerous and I will elaborate on this in one of my next CX-Standpoint articles.

Some of you might think “What about plausibility. I can easily check correlations and facts on plausibility and see if they give a plausible holistic story” That’s another topic I like to discuss in one of my next CX-Standpoint articles as the whole topic of plausibility is a big misconception.

Make being mindful to become a habit

Every evening, before going to bed, please repeat those sentences 5 times 😉

- I do not hunt for facts, but the relationships between facts.

- Correlation is not causation.

- It needs causal modeling to learn what works

Them when the next day your meetings start and again colleagues starring at and comparing facts, make it a habit to remind them “Correlation is not causation”.

And when they again respond “[Your Name], we know this, but that the best guess we can get now”. Tell them: “Yes this is certainly the easiest way to draw conclusion….

…. But what if this conclusion is likely wrong and we could make it “mostly right” with a little effort — how much cost savings and growth would we be able to generate?”

Stay curious …

and remember Sherlock Holmes famous words:

“There is nothing more deceptive than an obvious fact”



Frank Buckler, PhD.

Founder of and CEO of Success Drivers // Pioneering Causal AI for Insights since 2001 // Author, Speaker, Daddy