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Lilla Szücs

Beware Storytelling

Beware Storytelling, Practice Truth-telling

Storytelling is a crucial skill to “sell” insights internally, but it comes with a risk. Storytelling makes it irrelevant to produce true insights.

“It was a hot summer day when I got a call from David….” This is how I usually start my conference presentation and keynotes, simply because stories suck the audience’s attention. You merely want to know what’s next.

Like a pleasant song, it feels painful when it stops. Your brain sings the song along even then. Same with stories.

It was 2008 when I was traveling to Russia, and I was fortunate enough to join someone’s class reunion. What I was witnessing was unexpectedly amazing. Many times during the evening, someone stood up, raised his glass of vodka, and started telling a story. It always begins with a random occasion like…

“This morning I went to the shower, and it was hard to calibrate the water temperature …(then interpreting this into an analogy) … Isn’t it like in life?… You need time … But once you find an optimal mix, it’s such a pleasure. It’s like with people, once I find you — my friends, I don’t want to miss you anymore “.

I learned that the Russians are pure naturals in storytelling. It was so emotionally intense … And the perfect validation of the point — Storytelling is so powerful.

BECAUSE storytelling is so powerful, it is dangerous.

Please check the following statements and figure out if you agree with them:

  • Differentiating our brand is a vital marketing task
  • Loyalty metrics reflect the strength not the size of our brand
  • Retention is cheaper than acquisition
  • Price promotion boosts penetration, not loyalty

I bet you agree since the trillions of great success stories have been written about those statements in the last decades. Still, none of them is true. Please bear with me for more!

In my college days, I made a strange observation. Students studying language majors were (not surprisingly) very eloquent. But their whole argumentation and the flow of reasoning when talking about what matters in life were odd to me and full of noncongruent thought and explanation.

This was astonishing to me, as I learned that language is the operating system that runs the thought process. Like math runs on numbers and variables, rational thinking runs on words and language.
Fewer language skills or words = less elaborate thinking possible.

Although this might be true, it turned out that the eloquence’s wealth can simply be misused to camouflage not existing logical stringency and non-existence of proper meaning.

People who are trained to judge their own comments on “how it sounds” as opposed to “what it exactly means” are not able to produce potentially true content.

The same is true with storytelling. Its key for someone with great insights is to transform the insights into actions because it is needed to win peers and business partners.

At the same time, storytelling is excellent to camouflage non-sense.

Storytelling is like nuclear power.

Nuclear power is able to generate electricity for the prosperity of our society. But in the wrong hands, it can kill billions of lives.

Have you ever thought about how to save your organization from the “BS army”?

Storytelling has become art on its own. Besides the story structure, the sequencing of a Hollywood movie, the use of metaphors to link back to existing memory structures, it’s built on this simple yet super powerful trick: “plausibility.”

I served as a Sales & Marketing Director in my former business life, and I had monthly performance reviews with my sales reps. It was a rainy Friday which I will never forget…

Karl showed me his dashboard, and I asked him. “Mmh. Volume for X is down, why is this?”. Quickly he started giving me a super plausible story as an explanation. Suddenly, I realized we were starring all the time at past years’ data.

Switching that, suddenly, the volume went up. Karl again had a remarkable story at hand.

“How worthwhile are explanations really?” — I suddenly realized.

Storytelling gives holistic examples that illustrate the theory (= the insight). With this, the theory (=the insight) feels plausible.

Here is the catch…

Plausibility is USELESS

“Targeting always improves ROI” — right or wrong?

Sure, this is a plausible statement. The more of the addressed people are likely to respond positively, the better the effect will be.

But still, it’s plain wrong, as you can read in the widely published work at the Ehrenberg-Bass Institute.

We (at Success Drivers) once did a Marketing Mix Modeling for a mobile carrier and included the ad channel of digital affiliate ads to the mix. The client could not believe its eyes. This channel that everyone was bullying as “junk ads”- showed further the most considerable ROI.

Sure, it was junk because affiliate ads are not targeted. But it is super cheap. Furthermore, nearly every internet user needs a mobile carrier. Affiliate ad reaches not ideal but relevant targets. The low ad cost overcompensates the lack of targeting.

The win of targeting needs to be traded by the rise of costs. If everyone rushes for targeting, it will have a lower ROI than non-targeting.

Reality is complicated… 😉

End of the game: the mobile carrier stopped working with us and switched to providers that are happy to produce “plausible results”.

Checking for plausibility means checking for existing beliefs.

It is helpful in the operational and tactical contexts. It’s valuable if you don’t have time to search for the truth, but you need to make decisions fast.

In the context of customer insights, plausibility can be DEVASTATING.

The role of customer insights is to create new knowledge, to challenge and change existing beliefs.

If your new insights only pass the test, when it complies with existing beliefs (=it is plausible), the wealth of mutual insights you are going to learn will be poor.

Plausibility is the end of the insights.

It is not to blame anybody, this is to open our eyes. The “plausibility” superstition has a long history that actually roots in social sciences.

When it comes to applied statistics, still today, students learn to proceed theory-led. It will take another article to clearly prove that this whole research approach is more harmful than helpful in today’s world.

It’s practical to publish scientific papers. But it is not helpful to make a relevant practical impact in real life nor gain genuinely unique, thus valuable insights.

“Nothing is more practical than a good theory” (Kurt Levin) was the professor’s mantra who thought me marketing and statistics. While certainly, the point is valid, it is abused by science and applied researchers.

The problem that we have is NOT that our “good theories” are not used. The problem that we have  is that we do not use proper methods to DISCOVER good theories (=insights).

I know. This now violates the existing beliefs of most of you. You are skeptical. That’s fine. Make up your own mind. (And challenge me if needed to write a more in-depth article about this)

Today’s statistical practice of causal modeling is built on this unpractical theory-led approach and works like this:

Collect all hypotheses that are backed up by theory. If it’s just speculation, leave it out from the model. Then test the model with a statistical modeling approach (can range from regression, econometric modeling, to structural equation modeling) to validate the relationships where you already have the theories.

The only meat on the bone you are getting is the linear strength coefficient of the relationships. No wonder that researchers are starving for richer insights.

And they roam to those who promise “richer insights” — the story and fortune-tellers.

The reason why not many are using conventional causal modeling in practice is not mainly because it is clunky and complicated. They don’t use it because it simply validates what you already know it is not that helpful.

Now, if a plausibility check is a numb sword, how can we test theories and potentially new insights?

The 2 types of insights: There are just facts (descriptives) and relationships between facts (cause-effect relations). The latter is what businesses are unknowingly asking for: “What action A will lead to outcome B”.

The art and science to gain those insights can be labeled as “causal modeling”. This talk here discusses this in more detail.

… and this article explains it in layman’s terms. JUST SIMPLY TRYING to be more causal will drive huge bottom line impacts.

Every baby step to better causal modeling will bring you closer to the truth. It’s a journey. You can’t do it wrong just more or less good.

The only mistake you can make is not doing it and reverting to the usual “plausible” procedure of looking at and comparing facts.

We need to accept that in truth, we might be wrong. Actually, we are all the time kind of wrong. With fancy stories, we make ourselves feel better and camouflage the fact that we are living in a big bubble of false beliefs.

Endless examples pop up if you take your “funnel glasses” off. Do you remember these statements from the beginning of the article?

  • Differentiating our brand is a vital marketing task
  • Loyalty metrics reflect the strength, not the size of our brand
  • Retention is cheaper than acquisition
  • Price promotion boosts penetration, not loyalty

All very plausible statements, right? Most backed up by “theory” and all are elaborated in established marketing books from Kotler & Co.

The world’s largest marketing science institute “Ehrenberg-Bass” has access to the most comprehensive datasets spanning all industries from the largest brands and corporations in this world. They found no support for those statements and often found proof for the opposite.

Just because it’s plausible, just because marketing textbooks site it, doesn’t mean it is true.

Up Your Storytelling Game: Practice TRUTH-TELLING.

Storytelling is the art of wrapping a theory in a way it feels easily understandable and true. It feels like the natural way of proving a point.

But it’s not. It’s an illusion.

Now, what do we do with this new insight? Depending on your role in a company, the learning will be different.


  • Continue the art of storytelling but add the art of proving the truth to the mix
  • If you want to be an ethical leader, you should align your research with what creates the best stories. Remember, if you wish you can make ANYTHING fueling a great story.
  • Instead, focus on creating true insights. (sounds self-evident, but it’s the exception)
  • Educate business partners on truthtelling instead of simply selling them what they ask for (plausible stories)


  • Challenge your insights leaders to provide evidence of (causal) truth.
  • Every time they come up with facts, comparing or correlating them, stand up and shout “BS”. Or if you are a nice guy with manners, tell them you don’t buy it because of the apparent risk for spurious findings.
  • Embrace insights that VIOLATE existing beliefs. Take them as an opportunity to learn and grow.

I like to close with three simple takeaways that will guide you on your way from storytelling towards truth-telling:

1. Be suspicious of plausible stories

2. Be aware that most of what you know is wrong


“If you would be a real seeker after truth, it is necessary that at least once in your life you doubt, as far as possible, all things.”

– Rene Descartes

Stay suspicious,

Stay aware,

Stay curious,


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Correlation is not causation — but what is causal?

Correlation is not causation — but what is causal?

Correlation, causation, statistics — all this sounds boring, complicated, and not practical. I’ll prove in this article that NONE OF THIS IS TRUE.

Since the beginning of humanity, we have roamed through savannahs and ancient forests and gained causal insights day in day out.

One tried to light a fire with sandstone — it didn’t work. One used a sharp stone to open the Mammut — worked. One tried these red berries — died within one hour.

Correlation works excellent in simple environments. It works great if you have only a handful of possible causes, AND the effect is following shortly after.

Fast forward, one million years: Day in day out, we are roaming through leadership zoom meetings and business dashboards.

“David did this, next year sales dropped. Let’s fire him.”. “NPS increased, great job our strategy is working”.

Is it really that easy?

We still use our stone-age methods. We use them to hunt for causal insights and to justify the next best actions. Action that costs millions or billions in budget.

Business still operates like Neanderthals

If you invest today in customer service training, you will not see results right away. It may even get worse for a while. Later dozens of other things will impact the overall outcome — new competitors, new staff, new products, new customers, new virus mutation, or even a new president.

You cannot see -just by looking at it- that an insight is wrong or right. Even if you put the insight into action and try it out, you will not witness if it works or not.

Dozens or hundreds of other factors influence outcomes. Even worse, activities take weeks, months, or years to culminate into effects.

I believe people know this. But they don’t have a tool to cope with it. This is why everyone goes back to Neanderthal modes — like a fly, hitting the window over and over again, just because it knows no better way.

Businesses live on Mars, Science on Venus

It was a sunny September day in 1998. I was sitting in my final oral exam of my master diploma with Professor Trommsdorff — THE leading Marketing scientist in Germany at that time.

He was asking me, “What are the prerequisites for causality?” I answered what I had learned from his textbook:

  1. Correlation: effect happens regularly after cause.
  2. Time: cause happens before the effect.
  3. No third causes: no obvious external reasons why it correlates
  4. Supported by other theory

Even during this exam, I knew that this definition is useless for real life.

Here is why…

Point #1 — Correlation: most NPS ratings do NOT correlate with resulting customer value. We can still prove a significant causal effect. Below you will find a great example of why it is. Correlation is NOT a prerequisite of causality. This is only true in controllable laboratory experiments.

Point #2 Theory: How can you unearth new causal insights if you always need to have a supporting theory? This is just useless for business applications. Actually, it’s also holding back progress for academia too.

One underlying reason for this useless definition is that academia has different goals than businesses. Academia aims to find the ultimate truth. As such, it wants to set more rigid criteria (spoiler: this helps for testing but not exploring causality).

For businesses, the ultimate truth is not relevant. Instead, what you want is to choose actions that are more likely to be successful and less likely costly.

Because today “Causality” is associated with “ultimate truth”. Academia is avoiding this word like the devil in the holy water — from statistics all the way through marketing science.

Because science is largely neglecting causality, it is not correctly taught in universities and business schools.

This then is why businesses around the world are still in a Neanderthal mode of decision-making.

Causality in business equals better outcomes

Question: What are the most crucial business questions that need research? Is it like how large a segment or market is (descriptive facts), or is it which action will lead most effectively to business outcomes?

Exactly, this is the №1 misconception in customer insights. Everyone expects that “insights” are unknown facts that we need to discover.

In truth, these crucial insights are mostly not facts but the relationship BETWEEN the facts that a business is looking for. It’s the hunt for cause-effect insights.

But how can we unearth such insights?

Here is a practical causality understanding that enables the exploration of causal insights from data. At its core, it relies on the work of Clive Granger. In 2000 he was awarded the Nobel Prize for his work.

In 2013 we took a look at brand tracking data of the US mobile carrier market. T-Mobile was interested to find out why its new strategy was working. The question was: is it the elimination of contract terms, the flat fee plan, or the iPhone on top that attracts customers?

Causal machine learning found that NONE of the many well-correlating factors had been the primary reason. It was the Robin-Hood-like positioning as the revolutionary brand “kicking AT&T’s bid for screwing customers”.

A “driver“ causes an “outcome” directly if it is mutually “predictive”. It means that when looking at all available drivers and context data, this particular driver data improves the ability of a predictive model to predict the outcome. So did the new positioning perception for T-Mobile.

If every driver correlates with outcomes, the model may need just one of all drivers to predict the outcome. This one driver is -proven by Granger- most likely the direct cause.

Machine Learning revolutionizes causal insights

95% of new product launches in grocery do not survive the first year — although brands have professional market research departments.

We let causal machine learning run wild on a dataset with all US product launches, its initial perception, ingredients, pricing, brand, repurchase rate, and then the effect to survival and sales success.

Our client was desperate as nothing was correlating and classical statistical regression had no explanatory power.

It turned out that reality violates rigid assumptions that conventional statistical models require. Machine Learning suddenly could very well predict launch success with 80% accuracy. It even could explain it causally. What it takes to launch success is to bring ALL success factors in good shape. You cannot compromise on any of them.

The product needs to be in many stores (1), the pricing must be acceptable (2), the initial perception must be intriguing (3) and the product must be good to cause repurchases (4). Only if all comes together, the product will fly.

A driver is causal if it is predictive. Now Machine Learning enables us to build much more flexible predictive models. We don’t need to assume anymore that those factors add up (like in regression).

We can have Machine Learning find out how exactly the cause enfolds its effect. No matter if additive, multiplier type, nonlinear saturation or threshold effect, Machine Learning will find it in data.

If the predictive model is flexible e.g. it can capture previously unknown nonlinearities, it improves predictability. That’s what AI and Machine Learning can do today.

Causal insights require a holistic approach

Coming back to the T-Mobile example. None of the new features had been found to be the direct cause of success. Does this mean they had been useless?

Not at all. The new features like “no contract binding” were reasoning the Robin-Hood-perception. The feature perception proves to be predictive for positioning perception. This is called an indirect causal effect.

A driver can cause the outcome by indirectly influencing the direct cause of the outcome. That’s why you need a “network modeling” approach.

The whole philosophy of regression and key driver analysis is a simple input-output logic — and it leads to bad, biased, misleading results.

Nothing in this world is without assumptions

… we should use them as a last resort only.

Often we see that NPS ratings do not correlate with increased customer value. The picture below shows the data points of customers. On the horizontal axis is the NPS rating and on the Y-axis the change in cross and upselling afterwards.

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 as these are high-income segments.

Within each segment, the NPS rating correlates, overall it does not correlate.

If your causal model would not have the segment information and if it would not have as well other information that correlates with the segment, THEN ….

…your model is only true with the assumption that no significant third factors (so called “confounders”) influence cause and effect at the same time.

Granger called this in his work “the closed world” assumption.

There is a last causal assumption to discuss:

Let’s take NPS rating data again. You could be tempted to take it and correlate or model it against the customers’ revenue.

Customer revenue is an aggregate of the last year’s purchases but NPS is just the loyalty of now. Such analysis would assume that the present can cause the past.

Of course you need to make sure that by any means the cause is likely happening before the effect.

Often, we even do not have time-series data. Then you need to judge in the causal direction using other methods, such as PC-algorithms used in Bayesian networks, or additive noise modeling methods, or as a last resort, an assumption based on prior knowledge.

Neanderthals become Plumper

When I speak about causality in talks, I typically hear the objection: “yes, but it’s impossible to be sure that those two assumptions have met.”

Fair point. But what’s the alternative?


BS storytelling?

Back to Neanderthals spurious correlations?

This is so hard to accept: while insights about facts are obvious, insights about (cause-effect) relationships can NOT ultimately be “proven”. You need to infer them from data.

When doing so the only thing you can do is to make LESS mistakes.

Latest Causal Machine Learning methods enable us to:

  • Avoid using theories as much as possible (when in lack of data, they can still be very valuable)
  • Avoid risk for confounder effects by integrating more variables (plus other analytical techniques)
  • Avoid assuming wrong causal direction by combining direction testing method with related theories about the fact.

Leave Neanderthal times to the past and take the latest tools and become plumper of insights 😊

The good news is…

You can NOT make a mistake by just starting to improve.

The benchmark is not to arrive at the ultimate truth. That’s an impossible and impractical goal. The benchmark is to get insights that are more likely to drive results.

Causation is an endlessly important concept that everyone seems to avoid — simply because it’s not understood.

You can drive change by educating your peers, colleagues and supervisors. The first step is to share this article. 😉

“There is nothing more deceptive than an obvious fact”

Sherlock Holmes


Buckler, F./Hennig-Thurau, T. (2008): Identifying Hidden Structures in Marketing’s Structural Models Through Universal Structure Modeling: An Explorative Neural Network Complement to LISREL and PLS, in: Marketing Journal of Research and Management, Vol. 4, S. 47–66.

Granger, C. W. J. (1969). “Investigating Causal Relations by Econometric Models and Cross-spectral Methods”. Econometrica. 37 (3): 424–438. doi:10.2307/1912791. JSTOR 1912791.

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The No. 1 Misconception in Customer Insights

The No. 1 Misconception in Customer Insights

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?” 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 do 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 cannot observe.

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

It seems more than plausible that this will give you an answer. But it won’t.

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.

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

This way of retrieving insights (to look at correlations of actions and outcomes) works well if the outcome happens 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. There are many context factors moderating outcomes. On top of this, you don’t see effects right away. You may need to wait 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.” says Thomas Sowell

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

Step back.

The most often and most important question we have in businesses is 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 a month.

Why has nobody taken a notice of it? The trillion worse industries are built on this.

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

Answer 2:“Nobody” notices it because you cannot 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 cannot observe cause-effect, you can only induce it from facts. It is an art and science to do it 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.

For more details check out the recording of the presentation 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 is 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 the 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. It’s 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

Then 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”

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CX.AI for Dummies

CX Analytics 101

CX Analytics 101 – All You Need to Know!

The NEW Way to Use Your Satisfaction Survey Data To Do the Right Things

Companies want happier customers. Satisfied customers will buy more, pay higher prices, recommend more to friends, and stay longer with the company.

That’s why every company is asking its customers for their satisfaction. In such a survey, most customers also tell in their own words why they are satisfied or unsatisfied.

Large companies get so much feedback. It’s impossible to read it all. But not reading is not an option. It would be a customer assault and a waste of pure gold.

Stop Wasting Customer Feedback

That’s why companies use intelligent software to read this text feedback. As an outcome, the software puts a tag to every customer feedback. These tags describe the themes that had been mentioned, such as “great service” or “too expensive”.

Now companies are counting those themes to see which are most often mentioned. They assume that those most frequently mentioned topics are most important. This seems to be true because the topics are the answer to the question, “please describe WHY you are satisfied or dissatisfied.”

Reading Between The Lines

Unfortunately, a fair amount of essential topics are not raised as frequently. Instead, clients mention topics often that come first to their minds. For example, Home Speaker clients are referring to “great sound” first. Insurance clients are saying “great service” first, and restaurant guests are noticing “great taste”. Interestingly, improving those topics will not necessarily make customers much happier.

Imagine you go to the doctor with pain in your stomach to find out why. He may ask you why you have this pain. But should he take your ideas for granted?

Or, you go to a psychologist because you fear spiders. He may ask you, “why do you fear spiders”. But should he take your answer for granted?

Human Become an Expert with Training – so does Artificial Intelligence

A doctor needs long years of studying and practicing to understand underlying reasons based on what patients tell them.

In the same way, intelligent software can study what hundreds or thousands of customers had been saying. It can find out which topics are a clear indicator of satisfied or dissatisfied customers.

For instance, many speaker customers give “great sound” as a reason. A fair amount of them, however, have not a very high satisfaction score. In contrast, not so many customers give “reliable music streaming” as a reason, but all of them are very satisfied.

Which topic do you believe is more powerful to make customers happy?

CX.AI takes your customer feedback to find the hidden truth

It delivers insights on what makes customers happy, companies successful, and insights leader powerful again.

CX.AI uses an intelligent software that tags customer feedback. It does this with a precision as if a human would do it. But at the speed of light.

CX.AI further uses an intelligent software to study all customer feedback. Just like a psychiatric doctor studies patients for years and years. But at the speed of hours.

Reliability equals Profit

The best thing with CX.AI is that it is so reliable.

Imagine, a doctor would use not very reliable knowledge and therefore would give the wrong medication to every second patient. Would you go to this doctor?

CX.AI has four times higher reliability than conventional approaches. Let me ask you: What reason could justify NOT using reliable insights? What reason justifies taking the wrong medication?

Why would you want to make your customers NOT happier by a factor 4 higher reliability?

Why would ever someone want customers that buy NOT more? … that not pay higher prices? … that not recommend more to friends, or not stay longer with the company?

You know the answer.

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#14 Interview

Most Important Thing When Collecting Customer Feedback

Most Important thing when collecting customer feedback


Hey, if you collect customer feedback, the most important thing is… to make use of it. That’s the biggest thing, what I am seeing in companies and enterprises that, of course we ask the customers open-end question, but feedback is rarely used.


You may send the bulk of feedbacks to the front line, but nobody looks structurally into it, what it really means and what drives impact. Why? Because we don’t have the means to analyze it. I explained that there are techniques to do it.


If you think about it, customer centricity is a big buzzword nowadays. How customer centric are you? If you ask your most important partner, which is your client and… you don’t give a s***, what he’s saying, because you don’t analyse and act on it, what does this tells you about your customer centricity?


Ethically it’s your duty to make more out of your data. We owe it to our customer – they pay our bills and they secure our future.

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#13 Interview

CX Enterprise Platforms vs. Specialized Solutions

CX Enterprise Platforms vs. Specialized Solutions


Enterprise typically choose so-called enterprise solutions, like we’ve got Qualtrics or Medallia or InMoment. Still there is a universe of specialized solutions just like Would you advise to ignore it?


Good question. If you want to get the best out of it, if you really want to drive impact, you should think of those specialized solutions for a simple reason: it’s possible to use those systems.


Qualtrics are not closed systems. They all have docking stations where you can basically plug, with any kind of other software systems. If you look into this, these systems as any other platform are not the best ones in the market now. So they have good standard modules. So for instance, if you look at those two pieces:


they have a text analytics model and they have a key driver analytics model. You can take those modules from the platforms and try to use it.


Those the text analytics modules are just supervised learning. It will not be sufficiently precise compared to what is possible. The key driver analysis module is a simple regression (invented 100 years ago). It’s neither capturing non-linearities nor capturing indirect effect. It’s not a causal engine.


If you do the comparison you will find that the validity of taking specialized solutions is four times higher.


This is just a number, but actually you can see it in real life. If you just use out of the box solutions, you often get those strange results. For instance, positive things like friendliness suddenly have a negative impact. Then becomes strange because you cannot explain this anymore. It becomes obvious that the methodology is, missing something now. Therefore it is a good mix to used what you have, the enterprise system, and plug and play on a specialized solutions for that.


The best example for such a solution is

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#12 Interview

How to Setup Text Analytics When Having Multiple Languages?

How to setup text analytics when having multiple languages?


There are two things you can think of. I always recommend supervised text analytic systems and there are two ways to train them.


One way is that you auto translate everything into a main language. For instance, English, then have an English coder, teaching the AI. That’s one way.

The other way is to have another native coder and teacher. Everyone teaches the AI in native language.


Both approaches has pros and cons. There is no way which is just better. The con of the translation is obviously you loose some information, while translating it , but the disadvantage of having native teachers is that you cannot make sure that they really understand every category in every topics, the same way. You cannot make sure that they really code the same way. If you end up at least with more than three languages, we recommend to auto translate into one language.


That’s typically what we do and actually what’s not so well known so far is that there is an even much better translation machine than Google translate in the market it’s called

It has been proven to be much more precise than google translate and for all systems, we use that machine to auto translate every single one language.

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#11 Interview

Is Advanced CX Analytics Applicable for B2B or High Value Niches?

Is advanced CX analytics applicable for B2B or high value niches?


So does advance CX analytics also work in a B2B company?


Absolutely. What’s different in B2B? There are maybe two things which come to my mind. Language is very specific and second sample size is low. As explained a supervised learning approach is tailored exactly for specific language. So if you run this methodology, you can really train like an domain expert, i.e. having an AI categorizing like a domain expert to a B2B. Regarding sample size, of course, if you have very little data, it’s probably not enough, but there are many tricks where you can work with it and if you are a sizeable B2B company, typically you have hundreds or even thousands of feedbacks. Enterprise B2B typically have enough data.


One of those tricks and trades to work with lower sample size is so-called split analysis. You take the whole data set and split it in certain subset of your customers, which you want to research on. You model with all data but overweight the split. That handles the instability caused by the low sample size and smooths out by the larger dataset.

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#10 Interview

The Role of Context Variables in CX Analytics

The role of context variables in CX analytics


So what’s the consequence if you’re not handling the role of context in modeling.


The consequence could be for instance, that your insights are biased. So I think I gave you the example recently. Customers with higher income are often more critical and they may give different reasons in their explanation. This lead to negative correlations between those reasons and the rating, although the reason may have positive impacts. If you have the context information in the model, your results become cleaned and true.

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#9 Interview

How Does CX Analytics Deal With Sizing the Importance of Topics?

How does CX analytics deal with sizing the importance of topics?


So the question is how does CX analytics actually deal with it?


Contexts information should be a part of analytics. If gender influence what’s important, it should be in the model, it is then a variable saying “one”, it’s a woman, “zero” it’s a man and this variable is influencing the outcome. So with this context variables in the model, the model can learn, can attribute this outcome to the context, or whether it is really driven by the topic. If you could give the model the information, it can find it out by themselves. That’s the intention to use the data you have. You may argue, Oh I don’t have the data for certain contexts information. You will never have a perfect data set. We are not in the business of proving the ultimate truth. We are in the business of making better decisions.


That’s basically what we can do. We can take some data that we already have, and we can work on having better data tomorrow, but everything comes with a price, the price to learn about the ultimate truth is very high. It has the best ROI to get insights that are just closer to the truth, than everything else so far.

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