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REINVENTING NPS: A Call for Corporate CX Pioneers

REINVENTING NPS:
A Call for Corporate CX Pioneers

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: November 11, 2022 * 7 min read

NPS embodies one of the most common KPIs for the last two decades. Moreover, it served well and helped organizations become much more customer-centric. All signs, however, indicate that the CX industry is losing grip. Progress in customer centricity is becoming harder and harder and most of companies have reached a plateau.

Even Bruce Temkin (Co-Founder of CXPA) and early NPS ambassador recently published an article describing the need to reinvent NPS. He called it “True Loyalty Measure”. 

New tech vendors popping up and suggesting pNPS (predictive NPS) or eNPS (emotional NPS) – all pointing out other drawbacks of the NPS system. Against the background of this potpourri of small-scaled NPS facets, maybe it’s time to put it all together and develop something holistic and robust.

In this idea pitch, firstly, we will outline the current key challenges of the NPS. Secondly, we will illustrate a potential solution that leverages cutting but established 21st-century technology.

Besides all scientific and technological explanations, this idea pitch is intended as an invitation and inspiration to and for you as a corporate CX decision-maker. With you, we are willing to build the next phase of CX insights. 

It takes your initiative to move the industry.

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Challenge No.1 - Benchmarking is expensive or sometimes not even available

Benchmarking is a key exercise every C-Suite is asking for. At the very core, the management wants to know, “Is this a good score or is it a bad score?”

To answer this, customer insights teams sometimes struggle to find truly comparable benchmarks. Many companies invest in large NPS trackers that measure the NPS of all relevant competitors. Results still leave most experts puzzled. It often seems to be mysterious why, e.g., the market leader has such a high score.

Benchmarking even becomes impossible if you want to benchmark touchpoint/journey scores.

Research has shown that not only loyalty is impacting the NPS score but also brand strength and market share. We need an explanatory -not only a descriptive- answer to the question of “Is our performance good or bad?”

Challenge No.2 - Need for a highly reliable measure

More and more NPS scores are used to incentivize the management of a company. This requires the score to be true and robust. In both aspects, the NPS system is easy to attack.

The NPS score is not true

The likelihood to recommend is a rating scale that delivers responses that are highly biased by rational filters and methodological effects. Often loyal customers tend not to pick the 10 out of strategic rationale. Also, customers have a subconscious tendency to be polite and avoid picking 0-5 points on the scale. 

Furthermore, only the endpoints of the scale are defined. As a result, you find major cultural differences in responding to the scale. The general concept of a 0 to 10 rating comes from the anglo-american region and is largely unknown in most other parts of the world. As such, people respond differently due to their traditions.

Other topics are biasing results: most customers you ask do not participate. What would have been their answer? Depending on the self-selection process, your results a chronically screwed. There are modeling methods for debias available, but they are hardly ever applied.

The NPS score is not robust

The score calculates from the difference of percentage share values (% of promoters vs. % of detractors). As a well-known statistical phenomenon, small percentage scores have huge error bands for small sample sizes and, as such, are largely impaired compared to averaging Likert scales. 

This impact is even amplified if you start to weigh your sample e.g., as you want to overweight your high-value customers. If, suddenly, one instead of two high-value customers are among your promoters, the NPS score changes dramatically.

Challenge No. 3 - Unexplainable differences in NPS

Companies witnessing unexplainable differences between NPS scores. Large competitors often tend to have better scores, although some of them do not outperform your performance in any way.

The same you find when comparing NPS scores between countries or regions of the same brand.

Even worse with scores related to a touchpoint (or journey) survey. It’s hard to compare it with other touchpoints.

Part of the unexplainable differences can be explained by drivers or impact analysis. It helps to learn why the NPS is high are low.

But mostly, still, half of the variance stays unexplained. In other words: half of the differences in NPS is not due to CX performance but thru the specific market situation and brand strength.

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Imagine there would be a solution…

Imagine there would be a solution that estimates a better NPS score. The score will be above 100 if the company performs better than a predictive model would expected based on the industry, touchpoint and market position. The predictive model is calculating the expected value of a look-alike company.

It would be below 100 if worse than such a look-a-like company or brand. The same applies to touchpoints or region cuts. 

It would make benchmarking not only redundant. It would provide a much better answer of the question, “are we doing well”. 

Imagine further that this new score is based on the neuroscientific measurement of implicit attitudes. (Actually, loyalty IS an implicit attitude). This method is intrinsically metric and not based on percentage values. As such, it has the foundation to be more reliable, robust, and true.

Imagine, finally, the score even controls many biases and also is able to retrieve more information from each customer (without the need for more time), which is used to stabilize the score.

Such a system provides for the first time a reliable benchmark for performance.

Why?

The worlds-largest marketing institute Ehrenberg-Bass conducted many large-scale studies (published in Byron Sharps’s famous book “How brands grow”). One key finding was that -independent from the category- larger brands have more loyal customers not because they are good but because they are large. Exactly this is what you find in CX studies around the world. 

The very same finding is independently backed by the results of the iconic PIMS study finding thru modeling that market share is the key driver of profitability. This impact was found to be independent of any other business metrics. In other terms: market share drives customer loyalty and, thus, profitability. There are not just confounding other reasons (e.g. a better performance) that make market share appear to correlate with loyalty and profits.

From this perspective, benchmarking with competitors never made sense. 

With the following new system, now, it will.

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REINVENTING NPS: “Supra CX” as our proposal

The following process has been planned out in order to solve all challenges above. We will build a survey, analysis, and data interfaces to make this system usable for everybody.


STEP 1 – REINVENTING  the survey process

  • We run a four-item implicit association test (IAT) that, on average, takes in a total of 10 seconds. It will be designed as a tinder-like setup, which frees from the need to brief respondents in great detail.

     

  • Followed by an open-ended question on why. Instead of restraining to a open text form, the audio option should become the new standard. It provides much more information, is more customer-friendly and also give more emotional context.

     

STEP 2 – REINVENTING the score with machine learning

  • Collect prior to the survey the context information on the business (market share of the country, brand, region, etc.), and use the customer information if available, like customer segment, time of day, age, gender, and customer since,..

     

  • Build a score based on the IAT responses and build a machine learning model that explains that score using all available information on the customer and the context.

     

  • Use all available information, including categorized open ends, to predict the true likelihood-to-recommend score. This helps to increase the robustness of the then-calibrated score further. It enables to report of scores of entities with very low sample size, such as of N=25.

     

  • We simulate the resulting score pretending to be a norm-market share. This is your “look-alike score” – the benchmark that you should aim to outperform.

     

STEP 3 – REINVENTING explaining why

  • Run drivers analysis using the text categorization and context information, preferably using causal machine learning – a method used at www.cx-ai.com
  • Audio information needs to be auto-transcribed first.

     

STEP 4 – REINVENTING  Deliverables

  • Online dashboard to view and analyze the score, the customer topics impact, and an impact simulator and a bridge that explains why the score changed that way.

     

  • API that enables delivery of that information. This enables you to report results in your existing dashboard environment.

Engage and help to move the industry

To implement this “Supra CX” concept in accordance with market needs, we need “you”. We can only work with one corporation (or a consortium) and develop and pilot the described system. All parts of the system are proven and tested. The task is not to develop the tech. The task is to tweak the setup and prove that it solves the issues outlined in this article. 

Are you willing to engage in such a pilot? Write me via frank@cx-ai.com 

Do you have an opinion on what needs to change? Write me via frank@cx-ai.com 

Do you want to suggest a contact of yours and engage him/her? Write me via frank@cx-ai.com 

Write me!

Frank

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The Confirmation Dogma

Rigor or Dogma?

About Theory-Led Research for Businesses … and Alternatives

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: September 08, 2022 * 6 min read

“Build hypotheses and test them” is the guiding idea of social science that guides how practitioners try to solve marketing and sales insights problems today. However, the damage of this confirmatory research principle to business practice is huge. This becomes only obvious if you take a closer look. Let me illustrate here why this is, why exploratory causal analysis is needed in many cases and how AI can help.
First, why does confirmatory research make sense?

Of cause, there is a reason why confirmatory research is so dominant in social sciences such as marketing science. Social matters are typically very complicated. When you see a correlation it does not mean there is a relationship or a causal link. 

People tend to find reasons for correlations after seeing a correlation. But prior to the fact, the same person would have disregarded the hypothesis.

But when you have a hypothesis upfront that is based on a system of tested theories, and if then this hypothesis matches with the correlation measure after building the hypothesis, then – yes then the likelihood that the hypothesis is true is high.

The same approach still makes sense in multivariate models such as causal network models. 

The example of the ancient philosopher HUME says: When a pool ball is struck by a stick – this could mean the stick causes the ball to move or the ball caused the stick to it them. Only your theory about pool billiards will tell you which version is more plausible.

Since now over 200 years this is the main idea behind social science. While being plausible, its practicability is so seldom questioned.

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The right approach at the right time

Yes, we all are hunting for the truth. A closer look will reveal that confirmatory approaches are well preferred in this circumstance:

  • You have a validated theory framework behind your hypotheses

  • With this you can assume that there is no unknown confounder that influences potential cause and effect at the same time

  • Your assumptions on the type of relationship (linear, nonlinear, free of the moderating condition, etc.) are ideally backed by a validated theory framework too.

If you have this at hand your confirmatory analysis will probably be the best method to use.

Sure if you are in doubt it might be wise to consider more explorative methods prior to using confirmatory methods. Here is a spectrum ranging from very explorative and qualitative to a quantitative approach that augments explorative with confirmatory approaches.

  • IDI – In-Depth Interview
  • Open-ended questionnaire questions
  • Data Mining
  • Causal Machine Learning

Most practitioners will agree: If you are new to a field, there is nothing better than talking face to face with customers in in-depth interviews. Yes, it is biased. But it helps you to understand holistically what might be important.

As always, the world is not black or white. There is something between pure qual and confirmatory quant.

There is a tendency to view confirmatory research as “better” than qualitative or quantitative explorative methods. Indeed, when the requirements are met, it is “better”. 

Based on your experience, how often are confirmatory methods applied when actually requirements are clearly violated? Would you still use it if you would know a viable alternative?

A true story from SONOS

David ran this customer satisfaction survey for SONOS. They reached out to every new customer one month after purchase. What David saw in the data was a big correlation between “excellent customer support” and loyalty/recommendation. It was validating what everybody believed. Not only that they build a multivariate model based on their hypotheses and well: the hypothesis was confirmed.

Taking a bit more explorative but still causal approach to the model (Causal Machine Learning) they included so-called context variables in the model. These are variables that may or may not explain outcomes or even moderate other effects. 

Long story short: it turns out that customer support only correlates with (and “explains”) loyalty, because those buyers that already had SONOS speakers needed less support, and therefore, had less trouble, and naturally are more loyal and recommend more. The statistical ”effect” was spurious and no confirmatory approach could ever find this out. Why? Because it was missing the confounding variable “already existing customer” in the model. 

Theory as well as “models are always wrong, some are useful” is a famous quote from George Cox.

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A true story from a beer brand

Jordi was running the Marketing and Sales of Warsteiner – a leading German beer brand. They wanted to relaunch the beer case and needed to find out if sales would drop or even increase with a better case. Investing in a new beer case is a nine-figure investment.

A/B testing is a scientific confirmatory experiment and is seen as a highly valid method. Doing that they found that the new case would lose nearly 10% of sales. 

A causal machine learning exercise however discovered something that (after the fact) made much more sense. The new case looks better and the customer like more in all relevant associations. But it lacks familiarity. The model showed that familiarity is one of the main drivers of purchase, which explains the drop in the A/B testing.

Now, any new design will lack familiarity. It grows over time, the more customers see the new design in the shops or in the commercials.

Now the cause-effect model could be used to understand this: When the new beer case design will be as familiar as the old beer case, the brand will sell 7% more, not 10% less.

A/B testing can be like comparing apples with oranges. If you sit on an imperfect hypothesis the rigor of your approach can be the cause of your failure.

A true story about Sales Modeling

Daniel was heading the commercial excellence program of SOLVAY – a pharmaceutical brand. He collected data on the activities of the salesforce and all marketing support activities. All this was fed into modeling to understand which actions drive most prescriptions.

One of the hypotheses of the confirmatory modeling was that providing product samples would drive prescriptions. But no matter how you tweak the modeling it always came to the conclusion: no significant impact.

Daniel tried a causal machine learning approach and was blown away by the elegance of the finding: Providing product samples has a nonlinear effect – an inverted U effect. Here is why.

If you provide samples it will help patients to try and eventually to use it long-term. However, if the physician has too many samples on stock, he becomes the sole source of the medication for more and more patience.

Some sales reps simply sampled too much, some not enough. Sampling makes sense but you need to have the right balance.

Nobody was hypothesizing the nonlinear effect. Still, causal machine learning could discover it and it turned out to be very useful.

A true story from a CX research

There is a seldom shared pain in confirmatory modeling: often times coefficients turn out to be counterintuitive, e.g. showing a negative effect instead of a positive one.

Mel was running a CX program for insurance and she had this very same problem. “Excellent service” had a negative impact on the Likelihood-to-recommend. This made no sense at all.

Later it turned out that his confirmatory approach was the route cause. It is common practice to only include explaining variables in a model if there is a good hypothesis for its impact.

The point that this procedure totally misses is that eliminating a cause from a model is a hypothesis on its own. It assumes this cause has no impact.

Instead, it’s a good practice in causal machine learning to add “context information” to the model. For Mel it changed everything to add the customer segment as an explaining variable.

It turned out that there are segments with higher expectations which are having a lower likelihood to recommend at the same service level. While they have seen at times even better service levels, it leads “service” to correlate slightly negatively with the likelihood to recommend.

Due to including the segment, a Causal Machine Learning can derive from data, that the outcome offset is due to the segment but better service still improves the outcome.

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A true story from T-MOBILE USA

It was 2013 when T-Mobile reinvented itself. The repositioning worked and the brand was growing. Only nobody exactly knew why. So they took a look at the brand tracking data. Modeling was done to reveal, which of the customer perceptions and service properties were causing the customer to come. With little success. No clear answer was available.

David – the head of insights at that time – was looking for fresh approaches and ran a causal machine learning exercise. The approach takes all information/variables available and structures it in a knowledge-led way: specific items about the service are seen as a potential cause. Purchase intent and consideration as an outcome. Other more vague items like brand perceptions are modeled as mediators. Also, context variables are included as potential moderators.

Someone had asked the team to include the item “T-Mobile is changing wireless for the better” into the tracker to measure whether or not the repositioning works. 

As one of many items that could be a mediator, it was included as such in the model – not theory-led but “possibility-led”. This move changed the whole history of the company with a 6-fold market evaluation a few years later.

The analysis revealed that none of the changes like end-of-contract-binding, flat rate or free-iPhone were directly driving the customer to buy directly. It was that those actions perfectly reasoned why the new positioning is valid. This new positioning perception -to be the un-carrier- was what attracted customers. The learning was to continue introducing new features that reasoned the very same positioning.

Only a modeling approach that allows handling vague hypotheses in an explorative setting was able to discover what the company needed for its growth.

What can we learn, what can we do?

It seems that confirmatory research has some blind spots. You don’t know what you don’t know.

The question is if it would make sense to change the way we look at it: Instead of asking “what is the best approach in general”, why can’t we ask “what is the right approach right now?”

Confirmatory research brings most certainty and validity – only if the requirements are met.

More exploratory research is by design made to make us learn more, designed to discover new knowledge.

Sure, the discovery comes with failure, but as examples show us, confirmatory research too often provides illusory security.

Shouldn’t a researcher ask himself: Do I want to discover or do I really want to validate?

Write me!  

Frank@cx-ai.com



p.s. Can “Causal AI” the new North star?

The recent Gartner Hypertrend Report now shows “CAUSAL AI” as one of the most promising technologies. It says that in 5 to 10 years the technology will be the tech that everyone needs to have to be not a laggard.

The two most promising platforms for Causal-AI are CausaLens which just received 50m funding and NEUSREL.com. While CausaLens is probably great for enjoying a good user interface, is -in my opinion- Neusrel technology-wise most advance.

At the end, you need to build your own opinion. 

Trying out is the way to know better 😊

"CX Analytics Masters" Course

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P.S. Would you like to get the complete & interactive FREE CX Measurement Guidance for your business in 2021?

Simply subscribe on the free “CX ANALYTICS MASTERS” course below and enjoy the above-mentioned training guidance in its Class # 1.

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Text Analytics to the Rescue

Text Analytics to the Rescue

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: July 5, 2022 * 5 min read

Text analytics had great success in recent years and most larger enterprises use them in CX in one way or the other. But still, most companies are far from getting a lot of value out of it. It’s more like a piece of software that someone plugged in the process. Here is why this is a problem and how companies can circumvent them.

Text analytics software is supposed to read and understand unstructured qualitative feedback. This understanding is defined by associating a verbatim with the correct theme. In short, the task is all about categorizing text feedback into a finite set of topics or categories.

First and still most used text analytics methods are unsupervised. They analyze a set of feedbacks and start to cluster and build topics. The problem: for simple matters, it makes even a good first impression. But when you look more closely, it doesn’t perform even nearly as well as a human reader would do.

The more specific and complex the feedback, the more apparent the lack of understanding becomes.

Sure, the algorithm has no deeper industry knowledge. So far there is no alternative than using AI that can be tough by domain experts – the human.

Even this – done naively – can do more harm than good. 

After all, even worth, at the end of a text analytics implementation this question always: Now what? What do we learn from this? Should we really fix this?

I like to share three principles that can help see the light.

The effort is worth it. Being able to leverage unstructured customer feedback is worth gold. It is truly customer-oriented to not torture respondents with lengthy closed-ended questionnaires, but simply ask a question or two and let customers express themselves in their own words.

This enables you to conduct research on every customer and every touchpoint, and get in-depth insights with a comparably simple research approach.

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How MICROSOFT drives value from unstructured feedback

The brand runs one of the world’s largest B2B customer trackers collecting over 200.000 feedback every year. Early on it adopted text analytics but the depth of insights a convention text analytics can extract from highly technical feedback is sobering. The categorization in 20 rather generic categories like Quality, Price, or Service was not very helpful and prone to failure as well.

Microsoft implemented already in 2019 a highly sophisticated text analytics approach. Trained by domain experts it gives nearly 200 highly granular topics. Not only that it proved that the accuracy even exceeds human categorization.

Not only that. The brand invested in an elaborated driver analysis – a causal machine learning that identifies the impact that an improvement in a topic would have on customer satisfaction. 

Now, instead of looking at 200 topics and how they changed, the team is focusing on those positive topics that will have the highest impact (hidden drivers) and those who are negatively important and too often mentioned at the same time (leakages).

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The Three Principles of Good Text Analytics for CX

These are the fundamental principles to consider in order to drive value from your customer’s unstructured feedback

1. Build your own codebook and use the right AI

It’s not enough to just buy a text analytics software or subscription. In most cases it does not fully satisfy the expectations your business partners have today. 

What you need is a text AI that you can train. 

This training starts by defining the set of themes (codebook) that you want your customer’s feedback gets quantified. You don’t want to outsource this to software. 

Be sure the software you are using has the right measures to validate its accuracy. E.g. you do not want to look at hit rates but F1 score.

 

2. Train it well

Garbage in garbage out: Training itself has some tricks and trades that you can either learn yourself or you can find external partners who are experienced in this.

It’s not enough to use a domain expert. The codebook should be documented well and the person should stay the same over time – or the handover phase should be extensive.

Over time training is changing the way your system categorizes feedback. Either you stop training to maintain consistency (not recommended, as the accuracy will decay over time) or you must rebaseline the past once in a while.

It’s important to communicate this expectation early on: No categorization will ever be perfect. 

 

3. Do NOT interpret text analytics – its just data

The greatest misconception about text analytics is jumping from data to conclusions. Intuitively businesses look at the most often mentioned topics. Because they believe these are the reasons for success or failure.

Even worse: this makes perfect sense as it is the answer to the question “why did you rate that way”. And the customer is telling us why.

However, it turns out that the frequency of mentionings and importance is largely uncorrelated.

In other words, whenever you give your business partners unguided access to frequencies of topics, they will most likely conclude with highly imperfect decisions.

The Microsoft case above shows how this issue must be solved.

Keep Yourself Updated

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State-of-the-Art Text Analytics

Text analytics is an amazing opportunity to discover unbiased insights in an easy and practical form. 

The state of the art relies on deep learning AI systems that are not only pre-trained but still can be trained by domain experts. This training requires some care and rigor in the process to avoid garbage-in-garbage-out.

To finally derive business value from text analytics it must be linked to some kind of driver analysis process. Even in this respect are (causal) machine learning approaches most appropriate.

A detailed education program on the state-of-the-art provides the world’s largest CX Analytics Masters course. It is open since spring 2021. Here is more https://www.cx-ai.com/cx-analytics-masters

The safe and easy way of course is the use vendors like www.cx-ai.com who have perfectionated all those measures and provide those processes as a managed service.

More background provides also my latest book “The CX Insights Manifesto” available at Amazon.com, co.uk, .ca, .it, .fr and .de.

Cheers 

Frank

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The Customer Experience of Pricing

The Customer Experience of Pricing

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: June 14, 2022 * 3 min read

When companies who do not position as cost leaders do NPS studies, VOC studies, or alike, one theme pops up regularly: “Too expensive”.

The intuitive interpretation is that lowering the price will improve the customer experience, satisfaction, and loyalty. That’s obvious.

But at the same time the question arises: Does the loss in margin really pays off thru less churn and higher cross-selling?

At the resource section of cx-ai.com and CX.AI’s Master Course you can learn, how you can find out, how much bottom-line impact lower prices would bring.

But the big question “how exactly will be existing and new customers respond to price changes?”

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Update your Pricing Know-how

Because of those reasons, as a CX professional, you need to be aware of the critical role pricing has for every company. This article gives a perfect introduction:

https://supra.tools/why-pricing-is-the-most-underestimated-consumer-goods-profit-lever-and-what-you-can-do-about-it

Once you have acknowledged the pricing decision as your #1 lever to manage your profitability, you know it’s important to measure customers’ willingness to pay.

Here is an overview of classic methods to accomplish this:

https://supra.tools/classic-survey-methods-for-finding-optimal-prices-in-focus-the-gabor-granger-method

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Everyone knows Conjoint analysis. Many view it as the holy grail of price measurement. Here is a review of different Conjoint methods and a conclusion that it is by no means a holy grail.

https://supra.tools/conjoint-surveys-for-pricing-consumer-goods-why-when-and-which-conjoint-analysis-makes-sense

The latest trends lead to methods that combine Neuroscience with Artificial Intelligence. The interesting part: it makes it simpler, easier and better. This article gives an overview.

https://supra.tools/implicit-intelligencetm-a-new-gold-standard-in-price-research

Keep Yourself Updated

On the Latest Indepth Thought-Leadership Articles From Frank Buckler

Whenever Pricing is an issue in your CX research…

Whenever “too expensive” is an issue in your CX research and stakeholders ask you what to do, you need to respond:

“I don’t know …  yet.”

But what you know is this is your moment to research the #1 profit lever of your company: pricing.

Having updated yourself in pricing research methods you are now equipped to decide whether Garbor Granger type methods, Conjoint analysis, or Implicit Intelligence tools might be good for you.

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P.S. Would you like to get the complete & interactive FREE CX Measurement Guidance for your business in 2021?

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Why “impossible” solutions are already around us – without our notice

Why “impossible” solutions are already around us – without our notice

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: May 3, 2022 * 5 min read

Too often companies try to solve their challenges and pains by going out onto the “market of solutions”. They ask some peers, to look at G2, Captera, or simply Google for it. Then vendors are evaluated. Most of the time though you find that there is no perfect solution yet available.

This article here is to remind you that this is actually not true! Chances are high that things ARE doable. Solutions ARE already possible. You just need to be brave enough, reach out to some talents and pioneers and let them do what can work – if you just try.

“Aren’t the major invents been already made? The wheel, the lamp, the computer?” This is what a fellow Ph.D. student asked me back then in 2000.

I was shocked that he ask such questions.

22 years later I need to conclude: Not only is it amazing which investors are popping up year by year. Even more: No matter which mission impossible you can think of, chances are that the solution is already around us. You just need to find it.

You don’t believe me?

You will – if you follow me back in time and let me guide you thru 3 examples from my field of expertise “Unearthing success drivers from data”

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The History of Key Driver Analysis

The basic concept behind driver analysis is the multiple regression which was published by Legendre in 1805 and by Gauss in 1809.

Unilever is using it since 1919 for Marketing Mix Modeling.

Even today too many marketing executives view the method as “advanced analytics”. It is still more common that practitioners to compare KPS or look at correlations instead of using regression.

Even worse, many software companies use multiple regression and call is “Artificial Intelligence”.

The potential that is available since 1805 is still 200 years after being largely underleveraged.

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The History of Machine Learning

The invention of the backpropagation concept was the ultimate breakthrough for machine learning.

Long time this invention was attributed to Rumelhart, Hinton & Williams published in 1986.

Just yesterday I learned that the technique was independently discovered many times, and had many predecessors dating to the 1960s – the earliest Henry J. Kelley in 1960 and by Arthur E. Bryson in 1961

Imagine! A lot of what we know as modern Artificial Intelligence is already possible since the 1960s. Its 60 years ago!

And I would not be surprised to learn that lots of pilot applications already happened in the 60s and 70s. We just don’t know about it.

The History of Causal Machine Learning

The concept of causality was a long time just a matter of philosophy  -mostly known by the work from Hume (1748).

Later the statistical framework of the multiple regression served as a means to calculate causal impact. Later major contributions followed by Granger in 1969, Pearl 2000 and Rubin 1974 added techniques to identify causal directions from data.

My own contribution in this space focusses to combine machine learning with causal inference. This for a practical reason: It turns out that it’s the most versatile, predictive, explanatory and practical way of modeling, reasoning and predicting.

I am publishing my work since 2001. It feels a bit like a treadmill. There is progress but large growth percentage on something small is still tiny. There are amazing success cases.

Still, the vast majority of possible applications don’t know about it. This will probably not change in the decades to come.

Now looking back to Key Driver Analysis and to Machine Learning gives me patients. When AI needed 60 years to prosper, I can not expect Causal AI to do in 20.

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My Take Away

Imagine you are in 1880 running a consumer product business. Key Driver Analysis would be available already to perform Marketing Mix Modeling (you just need to run it by paper and pencil).

Imagine you are in the 70s building one of the first home computers like the Mac – you could have built-in Artificial Intelligence already back then – if you would have understood the power of it.

Imagine you are who you are today. You are eager to understand better than anyone what are the hidden causal reasons, the ultimate actions that most effectively drive success. You could run causal machine learning already today. Learn how things relate and influence each other.

Just DM me 😉

Frank

(frank@cx-ai.com)

"CX Analytics Masters" Course

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MULTIPLY! The Hidden Mechanics of Business Success Drivers

MULTIPLY! The Hidden Mechanics of Business Success Drivers

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: April 05, 2022 * 5 min read

We tend to think about business as if each department or each task would contribute a fixed piece of value to the company’s success. With this in mind, we divide companies into departments and teams, and resources into budgets. Hope this article will convince you: The underlying belief is flawed. A new view changes how we set up strategies, budgets, and project plans. It even changes which statistical method we use to measure the importance of business success drivers.
Why 95% of product launches fail

Claudia worked on this piece of analytics for the last 6 months. It was nerve-wracking. She was working for a large global provider of syndicated market research that had assembled a mind-blowing dataset: Data about all new product launches in CPG in the US. Details product perception and presales purchase intent, sales data, distribution data, everything.

Claudia’s task was to build a mechanism that predicts -based on presales shopper assessments- whether or not a product will sell and survive.

Nothing worked. Purchase intent correlated with success ZERO. Regression delivered R2 close to ZERO. Then, some destiny let the company reach out to us at Success Drivers. 

We ran our holistic causal machine learning approach and achieved great explanation power but something was still odd. Typically, the method gives great transparency about causal relationships, nonlinearities, and two-way interactions of any kind.

We paused. The look at the skewed distribution of success (very few are very successful) brought the Eureka moment. Such a distribution evolved only if you multiply four or more independent success drivers. Its rare that all 4 hit the mark at a time, so it becomes rare that a product survives the first year after lunch.

Suddenly all this made sense. You will not survive with bad packaging or messaging, hardly with a missing brand. You will not survive if the pricing is not appropriate. You will not survive if the product is not that great, so people don’t want to rebuy it. You will not survive if retailers do not put the product on their shelves. 

There are so many MUST Dos, it is likely that you miss just one of them. If you do, you fail.

Methodically speaking, this is a 4-way interaction – the success can be computed by multiplying success drivers variables. Causal machine learning can learn this out of data.

Later, I realized this learning applies to some degree to all business success drivers. Drivers seemed to work indeed independently on an incremental/micro level, but not on a macro level. This is why we can get easily fooled.

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Do not ride a dead horse

The other year we were starting to promote a new insights product and launched a new website for it. Thru multiple channels, we were planning to drive traffic to it. But the website was not converting into demo requests.

We hired conversion experts, UX specialists, and multiple star copywriters. We optimized and ran A/B testing. It got better and better. We thought. Actually, the performance stayed very bad.

Then I talked with Pedro – a Conversion rate expert – and he gave me the Eureka moment. “Your website is not the problem,” he said. It seemed that the audience does not resonate with the offering. 

Clear if the audience you attract is not exactly those people who may need your product, the website cant convert. If your product is not solving an obvious pain point of the audience it will have a hard time ever converting.

The greatest website of all times will not sell, if your product doesn’t solve an urgent need.

It’s like riding a dead horse. Lipstick on a pick. Success Drivers do not compensate each other, they multiply. Any multiplication with zero, stays zero.

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Business Analytics to the Rescue

Senior leadership use to ask “how important is x or y”. The answer is always “it depends”. Even worse, the truth about the importance constantly changes.

If you fix the pricing of your product, it may still not fly, simply because you still have to manage that retailers put it again back on the shelves.

If you fix your biggest bottleneck the next bottleneck pops up soon.

Like a water hose with multiple holes. If you fix one hole, the others start leaking even more. Until you fixed them all.

Let’s take Customer Experience Management. If your processes do not work, your apps crash, your telephone routes people nowhere, when the basics are misaligned, everything else is not important. 

Success Drivers multiply. Any multiplication with zero stays zero.

It’s fair to assume that every business is operating a chain with crucial elements. The weakest link defines the total chain’s performance.

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When you measure the importance

When you next time tries to measure the importance of success drivers, think twice.

Most methods out there are assuming that success drivers are independently important. No matter if you run a regression, econometric modeling, Bayesian nets or you name it.

Maybe they allow for build-in assumptions on interactions/multiplications – but you need to know upfront which one. This is an unrealistic ask that 99% of businesses cant specify upfront.

Using cause machine learning (at CX-AI.com we use NEUSREL neusrel.com) instead gives you the full flexibility to discover “what is” instead of “what should be”.

What are your experiences in this?

Write me!

Frank   

(frank@cx-ai.com)

"CX Analytics Masters" Course

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The 3 things I learning from a JustEat driver

The 3 Things I Learned From a JustEat Driver

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: March 08, 2022 * 4 min read

Every morning I am cycling to work. This year I decided to not take the direct route but to cycle thru the park along beautiful canals. Suddenly I passed by the Italian embassy that had a mobile coffee booth in front of it. Here I met Arnd Hallemeier – a JustEast food bicyclist. (JustEat is the equivalent of UBER Eats – and branded Lieferando in Germany) For weeks every morning Arnd and I talk and I am amazed at how much I learned from him about Customer Experience management.

These are the three pillars I would bundle my learnings

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#1 Be interested in other people

When I met Arnd for the first time, he was leaning in admiration over my bike. “It’s a racing frame,” he said. I didn’t know and of the cause was flattered.

After meeting him more often, I realized how many other people just like me were stopping by every morning to have a coffee – at this VERY lonely coffee booth. Arnd knows all of them.

How? He simply is interested in them. And he spends time – one hour each day. He loves to meet people and enjoys company.

After stopping by every morning, I believe that people are not just taking a coffee break because of the magnificent coffee. They now enjoy meeting strangers.

Actually, because of Arnd, I know many others now too. I know Philipp the construction worker, Jan the banker, Doro the accountant.

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#2 Proactively communicate possible challenges and show empathy

Arnd told me about his job. Whenever there is the slightest delay – he calls the recipient giving background on the delay in person. The result – doubling tips.

Sometimes the food delivery is not complete. Arnd immediately takes photographs of everything promising to handle in the complaint. The result – immediate doubling tips. He turns a complaint into praise.

Thinking in the hearts and minds of customers will automatically bring you to these actions. Customers will thank you for this.

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#3 Hire people who want to get paid for their hobby

Now, this is the bummer. Arnd is 67. He could stay home end enjoy retirement. Instead, when retiring he hired a coach on how to maximize his life expectancy. 

The insight: become a JustEat bike driver and even spend holidays with bike trips crossing continents.

Arnd works because he loves cycling. And he even gets paid for his hobby.

When you are looking for your next customer-facing hire: look for people who love to do the job as a hobby. Your customers will be raving.

###

What is your take? 

Let me know. I’ll appreciate it,

Frank (frank@cx-ai.com)

"CX Analytics Masters" Course

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How To Convince an Opponent

How to Convince an Opponent

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: February 22, 2022 * 5 min read

Let’s face it. Convincing is impossible. Full stop.

The same way a conservative can not convert a serious liberal (vise versa), or a government can not convince a die-hard anti-vaxxing activist (vise versa), the same way a Creative Director will not convince the head of quant modeling, that quant is about counting pees.

While I feel too that the mission is impossible, I found mind-blowing evidence that gave me hope. I found inspiring pieces of science that show the tricks and trades to bring people’s convictions closer together. Imagine how powerful it would be if company silos would no longer fight each other but become ally’s in one common mission … if political streams would cooperate, not shout at each other.

This story is about three scientifically validated principles on how “convincing” works. Spoiler: whoever wants to convince, will fail!

David was just been named the Customer Experience leader in a bank and was full enthusiasm, he build a full blows CX Management system including a state-of-the-art customer experience insights system. Everything went well and he got lots of compliments.

Until this day, when the second time in a row the NPS dropped, although the organization was using his system.

“What’s wrong Dave”, did Frank, the CEO, stare at him. Leaning back in his black-leathered chair he was expecting a promising answer.

David had a suspicion. It’s hard to improve customer experience when the CFO sees them just as costs … if the COO is looking more at efficiencies and thinking about CX as fuzzy fluff.

David’s new mission was to make his company more customer-centric. “CX is not just something for marketing or service people. If the whole company has not had a customer-centric culture, it gets hard.”

David reached out to Mel, the CMO of the company. “You need to explain better and bring data and evidence that it works,” she said. David knew Mel is always right.

Six months later David realized however, this didn’t was satisfactory either. Easier explanations, nicer dashboards, and clear impact fiscal impact estimators still left the C-Suite hesitant.

“Why?” David asked himself.

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PRINCIPLE #1: Build Common Ground

As Julia Galef points out in her TED talk [1]: science found clear trades of people, that are able to see the truth in data: You must be CURIOUS to learn new things. You must be OPEN to new ideas and GROUNDED so that you are ok if the evidence will prove you wrong.

While we can’t make everyone Curious, Open, and Grounded, we can spark this trade in every person by a simple rule: 

Speak to your “opponents” personal interests. 

A creative director wants to win creative awards and wants it to be seen as a creative genius. The CFO wants to create bottom line profits and the COO wants to cut costs. The anti-vaxxer wants freedom and the government re-elected. Become crystal clear about the mutual interest of your opponents, accept them, and find ways to build bridges.

If you want to convince your COO, talk about how a better customer experience will create less friction and more efficiencies. 

But it does not stop there. Research by Stanford sociologist Robb Willer found it takes more to win opponents’ hearts [2]. It’s not about what you say but how you say it. In essence, you need to bring transparency about your and your opponent’s underlying values. According to Willer: while liberals are convinced by highlighting care and equality, are conservatives more receptive to values like group loyalty, respect for authority, and purity. 

As Julia Dhar – three-time World School Debate Championship winners- puts it [3]: You need to build common ground. You need to find a shared purpose. Something that unites you both first. Only then the debate can be productive.

Convincing is not about you and your opinion. It’s first of all about them, about their interests and values.

David took up the challenge. He first verbalized the top priorities of the board and emphasized that they are interdependent instead of competing. He managed to even estimate the impact of CX initiatives not only on the top line (increase customer value) but also how they can holistically decrease costs. 

Still. Staring eyes.

“What’s wrong?” David asked himself. “didn’t I bridge interest and carefully catered their values?”

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Principle #2: Create Positive Emotions

It’s not enough to say the right things. You need to wrap it in a story. Science found that an impactful story must be also positive to drive change in conviction [4]. No surprise that negative stories of drug prevention campaigns do not work.

As Professor Jamil Zaki found is that the longer a debate lasts the more cynical we tend to get. This has devastating effects. Cynicism may be pampering our own bubble but will close opponents’ hearts forever. Instead, any attempt to convince must be filled with optimism that a common solution is possible. This has proven effective in scientific experiments [6].

Other research emphasizes this and elaborates: stories must not only be positive but be designed to create emotional moments [5]. Only if you touch people, you move them.

A story is much more than a narrative. It relies on a relatable hero, starting with a description of the context, followed by an unfortunate event that disrupts the balance of life, the heroes measured to restore the balance typically fails at least once until this final moment where the heroes have the final chance to create a happy end.

David went on. In his LinkedIn feed popped up Jeff. He was a peer CX leader in a health insurance company. Jeff was bragging about his success teaming up with the whole organization in the pursuit of customer-centricity. 

David convinced Jeff to record an interview with him over Zoom. Jeff happily illustrated his story in 3 minutes and even had invited his COO to talk highly about Jeff.

David showed this cut 2min video at the next board meeting. Everyone was very positive.

But the next day David met his CEO who had sensed something different. “David” he said, “Although your convincing techniques are great, the team is still hesitant for a simple reason

Principle #3: Build TRUST

Science found that presenting selected “convincing” facts typically has the OPPOSITE effect – referred to as “backfire” or “boomerang effect” [7]. 

This is why you need to start any convincing by acknowledging the opponent’s view. In fact, any opinion holds a part of the total truth.

If there is even a slides piece you can relate with you can start like “I think in part you are right. Can I elaborate on the things I believe I can add to improve the total picture?”

When confronted with some arguments you find ridiculous, you can say “I have never thought about this exactly that way. What can you share so that I can see what you see”

The underlying process is that you are framing yourself not as an opponent but as a friend. If you are an “opponent” in the recipient’s mind  – you are lost!

“Most people are willing to learn, but very few are willing to be taught.” Winston Churchill 

This is one of the reasons that it turned out in scientific experiments, that stories told in the the  person are more convincing than in the first person. This third person has a clean sheet and can be better related with. (this is why this article tells Davids story, not mine 😉 ). 

The third person is not suspicious of the intent to change the recipient’s mind [8]. It even doesn’t matter if the story is true or fictive.

Whenever it stinks like you are here to convince someone, you get resistance. 

Let it go! “Aim for progress, not victory” is what Julia Dhar is recommending [9].

Your reputation is your condensed past. Did you try to trick or manipulate others? Then your reputation might be to be an enemy. This is a hopeless start to convince others.  FOX will never convince a liberal and CNN never do so with die-hard conservatives.

What does it all mean: the more you want to convince, the less you will succeed! 

Work on your mindset. Accept people’s right to own their own opinion. Accept that every one of us holds a different piece of the truth. Only together we can solve the puzzle of truth.

With this mindset, good “convincing” is better described as “inspiring” and “inviting to a collaborative discourse”

David realized that someone else needs to lead the movement. His reputation was too much about fighting for a customer than finding a collaborative solution.

It was this reference video with the shining eyes of this insurances COO that made Frank. David’s CEO, think.

David had won Frank’s heart. Frank could start from scratch with a blank reputation on customer-centricity. He took the forge and waved in the new theme in all his dialogs. 

Here is what David had learned along his way on how to convince others formerly seen as “opponents”

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In a Nutshell

Principle#1 – Build Common Ground

  • Speak to your “opponents” personal interests
  • Argue on the “opponents” personal values

Principle#2 – Create Positive Emotions

  • Be positive and optimistic rather than negative and cynic
  • Weave your information in a story 
  • Make sure this story creates an emotional aha moment

Principle#3 – Build Trust

  • Do not intent to convince, intent to inspire
  • Start always acknowledging the opponent’s view
  • Guard your reputation as your biggest asset. In doubt, let someone with a clean sheet help you.

Wait, wait, wait! Isn’t a “convincing formula” very manipulative?

“Manipulation” is a mindset and the “formula” recommends to shy away from it because it does not work. Instead, please accept that every human has a right to his own opinion. Ironically this mindset is mandatory to have an impact on someone’s opinion. I don’t think there is something wrong with inspiring other people. Actually, it is very kind to help others to see the truth.

In this article, I do not suggest NLP-type persuasion techniques to trick the opponent’s unconscious self. Instead, it is all about giving information that is relevant to the person. It is all about making clear that you are not an opponent but in the same boat. It is all about being helpful by sharing information in a way it can be understood.

What’s wrong with that?

Isn’t it better to stay who you are – as authentic as possible – you, giving your point of view?

There is nothing more authentic and raw than going naked with unwashed hair to a wedding. But this is rude and disrespectful.

The same is to simply say what you think. It does not acknowledge that the other person may also possess a piece of the truth. It implicitly conveys the message “you are stupid and/or asocial”. 

Most of all, it is not only a waste of time and energy. It will create negative energy that will backfire.

The same punishment you will have when showing up naked at your friend’s wedding, you will receive by just being blunt and “honest”.

Being strategic when trying to inspire others is the most respectful, human, and productive thing you can do.

Convincing? Isn’t  “Marketing Communication” another term for it? Old wine in new pipes?

I used to think so. But I could not be more wrong. While diving into the topic I realized that marketing is a fundamentally different discipline. Here is why.

Marketing is trying to move people who are mostly already convinced that they have a problem or there is a job to be done. If I have hunger, I am looking for a product and I feel like eating. Marketing is trying to persuade that a particular product does the job best.

“Convincing” is the task to change the whole attitude of a person. To convince is converting a hater into a lover. In marketing terms: “Convincing” is trying to sell tree huggers a 1000 PS Lamborghini.

Marketing avoids these challenges for a good reason. It’s just too costly.

But when politics invest in for instance “vaccination campaigns”, they hire marketing experts that have no clue about convincing. The result is a growing -instead of a reduced- amount of anti-vaxxer. It is a total waste of tax money.

What do you think? Did I miss something?

Let me know!

Frank
[Frank@cx-ai.com]

"CX Analytics Masters" Course

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The Future of Customer Insights

Top 5 Future Trends of Customer Insights

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: February 8, 2022 * 5 min read

Multiple trends are flooding the customer insights industry. AI-powered solutions, more and more DIY tools and “Restec”, as well as the ask to tap on new data sources. I will take here a birds-eye view to distill the essence out of the noise. This can serve as your guiding rails for your 5-year insights roadmap.
Trend #1 – More data sources

Insights were all about creating a questionnaire and running a survey in the past.

This time is gone.

The classical survey will always be an important source of information for the same reason that “talking with customers” will never be old-fashioned.

But technology now equips us to get information without asking and digitalization enables us to connect all those data sources:

  1. Behavioral in-survey: Text analytics today enables us to quantify open ends. Within a survey, we can also ask for audio feedback and read not only text, but emotional value from this. We can record the video, get facial expressions, and learn something about customers’ living context. We can learn from implicit, time-based feedback about underlying attitudes.
     
  2. Transactional: Data about a purchase is undoubtedly more valuable than expressed willingness to purchase in a survey. As digitalization evolved, nearly all customer actions are digital now, along with growing information around the customer. Linking this data with other sources opens a whole new goldmine.

  3. Social listening: Social media debates, customer discussion groups, ecomm ratings with comments – everything that consumers talking about in public spaces can be a valuable source of information, especially at our fingertips, for virtually little cost.

Predictive enrichment: Today, AI systems are trained by eye-tracking data. Without any eye-tracking, they can predict the typical eye attention. In the same way, you can predict psychological characteristics based on some basic variables. You may be able to predict customer preferences based on the type of words they use. All this is based on a simple idea: Build a predictive model using a pilot dataset, then apply it at scale by relying on a few key predictor variables.

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#2 Merge Qual with Quant

The market research and customer insights world has divided itself into Venusian and Marsian: Qual vs. Quant.

True is that businesses need both.

But both fields could advance by learning from each other. Quant can improve by becoming more explorative rather than just theory-testing. Qual can improve by introducing more rigor and validity control thru the aid of quantification.

AI makes it possible to quantify text, audio, and video even more reliable than humans at low costs. Tools evolve that mimic qualitative conversations with respondents and perform short qualitative interviews.

This quantified qualitative information can now be used in quantitative modeling. It can be used to discover but also to predict impact.

Both worlds will and must be merged.

The process of hypotheses discovery and validation will not be a binary qual vs. quant. We will instead see a continuum where “pure qual in-depth interviews” on the one extreme and theory-testing models at the other extreme will be the exception.

The vast amount of research will play between those extremes.

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#3 Link Actions with Outcomes

What’s the worth of understanding that, say 50% of this speaker brands customers “love the sound”. Mmmh, ok isn’t it  “a lot”?

What’s the worse of knowing that 23% of speaker brands are “music enthusiasts”. Mmmmh.

The point is that this is not insights, just aggregated data.

An insight is for instance to know that controlling a most reliable music stream to the speaker is not yet always achieved but when it is, the user is by .5 scale points more likely to recommend, which translates into 23 Mio. aggregated Customer Lifetime value.

What is customer insights? The quest to learn what drives customer behavior, more specifically, which actions influence intended commercial outcome.

With this, customer insights is most and foremost, the process to understand the hidden link between actions and outcomes.

The more we have that growing wealth of data at our fingertips, the better we are capable of fulfilling this understanding of “customer insights”.

Not only that. The more data we have, the more we don’t see the forest, but just trees. It’s the job of customer insights to not just aggregate, slice and dice this data. This is not just “boring” or confusing. It is not helpful.

Helpful is when we build causal machine learning models that help us to explore how actions and outcomes are linked.

#4 Create a Learning Loop

Last year we at Success Drivers tried to optimize subject lines for emails that go out to our prospective customers. We ran a questionnaire to assess 50 different ideas.

Then we tested the two best and two of the worst-performing subject lines in real life. Guess what happened!

One of the worst-performing was outperforming regarding open rates, while the top-rated subject line did not perform at all.

We further analyzed the underlying properties of all tested subject lines with an AI called neuroflash. It gives you the world of associations behind the words used.

A good subject line was “Conquer with Billy Beans AI”. We learned that the dominant language would trigger open rates. At scale, this learning would come from Causal AI.

Next, we ask another AI (GPT3) to write us related alternatives. We tried them.

This process produced the winning subject line “Straight to the point, FIRSTNAME” which showed open rates of 50% as opposed to 10%.

Long story short: Experiments are the gold standard of insights. In the future, winning businesses will weave in continuous experimentation into the daily workstream. With AI, as the example shows above, we can speed up the learning process. Learning that would have taken two years with AB testing in the past will take two months or even two weeks.

Subject lines are just the beginning. You can do this with pictures. You can do it with each personal service interaction, with each sales call your company is doing. Track, experiment, model, iterate.

This loop will open a whole new universe of insights.

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#5 Insights needs its own marketing

Insights is like watching sports.

Everyone has an opinion. Everyone feels he can see what’s going on just by luring at data. Everyone feels he can be the coach.

This is even more true as more and more people within an organization are touching data. IT does it. Data Science does it. Everyone does it somehow.

Providing access to dashboards to a larger group in an organization can do more harm than good. This way, everyone draws the conclusion which fits his world.

The tendency is that “customer insights” is just running surveys.

It would be best if you staked out your terrain. Then it would help if you convinced others. This is called “marketing”.

Convincing others starts with empathy. It begins with understanding what key stakeholders genuinely care for. If it is “dollar”, give them the impact on profits.

Then educate on what you can deliver and why it requires marketing science to create useful customer insights.

A customer insights role needs an explicit internal marketing program. It is needed to bring insights into actions.

Building this marketing program starts with researching what is important to internal stakeholders. It continues with defining your brand. This is performed using a continuous content marketing and education program. 

Name yourself to advocate for “how to turn data into insights” in your company. 

If done right, you will be implicitely leading decision making.

 

# # #

What is your take? Which major trends did I miss?

Thanks so much,

Frank (frank@cx-ai.com)

"CX Analytics Masters" Course

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P.S. Would you like to get the complete & interactive FREE CX Measurement Guidance for your business in 2021?

Simply subscribe on the free “CX ANALYTICS MASTERS” course below and enjoy the above-mentioned training guidance in its Class # 1.

“Solves key challenges in CX analytics”

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Does it Really Costs Five Times More to Acquire than to Retain a Customer?

Does it Really Costs Five Times More to Acquire than to Retain a Customer?

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: January 25, 2022 * 5 min read

Since the late 1980s, several sources claimed retaining a customer is 5X cheaper than acquiring one. Later a Harvard Business Review article “The Profitable Art of Service Recovery” rephrased the myth with a simple calculation based on a set of assumptions. Since then any random management consultant has been quoting this “mind experiment” as scientific proof. The theory behind it is so pervasively intuitive that it has stood unchallenged for more than 30 years. To my knowledge, it has never been scientifically validated.
It Poses Even the Wrong Question

What are the costs of retaining a customer? Isn’t having an acceptable product/service and acceptable support typically what retains them? 

Most customers are inert. They change only if there is a strong reason.

How much of your core service can be really attributed to retention?

Same with customer acquisition. There is a share of customers who come by word of mouth or they find you on their own by researching hard enough. 

How much of new customer business is really the outcome of an investment in customer acquisition (marketing and sales)?

This comparison is not just unfair. It is not even relevant.

Why? A theory is only relevant when it informs a decision. Are you really considering closing customer support or marketing and sales entirely?

No, you don’t. The decision is whether to invest more or less in retention or acquisition.

This means: comparing customer acquisition costs with retention costs answers the wrong question.

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It’s not about costs, its about ROI

Is this a win when a bank wins two retail customers but loses one affluent client?

It’s a huge loss.

Of cause it depends on what kind of customers you acquire or retain and which products you sell to new vs. existing clients.

So when doing the math, it’s only worth the work if you look at both sides of the coin.

The right question to pose then is

“Does it has Five Times higher ROI to Retain than to Acquire an Additional Customer?”

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Do Not Trade CX with Acquisition

“How well does a knife cut meat?” Answer: It depends. Blunt knives do not cut at all. “Whats an impact of advertising?” Answer: It depends. Bad ads have no impact at all.

What’s the impact of customer retention initiatives?

We at CX.AI did the exercise to measure the ROI of CX several times for clients and compared it with customer acquisition. 

When modeling on real customer data, you can quantify the impact of certain retention or acquisition actions.

With this, you can calculate the ROI of those actions. Here is what we learned:

Customer loyalty initiatives vary largely in it’s ROI. A typical range is anything between 0 to 10X payback of the investment.

Customer acquisition ROI is much better understood. As a rule of thumb, 5X is what can be expected. But still, practices easily vary from 0 to 10X as well.

As you can imagine the actual picture heavily depends on the domain and industries. Markets with huge growth have much lower customer acquisition costs and businesses in saturated markets. 

The same is true for customer loyalty. According to research from the worlds largest marketing institute Ehrenberg-Bass, the market leaders always have higher loyalty. If not, they do not serve the same market.

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Does CX Make Any Sense Than?

If driving loyalty is not necessarily more impactful than acquisition, when increasing market share always comes with a higher loyalty, then, is managing your customer experience really important.

The simple answer is: YES.

It doesn’t need a comparison with marketing and sales.

What it takes is an estimation of the ROI (plus the risk profile) of potential CX initiatives. (this is what CX.AI has now integrated as a feature)

Senior leadership should ask in the same way it asks for CX ROI then also for marketing and sales initiatives ROI in order to pick the highest ROI strategies. 

The simple decision logic is to pick those initiatives with the highest ROI / Risk ratio and to not simply believe CX or Marketing has an impact. It only has an effect if done with mastery.

I recently hosted a webinar on this topic. Happy to share the link to the recording (frank@cx-ai.com)

If you want to deep dive into this, our CX Analytics Masters Course takes 5h to guide you through the how’s, tricks, and trades.

# # #

What is your take? 

What do you miss in this article?

Let me know and I will improve 😉

Thanks so much,

Frank (frank@cx-ai.com)

"CX Analytics Masters" Course

b2

P.S. Would you like to get the complete & interactive FREE CX Measurement Guidance for your business in 2021?

Simply subscribe on the free “CX ANALYTICS MASTERS” course below and enjoy the above-mentioned training guidance in its Class # 1.

“Solves key challenges in CX analytics”

Big Love to All Our Readers Around the World

IMPRINT
Our Group: www.Success-Drivers.com

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Copyright © 2021. All rights reserved.

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