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A Call for Corporate CX Pioneers

Founder of 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.


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
  • 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 

Do you have an opinion on what needs to change? Write me via 

Do you want to suggest a contact of yours and engage him/her? Write me via 

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