AI-Powered Insights: The Liberation From Spurious Correlations

March 21, 2024

Chapter 5

THE DOING - How to Win Your Organization on This

Dr. Frank Buckler

Founder, Success Drivers

When I talk to managers in larger companies, one thing always emerges. The big problem is often not figuring out what could be done better. Rather, it's how to get the organization on board and convince them to try out a better solution, implement it and systematically apply their findings.

Many of our customers are far greater experts in this subject than I am. I’m just summarizing here what I’ve learned from pioneers in marketing for large companies.

I have noticed three areas to consider if you want to implement the right methodology as an internal pioneer: 

  1. What prerequisites should you create for the solution to be accepted?

  2. Who should they convince to take the entire organization with them?

  3. How can you go about convincing people?

How to Proceed

Introducing a new solution into a company is like launching an innovative product on the market. Adoption research has intensively addressed this problem and has come up with eight universal reasons why innovations fail.

After publishing our Causal AI method in a peer-reviewed journal in 2008, I started helping companies as well as PhD and Master’s students to use the software for themselves. One master’s student was Daniel. He was doing his Masters part-time while working for a telecom company on the implementation of mobile payment solutions. I learned all about the eight adoption barriers from Daniel, as he built a Causal AI model.

In fact, the model revealed something unexpected for me. It wasn’t the utility or the simplicity of the product that was the problem in being accepted by the customer. Mobile payment just wasn’t “the process” or fit with the habits people lived by when paying for shopping. This change in habitual processes was the key barrier to product growth.

Since then, we at Success Drivers have been using these eight criteria when evaluating new products. The introduction of Causal AI in your company is also a question of adoption and it makes sense to optimize this task along the following eight aspects:

  1. Benefits: Does Causal AI solve a real problem?

  2. Certainty: Is the buyer sure that the effort and investment in Causal AI is worthwhile?

  3. Uniqueness: Is there an impression of “this already exists” or “we have something similar”?

  4. Ease of use: Is the solution easy to use?

  5. Quick start: Can be used quickly with little time expenditure.

  6. Does it fit into existing processes: Or does the company have to learn to work in completely new processes?

  7. Attractive design: Do you like the aesthetic appearance or does the design make you feel uncomfortable?

  8. Trusted brand: Is the solution offered by a trusted brand?

The importance of the eight factors differs depending on the product category. For Causal AI, all of the eight factors are certainly of some relevance. But in my experience, the first three factors are the most important:

Adoption drivers: Benefits

I often give keynotes on the topic of Causal AI in marketing. Among other things, I often speak at fundraising conferences. In discussions with fundraisers, one argument proved to be the most effective. The decision-makers were already aware that we can help them make better decisions or improve customer loyalty. 

But what really hit home was this argument: NGOs manage donations and have to pay for their marketing and sales (fundraising). Every euro that is invested inefficiently does not benefit charitable causes. Therefore, not using Causal AI in fundraising is negligent and looking at ROI instead of pure costs is a moral duty for the fundraiser. With this argument, I hit the existing problem on the head.

In this book I have tried to illustrate the advantages of the Causal AI methodology. Which of them solve a real existing problem for you? This can change depending on the situation. 

Are you currently experiencing a drop in the performance of certain forecasting systems? Then make the benefits clear by avoiding model drift. 

Is discrimination by AI a hot topic? Then focus on this.

Or is it a problem that the insights of existing systems lead to actions and initiatives that show little success? Then Causal AI could offer the solution here.

Do your areas of application lie in discovering strategic insights, creating automated individual marketing decisions such as retention measures? Or is there a need for more effective generation of marketing material through AI enabled by Causal AI?

The key is to find a problem area as well as an area of application that addresses a real problem. A real problem is a topic where a credible solution option always meets with an open ear. “Oh, that works?” is the question you hear when you propose such an option.

Adoption driver: Certainty

Isn’t the problem with the marketing of our time that customers no longer believe anything? We are bombarded with unbelievable claims day in, day out. Many customers have learned for themselves that you can’t really believe the marketing promises. It feels as if everything is “inflated” and overly important things are left out.

As a result, marketing as a source of information is becoming increasingly difficult. Content marketing has evolved from this. Peer-to-peer networks are sprouting up. People are more likely to believe their colleagues.

If one doubts that benefits really occur, the actual perceived benefit is small. It can be reduced to a formula:

Want to have = benefit minus effort times certainty

This is precisely why a pilot project is usually a good idea. However, it must be set up in such a way that the true benefits can really be experienced. It should be set up with the intention of building the full version if it is successful. This implies that the relevant people are involved in the pilot so that the requirements of the organization are already clear in the pilot.

Adoption driver: Uniqueness 

Before the streaming speaker brand SONOS wanted to launch its mobile models Move and Roam, we at Success Drivers had the pleasure of testing the product “in the market” with a survey. We implicitly determined the willingness to pay (Implicit Price Intelligence) and determined how the products were perceived on the eight adoption drivers. Causal AI then determined that when consumers perceived the product as unique, their willingness to buy and pay increased significantly. It turned out that most target customers did not understand the special difference to conventional Bluetooth speakers. This was the main roadblock for the product launch.

By emphasizing wifi and connecting speakers in their communication, Sonos made the launch a complete success.

I think it’s similar with the introduction of causal AI. Many data scientists simply don’t know it. There are many statistical methods that purport to measure the effect of a cause. I still get asked sometimes if correlation wouldn’t be enough.

Multivariate regression is still very often used and sold today as “driver analysis”. When it is important for companies to capture non-linearities, many mafo agencies switch to Shapley Value Regression, but scientific studies show that this method produces misleading results . 

Recently, market research software providers have come up with an “AI Driver Analysis”. This consists of a random forest method and an impact measurement borrowed from the Explainable AI area. If you have not dealt with the requirements for causality, you may well get confused by the abundance of methods. This is because they all claim to measure the influence of causes. This is the reason why I am writing this book. I want to make clear what it takes to understand what drives success (causality).

A random forest method (or other common AI methods) does not meet the requirements that we developed in the third chapter. The more the drivers correlate with each other, the more distorted and therefore unusable the results will be.

Who to Target

When David from T-Mobile USA commissioned us at the time, it was primarily his initiative and his decision. Sure, he got the ok from his boss, but she trusted him and didn’t start her own evaluation. He listened to everything in two online meetings with me and discussed his challenges with me. It was then clear to him that he wanted to pilot the method. The results were so convincing that he had a retainer set up.

After all these years, I have always wondered what kind of people want to try out Causal AI without months of internal discussions. I think the term “early adopter” comes closest. For most people – including managers – it is not enough to find something plausible or to obtain evidence of its effectiveness. They need social backing (in B2B: from the organization). References alone are not enough either. “They all have logo wallpapers” is what you then hear.

According to marketing literature, around 15% of people are early adopters. These are people for whom their own professional judgment is enough to decide whether something makes sense and should be tried out.

When I presented our work with SONOS at the ESOMAR Congress 2018, the head of market research at Microsoft – Reed Cundiff – was on the panel that selected the presentations. He immediately realized that our technology could help Microsoft.

 “If the head of one of the largest market research departments in the world gives his department heads a hint, it will be done” you might think. But in my experience, that’s not how it works. For good reason, superiors hardly interfere in the work of their teams, who then find ways to “get rid of” projects that, in their view, deliver little added value.

So we were lucky again that another early adopter – Dr. Rajul Jain – took over the testing of our method. She was so convinced of the methodology that she was committed to doing the internal persuasion work.

In my opinion, it is therefore crucial to identify early adopters in the company. You need these people to be able to gain a foothold in a larger company. 

If you are an early adopter yourself, you would do well to find allied early adopters. This can slowly create a critical mass that encourages the sluggish masses to follow.

If your company is so large that you cannot assess all your colleagues well, ChatGPT can actually help (no fun!) We tried it ourselves once. We used the LinkedIn Navigator to find 1000 people who belonged to the target group. Then we searched for each person’s self-description in LinkedIn and asked ChatGPT to what extent this person is an early adapter. The people scored as the top 50 received a book with a cover letter from me. 

I know from experience that such mailings very rarely result in responses or even project inquiries. This time was different. We immediately received a project briefing from a large company. Our offer was convincing and we carried out our first joint project.

How to Convince

Persuasion is a tricky business, because it involves “imposing” an opinion on someone else. To exaggerate, it essentially consists of the mindset of the knower who wants to convert the uninformed. However, nobody wants to be converted against their will. People can smell it, even if the tactics are sophisticated. 

That’s why, in my view, your own attitude is crucial. As a reminder, I like to ask myself the question “Can I bear to let the other person have their opinion, even if I don’t think it’s a good one?”. Respect for others means allowing them freedom of choice. And even more. It is regularly useful to remind ourselves to be humble. Because the more convinced we are ourselves, the more blind we can become to completely different points of view. Views that may later turn out to be wiser than our own. 

With humility and respect, we can enter the process with the intention of inspiring. This will be more fruitful in the long term than narrow-minded persuasion.


When I drive through my adopted home of Cologne, I see a Ford B-Max on the road every day. How are you doing? Production of this model was discontinued in 2017 due to a lack of demand. My wife has been driving this car for 10 years and I find it super practical. The lack of a B-pillar and the sliding doors are just great. Because I am a self-confessed (lonely) fan of this car, I notice this model every day.

This is also known as the cocktail party effect. Our subconscious filters the available information and brings to our attention the information that the subconscious considers relevant.

This process is constantly active, even when we are listening to a lecture. According to studies, only around 5% of what is said can be remembered. These are mainly arguments and information that our subconscious flushes out.

What information is this? It is information that is “relevant” to us in some way. In other words: Perception is interest-driven. If I am a CFO who wants to have a profit at the end of the year, then I am interested in everything measurable that contributes to this profit.

If I am a creative brand manager who “knows” that creative performance is difficult to measure, he will be interested in arguments that support his view.

So if I want to inspire someone to think outside the box, I should explain how my thesis serves their interests. The reference to his interests makes the argument relevant and is then examined.

An early adopter is interested in an objectively better solution. A CFO is interested in a solution that demonstrably delivers bottom-line results. A team leader who is likely to be promoted to another team in two years is interested in results that show visible success after one year. 

It’s actually trivial, but it’s work to put yourself in people’s shoes and check where their interests lie. Although it is trivial, it contradicts our view of the world that we should all be objective and all serve the same corporate goal. Putting this into perspective is an important step towards being able to inspire colleagues.

Case studies

I have spent 20 years in the education system, including my doctoral studies. The didactic method of science is deductive. This means that a theory and thesis is first discussed in abstract terms and then concrete examples are derived. The way in which a person learns is exactly the opposite. Learning is an inductive process. It starts with examples. Only when we have heard at least one are we ready to receive the general learning from it.

As far as it made sense to me (and I was attentive enough), I did exactly the same in this book. First an example, then the learning. Even from my own professional life, I am deeply convinced that the presentation of a thesis cannot be understood without prior examples. Because “understanding” means putting abstract things in relation to other concrete knowledge. This is how it becomes conceivable.

I myself am a victim of my educational background and try to improve a little every day. Do the same! Your listeners will thank you for it.


The refinement of a case study is a story. Mankind has always been concerned with how events can be presented in such a way that the audience listens to a story with a high level of attention. The structure of an optimal story has remained unchanged since the ancient Greeks.

The art of storytelling was already cultivated by the ancient Greeks and continues to be practiced professionally in the film industry. In recent years, management authors have taken up this art and transferred it to the world of management.

There are great books on this and I won’t be able to summarize them perfectly here. However, the essence is that case studies need a few central stylistic devices and a dramaturgy.

As a stylistic device, we need a hero who is as “likable” as possible, a person with whom the audience identifies. We also need a villain who has to be defeated.

The dramaturgy classically starts in a state of equilibrium, which is then immediately followed by a catastrophic event. This is the business challenge. The resulting suffering is then attempted to be overcome with the help of a solution. But the first attempt fails. The tension rises. This attempt to find a solution and failure can be repeated several times in a dramatization. Finally, there is a finale in which the hero confronts the villain (who defends the challenge) with a new solution and ultimately wins (eliminates the challenge).

Anyone who can pack case studies into these kinds of stories captivates the audience, entertains them and wins their hearts.

Evidence and safety

In my early career, I was Marketing and Sales Manager of the world market leader for industrial packaging. As such, I was also responsible for innovations. We developed new industrial drums and transportation solutions. It was not easy to find early adapters on the customer side. So I focused on clearly communicating and proving the added value of the solutions. 

In the process, I only realized over the years that something else was much more important in the industry than offering better products and services. Customers were usually filling packaging with dangerous or expensive products. Any risk of leakage or accident was a nightmare scenario for everyone involved. Packaging was also only a minor cost factor. If the production process was disrupted by packaging or logistics, the costs were significantly higher. Safety and reliability were the be-all and end-all. This was the “language” of the industry. Continuity, brand, reliability and quality were the attributes of the winners. 

The limbic brain center plays an important role in what we call the subconscious. It turns out that people react with different affinities to certain limbic motivators. Brain research has crystallized three dimensions that drive all people: stimulation, dominance and balance. 

Early adopters are particularly attracted to the new, to innovation. This releases dopamine. The topic is exciting and thrilling. Stimulation is the preferred motivator. This is why early adopters are the first port of call for innovations such as Causal AI.

Strong leaders are attracted to the best and to performance. It releases testosterone and feels like success and prestige. Dominance is the preferred motivator. Strong leaders are usually responsible leaders such as CEOs or CFOs.

People who are oriented towards preservation and care are attracted to security, harmony and balance. Oxytocin is released and feels like home, security and love. Balance is the preferred motivator.

Everyone has their tendencies and these also lead to a career choice. To successfully promote an innovation in a company, you should address all three aspects. The aspect of the new is inevitably linked to an innovation. 

To inspire the classic manager, more must be added: evidence, i.e. proof of performance. What are the factual advantages of the solution? How do you translate these into performance indicators that are important for the decision-maker? These are the questions that need to be answered in order to inspire performance and results-oriented decision-makers. 

We need results from comparative studies that clearly show that Causal AI is superior. Depending on the use case – discovering vs. deciding vs. generating – the comparative studies must be set up differently. 

Both the benefits of Causal AI “Model Drift” and “Discrimination” address the issue of avoiding risk or maintaining safety. Depending on the contact person, they are therefore only motivating for a specific target group. 


If you have read this book carefully, you will have noticed many metaphors. Whenever possible, I have tried to structure the chapters according to the pattern “Story > Learning > Metaphor”.

The inspiration for this came from Oliver Raskin. He currently heads the Insights department at MIRO. Oliver told me about his “superpower” at a dinner together when we were discussing how insights can take root in the company. 

“Whenever possible, I summarize everything in a metaphor,” he said and told me how this often works wonders.

We are very often dealing with very abstract things. “Causal AI” – what could be more abstract? It makes it understandable to use a parable to give the whole thing an image. Because what does understanding mean? Understanding is the process of placing something new in relation to known units of knowledge. The more concretely these units of knowledge are anchored, the more comprehensible the statement is. Images are very concrete units of knowledge.

It’s like trying to build a house with stones. If the stones are fluffy, made of cotton candy or soft lumps of slime, the house will not hold. Solid stones made of concrete are like pictures, they make a solid house, a comprehensible statement.

A picture is worth a thousand words. 

Role model

In 2022, we had started to market our pricing method “Implicit Price Intelligence” as a DIY tool. Then I had an idea. We invited our target group to a webinar where each participant had to take part in a 5-minute survey as a prerequisite for access. In this survey, our tool was used and the target group’s willingness to pay for the tool itself was measured. I then presented the procedure and the results in the webinar – along with the agreed price change. The price rose by 20% for frequent buyers, but fell by 30% for beginners. 

“Drink your own wine” is a popular saying and makes intuitive sense. 

But is it really necessary?

Convincing others is a difficult game. You can deceive and manipulate. Or, for nobler motives, you can tune in to your counterpart and provide him with the information in the way he best absorbs it.

But what matters is the intention. Whenever your interests are not 100% aligned with those of your counterpart, persuasion can become an attempt at manipulation. However, people usually smell manipulation attempts “three miles upwind”.

If we can let go of the need to convince, if we allow the other person to form their own opinion, we unconsciously send a strong message.

This message is amplified if you set a good example yourself and have the opportunity to pilot the proposal on a small scale and take risks yourself.

Actions are more powerful than words. 

Then, when you no longer try to convince, but your desire is the inspiration, you can trust that you will achieve more. By setting a good example, you make your attitude visible.

Success Drivers GmbH
Johann-Heinrich-Platz 4
50935 Cologne

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