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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)

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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)

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Each month I share a well-researched standpoint around CX, Insights and Analytics in my newsletter.

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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]

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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)

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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)

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My TOP 3 CX Learnings from 2021

My TOP 3 CX Learnings from 2021

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: 09.01.2022 * 5 min read

I run the world’s #1 CX Analytics course, but I still learn new stuff about CX every year. Our amazing customers are those who challenge us with real-world problems that need an answer. This curiosity, the persistence, and the faith our customers put in us let us find a solution and keep us me learning.
Here I am sharing my favorite three eureka moments of the year with you.

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LEARNING #1: Reporting raw feedback to frontline results in wrong learnings

Every second customer feedback of SONOS mentioned the great sound as the reason for their loyalty. The feedback was transparently distributed throughout the organization. 

When reading, everyone in the organization was learning the obvious. Focusing on sound quality was key, where the investments needed to go.

This was a fact. Wasn’t it?

Unfortunately, facts do not equal truth.

Sure, most customers mention the great sound. But this is what pops up in your mind when you, as a customer, get asked an NPS question. In any domain, customers are biased to mention the mutual property of the domain product. 

For restaurant customers, mention “great taste.” Do you think Mcdonald’s is the market leader because of its taste?

For washing machines, customers mention “washes well” while actually, nearly all devices wash well.

Customers praise “great service” for service businesses, while the worse airlines like Delta or Ryanair have the largest growth.

It takes a driver analysis – best done with so-called “Causal machine learning” to understand which customer topic truly matters.

Many enterprises have already run some driver analysis. But providing customer feedback “as is” to the frontline will make this analysis redundant.

Because the front line will read customer feedback and extract its own -wrong- lessons.

If it is wrong to look at the frequency of mentionings for the insights department, it is wrong to look at individual cases simply because all that happens with the reader is that he is implicitly counting topics.

In this article, I discuss the problem and suggest a solution FIXING-INNER-LOOP

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LEARNING #2: The No.1 pain point of CX Insights Professionals is to get leadership buy-in

When consuming content at industry content platforms or conferences, you could get the impression that the key is to have the best tools to become a successful insights professional.

While this might be true, it is not the topic representing the most significant pain of client-side researchers.

In May 2021, we did a large industry study interviewing CX insights professionals. We asked many questions and started and ended with open-ended questions on which issue moved them most.

The result was overwhelming. It’s the challenge to get leadership buy-in, move the organization, and get other departments to act on insights.

This means Insights does not have a tool problem. It has a self-marketing problem.

Part of it is to tailor its service to become more relevant to the internal audience. Do stakeholders want and need an insights report? Or do they instead love to get an evidence-based recommended next action?

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LEARNING #3: Great insights are mostly not converted into great decisions.

SONOS found in its CX survey that the new Voice Assistant was still not very widely used. But driver analysis found that those who used it had become huge SONOS fans.

The insight was clear. Increase awareness and applications awareness of the voice assistant feature.

But the decision behind it is a multimillion advertisement dollar investment. The insight is worthless if you can not predict whether the investment will pay back.

Instead, those decisions are left to genius and expert judgments.

SONOS used the causal machine learning model to predict the impact on NPS and the impact of NPS on churn and sales. The brand could understand that the investment should instead be made on another topic. 

As insights professionals, we are by nature too much self-focused. 

We believe great insights have value on their own, and the right decision is just a consequence.

But if we do not apply the same rigor in which the insight was born to the whole decision-making process, all insights are wasted

(btw this is the reason why we at CX.AI implemented the ADIM functionality – read here more.

# # #

What are your top 3 learnings of the year? 

Love to hear from you! Reach out over email or LinkedIn.

Cheers, 

Frank (LinkedIn | Email)

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The 5 Deadliest Mistakes in Business Decision Making

The 5 Deadliest Mistakes in Business Decision Making

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: December 15, 2021 * 7 min read

We all know that decision-makers fight with many cognitive biases. For some reason, we believe just “the other guy” has a problem with this. What we believe feels so true. It turns out that if you know what goes wrong in your thinking, then you can circumvent its downsides.

The 5 deadliest mistakes are:

#1 Use your gut and common sense when dealing with small or large likelihoods

The first mistake is the tendency of humans to underestimate small percentages and to overestimate the impact of large percentages

This is based on the work of “Daniel Kahnemann” (who is a winner of the Nobel Price in Economics for it) His work together with Tversky found the incapacity of humans to handle small and large percentages. It proves that we are loss averse (pay more for an unlikely loss (insurance)) and risk-taking (pay for an unlikely high gain (lottery effect)) at the same time.

Let´s take this: What happens if you are in price negotiations?

While negotiating, there is always a risk of not getting a deal. The typical decision of negotiators now is to lower the price to raise the likelihood of winning the deal. The phenomenon behind this is the (irrational) loss-aversion of the negotiator. 

Actually, our brain is quite bad with numbers. Do you know how much more dangerous is to drive a car than flying an airplane?

100,000 TIMES! 

Why are 42% of humans then afraid of flying but not to drive a car?

Be aware of the loss aversion and risk-seeking tendency of human. Instead, don’t give the decision to the gut. It is biased. Develop a decision calculus.

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#2 Believe that true risk can be measured in past data

The second mistake is a misconception of risk. Risk management is seen as a procedure for taking past data and calculating likelihoods from it. 

When done without software, we over or underestimate the likelihoods.

But the true mistakes happen in the belief that past data CAN measure future risks.

50% of the stock market changes of the past 50 years happened in 10 days. The financial crises in 2007 was so obvious – just after it happened. 

Look at the famous “Kodak” or “Nokia” cases. Things are happening where you can´t think of.  

This is the risk. A true risk is something unknown – not expected. 

It is easy to protect against threats that happened in the past. Because of this: Its not a risk anymore.

Unexpected shocks instead can be managed to become anti-fragile and robust against unknown challenges. Any living creature is made that way. If you loose an eye, an ear, a finger or a lung-wing, you still can survive. 

If you need to run 10 miles every day, you get stronger or more robust. 

This is what businesses need to do to prevent risk—becoming that robust and strong to withstand any weather.

#3 Considering selected facts and case studies as proof

The third mistake is all about case studies. How can you convince decision-makers? Sure, use case studies.

To prove a theory, it makes sense to provide evidence, give facts, and show case studies.

The problem: anyone can cherry-pick those facts and case-studies that fit the theory. If you believe in facts, you will likely become a victim of snake-oil storytellers.

You see it in the actual vaccination debate – both sides -pro and contra vaxers are showing examples – people died because of the virus or the vaccine. If you see victims laying in a hospital or doctors fighting for life’s, you often don’t need a second “case study”. 

It is dangerous to use singular cases to prove a hypothesis.

Instead, it takes a validation on a larger sample that is representatively sampled.

Machine learning to find out.

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#4 Being unaware that your world view is biased.

The fourth mistake is called the “Truman Show Synonym”. It can be described as the tendency of human to overestimate their own opinion. Science refers is as the confirmation bias.

People are trying to search for examples and specific data. In this search our unconscious brain brings information to our attention that are “relevant” to us (Cocktail party syndrome). 

Relevant is everything that is in our favor or supports our own theories. It validates your existing believe and makes it even stronger the more you inform yourself – without being aware of the effect.

As a result, humans are basically a “Truman” in its own show. The real world is different. 

Humbleness about your own opinion can be useful, because at the end you can manage your future more successfully if you know the truth – not just you feel great about your opinion.

#5 Infer Causality from Correlation

The fifth mistake is the famous correlation. Humans are growing up by using the methodologies of correlation. In many cases, this works perfectly well. 

If you have a nail and hit it with a hammer – there is one cause you can even control by yourself. As a result, you see the impact right away. Correlation proves causality.

In this case, where you have a limited number of causes and you see the results shortly after the cause, the correlation is a perfect methodology for finding out what works and what not – Try and Error!

Unfortunately, the business world is different. There are plenty of important causes and context variables. Even worse,  business decisions can take a long time until they show impacts. 

Learning about cause and effect in these circumstances takes data about drivers, context, mediators, and outcomes. And it takes a causal modeling analysis

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Your pathway to better decision making

No matter what you do in marketing and sales, if your assumptions and insights are biased all your work, strategies, tactics, and implementation work can be wasted.

Wise business leaders know the cognitive biases and this is what they do:

#1 – Trust a decision calculus, not common sense when treating low (and high) likelihoods 

#2 – Know that the true risk are threads you are not aware of (Black Swan effect)

#3 – Avoid case studies and selective facts and seek for analyzing representatively sampled sets of facts 

#4 – Review your information seeking process and actively seek challenging theories

#5 – Avoid concluding from correlations and instead aim to perform causal modeling – most practically use Causal Machine Learning 

There is an emerging technology readily available and already intensively tested. It provides a solution to those challenges: Causal Machine Learning and Causal AI.

It requires a causal mindset to make use of it. It requires you to understand that everything decision-makers are looking for is causal insights—the invisible link between actions and outcomes.

What are your thoughts on this? 

Do you want to engage in an exchange? Reach out, and let’s meet on a virtual coffee chat: book your spot here.

 

Cheers, 

Frank (connect here)

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How CMOs Should Lead Data Science

How CMOs Should Lead Data Science

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 30, 2021 * 9 min read

Data Science is seen as the new magician in enterprises. Business leaders think they just need to shout into the basement where “hordes” of data scientists are sitting, and soon after, these spit out the magic formula by reading the crystal ball of AI. Silo thinking like this is neither useful nor needed. Having learned the proper framework, a business leader can win any discussion with data scientists using their holistic intelligence that data science does not have.
The Problem With Data Science

There are unlimited ways to deal with data. There are even endless ways to set up a neural network it’s easy to get hung up on complexity. Actually, most data scientists hang up themselves in “local minima” – this is data science slang for a “sub-optimal solution”.

Lost in complexity, it’s easy to lose business outcomes out of sight.

Only if business leaders know what they really need, they can manage data science wisely.

These are the three challenges on which both need to become clear about

  1. Aligning on the difference between data and insights. Facts and data are used as a synonym for truth.

  2. Understanding how to gain insights that work. The universe of modeling techniques is infinite. It needs a clear Northstar to get insights that drive business outcomes.

  3. Setting requirements for data science methods: Once you know how to build an analysis approach that drives results, it should be clear which criteria the actual modeling technique needs to comply to.

Here we go.

Introducing the Starway of Truth concept to understand the difference between data, facts, and truth

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Stairway of Truth

The stairway of truth starts where most people believe ends: Facts.

     1. Facts

If you see that a plane has crashed in the news, you learn one thing: it’s dangerous to fly. 42% of people are anxious about flying, while 2% have a clinical disorder.

This is a fact but not the truth. Flying is by a factor 100.000 safer than driving a car.

When US bombers came back in world war two, the army analyzed where the bombers got hit and applied ammunition.

They acted on facts, but the initiative was useless because the analysis did not uncover the truth.

It’s impossible to understand why bombers do not come back without analyzing those who don’t come back.

In the same way, it’s impossible to understand why customers churn if you only analyze churners. It could be that churners and customers complain about the same thing.

     2. Sample

What it takes instead is always a representative sample selection of facts. Facts are just particular snapshots from the truth, like a pixel out of a picture. It might be true. But it, alone, is meaningless.

In 1936, one of the most extensive poll surveys made it to the news. 2.4 million US Americans had been surveyed over the telephone. The prediction was overwhelming. Roosevelt will be the apparent loser with only 40% of the votes.

Finally, Roosevelt won with nearly 60%. How could polling fail so miserably?

The sample was not representative. At that time, telephone owners had more fiscal means. This correlated with the likelihood to vote for democrats.

Just a sample of pixels can paint a picture. But if pixels are drawn just from one side of the picture, you are likely to read a different “truth”.

      3. Correlation

The journey to truth does not end at a well-sampled “picture”. Why? Ask yourself, what do business leaders really want to learn?

What’s more interesting?: “What is your precise market share?” or “how can you increase market share?”

The first question asks for an aggregated picture from facts.

The second asks for an invisible insight that must be inferred from facts. It is the question of what causes outcomes.

“Age correlates with buying lottery tickets” – From this correlation, many lottery businesses still conclude today that older people are more receptive to playing the lottery.

The intuitive method of learning on causes is the correlation. It is what humans do day in day out. It works well in environments where effects are following shortly after the cause and when at the same time, there is just one cause that is changing.

It often works well in engineering, craftsmanship, and administrations.

It works miserably for anything complex. Marketing is complex, Sales is complex, HR is complex.

“Complex” means that many things influence results. Even worse, the effects are heavily time lags.

Back to the lottery. The truth is that younger people are more likely to start buying lottery tickets. Why then are older more often playing? Purchasing a lottery ticket is a habit. Habits form over time (=age). This is amplified with the experience of winning, which again is a function of time.

     4. Modeling

To fight spurious correlation, science developed multivariate modeling. The simplest form is a multivariate regression.

The idea: if many things influence at the same time, you need to look at all possible drivers at the same time to single out the individual contribution.

The limitations of conventional multivariate statistical methods are that it relies on rigid assumptions, such as

  • No Interactions: All drivers are independent of each other

  • Linearities: The more, the better

  • All drivers have the same distribution

Sure, many advancements had been developed, but always you needed to know the specificity upfront. You need to know what kind of nonlinearity are what is interacting with what and how.

No surprise that this turned out to be highly impractical. Businesses get challenges and need to solve them within weeks, not years.

     5. Causal Modeling

It turns out that the majority of business questions concern the causes of success.

When you want to drive business impact, you need to search for causal truth. Science, Academia, Statistics, and Data Science shy away from “causality” like a cat from freshwater.

Because you can not finally prove causality, they feel safer neglecting it. They can ignore it as they are not measured with business impact.

All conventional modeling shares a further fundamental flaw: the belief in the input-output logic. This only measures direct, not indirect, causal impact (best case).

Causal modeling uses a network of effects, not just input versus output. Further, it provides methods to test the causal direction.

     6. Causal AI

Causal AI is now combining Causal Modeling with Machine Learning. This has huge consequence on the power of the insights. It eliminates all those limitations that modeling always had.

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Causal Insights Triangle

Equiped with machine learning, causal modeling becomes much more manageable and thus more practical.

The causal insights triangle gives you the framework for how you build your model.

Let’s go thru each component and illustrate it with marketing mix modeling (MMM).

First, define what measures the desired outcomes. In MMM this would be the sales figures per day or week.

Then collect on a blank sheet what drives and influences those outcomes. This list can be bucket into three parts.

First, there are the drivers. In MMM this would be the spendings per marketing channel in a particular day or week. Drivers are variables that are independent of other variables in the set.

Second, are the mediators. In MMM, this would be the brand awareness or share of consideration set of a given week or month.

Mediators are variables that drive outcomes but also are influenced by drivers.

Third, are context variables. In MMM this would be the power of the creative to drive impact, the type of creative, the region at hand, the demographics profile of the region at hand, etc.

Context variables are moderating context factors that you may not be able to influence but impact how the model works.

The good thing is that you don’t need to know how those variables influence others. You can even use any type of data, as long as it has numbers.

With the selection of data and the categorization into the 4 buckets, you have infused your prior knowledge about causal directions into the model.

The rest is up for causal machine learning to find out.

Causal AI Weel

The concept of the Causal AI Weel illustrates why it’s not enough to use conventional causal modeling techniques.

Three quick examples illustrate the need:

Unknown Nonlinearity: A pharma company found it drives sales to give product samples to physicians. But with causal machine learning, we found that providing too many samples will REDUCE sales. After the fact: of course too many samples substitute prescriptions.

Unknown Interactions: In CPG, purchase intention for new products correlates zero with future success. But with causal machine learning, we found that it takes five other success factors to be true at the SAME time.

Unknown Confounders: Many companies see that the NPS rating correlates zero with future fiscal impact or churn. At an insurance brand, this was because more critical customers segments (have perse lower ratings) will buy even more, ones they are loyal. This unlying effect can be considered when integrating segments or demographic information into the modeling.

Here is in a nutshell how machine learning separates from conventional modeling:

Hold a book into the room. The floors’ two-dimension symbolize your two drivers, and the height of each point of the book stands for the outcome. The steepness of the two angles of the book represents the parameters of your modeling. The process tries to fits the book’s plane into space. This attempts to approximate the data points that are like stars in the room’s 3-dimensional space.

Machine learning does the same, but it uses a flexible book or hyperplane. It’s like a tissue or a book made of kneading. It can be formed in a way to match the data points better.

The fewer data points you have, the more rigid and less flexible it gets to avoid overfitting.

This flexibility solves a lot of problems.

Legacy techniques instead are restricted by

  • Linearity assumptions “the more, the better”

  • Independence assumption “any TV ad always has the same impact”

  • Testing theory “we assume all assumptions are true and just fit parameters and hope for good fit metrics.” instead of finding new hypothesis in data.

With machine learning now, we can explore previously unknown nonlinearities and previously unknown interactions.

We can even now combine ANY quantitative data into one model. This capability eliminates confounder risks and will improve the likelihood that findings are indeed causal.

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This is How CMOs Win Any Discussion with Data Science

As a marketing leader, you don’t need to know the methodological solution. But you need to ask the right questions and be careful not to get wrapped into plausible-sounding storytelling.

Here are the questions you can ask:

Someone presents you:

 

You can ask

 

Facts as proof for something

How do we know these facts are representative of the truth and not just exceptions?

Descriptive statistics about a representative sample of facts

How do we know that our conclusion will really influence desired outcomes?

Comparing winner vs. losers or other kinds of correlation analysis

How do you know this is not a spurious correlation?

Driver analysis outcome

How can we make the model more realistic esp. by considering indirect effects – such as brand, changing attitudes and other long-term effects?

Driver or SEM model

How can we avoid confounder risk? (if external context influences drivers AND outcomes, it will screw results)

Driver, SEM, Bayesian nets results

How can we make sure results are not screwed by things we do not know of, such as nonlinearities or interactions?

Responds: “that’s not possible.”

I read this article from Frank from Success Drivers. He wrote it is possible. Shall we ask him?

As a marketing leader, you have the responsibility. Data scientists are just consultants. When there is no impact, they do not care much. You do.

Like naïve patience that runs at risk of getting unnecessary treatments. It’s not the doctor’s life that is at stake.

In a complex world, it’s not enough to check results based on plausibility. It’s easy to build a plausible story from random data. Plausibility is simply not a good indicator of truth.

Instead, challenge data science and challenge marketing science to “think causal”. Challenge them to use Machine Learning to help you learn from data instead of just testing made-up hypotheses.

Here is a good read for you if the topic interests you further. We send you the hard copy book free of charge if you are an enterprise client.

Stay curious and …

… reach out to me with questions

Frank 

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Compete on Insights

Compete on 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: November 15, 2021 * 12 min read

The success of strategic initiatives relies on three things: the analysis, a solution based on those insights, and implementation of the strategy. It can fail at any point in the cascade. But the insights are the Achilles’ heel.

There is a twofold irony: First, all resulting investments are wasted if the insights are wrong. Second, you can see if an implementation fails and if a strategy lacks consistency and rigor. But you can NOT “see” whether an insight is valid. Any insight can be turned into a plausible story.

Most CEOs and CMOs are not aware of this. It is my observation that this irony is the main reason for stagnation and the bottleneck for growth.

Let me give you some examples of typical fails I see enterprises do.

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FAIL #1 – Believing What Customers Say

👉 EXAMPLE: CUSTOMER EXPERIENCE INSIGHTS

Customer Experience research has a long tradition, and the latest trend is the simplification using the NPS framework. One rating and an open-ended question will measure the level of loyalty and reasons why.

Really? 

It turns out that the most often mentioned topics in open-ended, unstructured feedback in most cases are not the most important ones.

Actually, frequency does not correlate with importance AT ALL.

But this violates the fundamental assumptions of 91% of all CX measurement programs.

The consequence is that companies prioritize topics that they should not.

Even worse, such feedback is forwarded to the customer-facing employees. By reading this, those employees learn that the most frequent topics are the most important ones.

Wrong knowledge percolates throughout the company.

How to fix this? Below is more.

FAIL #2 – Focusing on Outcomes

👉 EXAMPLE: CREATIVE INSIGHTS

Advertizing research has two parts. Part one is how to spend ad budgets. Part two is how to optimize the creative.

Independent meta-studies from ARF have shown that at least 70% of ad impact can be attributed to creative quality.

Nearly all efforts of advertizers to assess creatives are some kind of measuring technique. 

There is the classic copy test that asks for the responses and purchases intent after exposures. And there are highly elaborated Neuroscientific and Biofeedback procedures. All of it can make a lot of sense and can be useful.

But what the industry is not appreciating is that FACT does not equal TRUTH. 

Measurement just produces facts. What brands, however, need to know is WHAT TO DO to achieve a particular outcome. This if-then link is a question about causality between actions and results. It can NOT be measured. 

It can only be inferred. The science behind this is called “causal analysis”.

The hunt for success strategies is handed to storytellers and creative “geniuses” instead of proper analysis. 

How to fix this? Below is more.

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FAIL #3 – Over-Simplification:

👉 EXAMPLE: NEW PRODUCT INSIGHTS

95% of CPG (FMCG) product launches do not survive the first year. This is despite brands invest billions in customer research. How can this be?

Yes, it isn’t easy. A product needs to appeal to customers before and after buying it. It needs distribution, shopper marketing, a strong brand to be recognized, and a fair price to get a yes.

We looked at all CPG grocery product launches of a year with data for all those components. Each component hardly correlates with success. The correlation of purchase intent with survival is nearly ZERO. Same with brand, product use scoring and even pricing.

Proper causal machine learning revealed that the long-tailed distribution of launch success could only be explained by one phenomenon: All success components do not add up to success; they multiply, they depend on each other.

One failure in one discipline, and you are out.

With the available data today, future blockbuster products can be predicted with 80 to 90 percent hit rate. 

Not only that. Causal machine learning can give hints on how to architect those blockbusters.

How exactly? Below is more.

FAIL #4 – Gut Over Rigor:

👉FAILS IN PRICING

Pricing today either uses gut or rigor. But it takes both to make a difference.

Most common is an explicit question to the customer (namely Van Westendorp Scale or Gabor Granger). It delivers a plausible indicator for an optimal price range. But plausibility is not a good validation of truth. 

Not only does it provide a price-demand curve, nor does it consider margins. As such, it is useless. It speaks to the gut of the decision-maker but fails to deliver rigor and validity.

The opposite is true for Conjoint Measurement. Based on multiple complex choice tasks, an algorithm can derive the utility of each product feature. Based on a market simulation, it then produces a price demand curve.

The approach falls flat because of very different reasons. Mostly the modeling assumptions turn out to be unrealistic. Often consumers select the “wait & see” option that conjoint models typically miss out – and with this – by far overestimate market demand.

The other downside, conjoint is so complex and costly that it is just applied for handpicked products.

Each sizable car manufacturer makes billions of revenue with parts that have never been empirically priced. The same is true for most consumer brands with many SKUs.

How to fix this? Below is more on a solution called Price.AI.

that.

FAIL #5 – Linear Mindset Instead Of Causal Thinking

👉 EXAMPLE: SALES, MARKETING, MEDIA MIX MODELING

Large brands spend huge marketing budgets and so want to know where to invest.

This question focuses mostly on the short-term impact of advertising, but the bulk of the impact is due to brand building and can only be found in the long run.

Ads of strong brands show larger short-term impacts, no matter where you advertise and how you advertise. Ads of weak brands can show no short or medium-term impact but the huge long-term impact by building brand equity. 

In truth, long and short-term effects must be modeled in one go to avoid misattribution. 

On top of this, the world is even more complicated. Complexity is not managed by just hiring the market-leading MMM vendor. It is by involving those vendors with the best technology.

Example? A drug brand had product sampling as one promotion channel. Any legacy modeling (even those who capture nonlinearity) had found “the more, the better”. Only the proper method was flexible enough to see that -of cause- too many samples will substitute product prescriptions.

Channels also amplify each other. Some only compliment each other. Mathematicians call this “Interactions”. As those interactions are unknown and invisible, it takes flexible learning machines to unearth them.

How to fix this? Below is more.

 

👉 EXAMPLE: Price promotion effect

It is a long-known trap. Still, most companies fall for it.

Price promotions clearly show sales uplifts. That’s a fact.

This sales uplift ALWAYS is a composition of:

  1. Customers who bought because of this promotion
  2. Customers who would have purchased it anyway but later -at a higher price
  3. Customers who would have bought it earlier but waited to know the promotion would come (Black Friday effect)

If you do not quantify all three, you do NOT understand whether or not the price promotion made sense.

How to fix this? Below is more.

 

👉 EXAMPLE: BRAND POSITIONING INSIGHTS

My last example about the negative impact of linear mindset as opposed to causal thinking is T-Mobile USA.

2013 the brand had a huge relaunch in the USA, attacking its huge competitors AT&R and Verizon. It worked, but T-Mobile did not know why.

Each feature they introduced could be even easily copied.

A revolutionary methodology found something hidden. All features were not the direct causal reason, but the perfect reasoning of T-Mobiles Robin Hood story. This story (being the Uncarrier) was attracting people.

Fast forward other features had been implemented over time to nurture this winning positioning. 

The impact has become world-famous. Today T-Mobile is on par with AT&T and Verizon with exceptional profitability and +600% market evaluation while AT&T declined.

How to extract your winning market factors? Below is more.

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On the Latest Indepth Thought-Leadership Articles From Frank Buckler

The Solution: A Causal Insight Mindset

The solution to all those examples is not simply “better tech”. It takes a problem awareness to see what is “better”.

With this, the ultimate challenge in enterprises is cultivating an ongoing discussion on causality and how to read the truth from data.

Everyone believes he can read the truth by looking at data. Our gut fools us most of the time. We do nothing about it.

A company that cultivates a mindset of humbleness and awareness about the art it takes to read the truth from data will be able to single out the best tech.

It takes leadership and education to make such a culture happen.

The education piece is obvious. Every manager needs to learn the 101 of gaining the truth from data. Such a training piece builds on simple insights:

Every business decision builds on this CAUSAL assumption: Action X will lead to Outcome Y. 

As such, we MUST apply “causal analysis”. This is either controlled experiments or causal modeling.

Period.

Causal Modeling In Action

Let’s review approaches to learn about causal impacts

  • Comparing facts (e.g. Male earn 20% more than female) – Is gender truly driving the income difference? Maybe, maybe not. This is a binary correlation analysis and has the same drawbacks as standard correlation analysis: Spurious correlations.

  • Correlation – neglects all other factors of being a reason for the outcome 

  • Regression – now considers other factors but fails to model indirect, nonlinear, and interaction effects

  • SEM/PLS – now this also considers indirect effects but fails to model indirect, nonlinear, and interaction effects. On top of this, it fails to provide exploration features, something elementary for business applications.

  • Bayesian Nets – now explores causal directions too, but fails to model nonlinear and interaction effects.

  • USM (Causal Machine Learning) – now is the most complete framework for business applications (available as the software NEUSREL)

Here is how USM and Causal Machine Learning can help your business to compete on insights.

Causal Machine Learning IN ACTION

👉EXAMPLE: CUSTOMER EXPERIENCE INSIGHTS

Every company has it – customer feedback like an NPS rating or stars on Amazon. Then most ask an open-ended question why. That’s all that you need.

First, make sure to categorize feedback into the topics mentioned as granular as possible. NLP deep learning systems can help to scale this.

Causal Machine Learning can unfold its magic. The categorized feedback comes as binary variables. Text AI also produces sentiment information that measures the totality of language. Also, context information can serve as additional predictors.

Causal Machine Learning can take care of so-called intermediary variables too. Besides the sentiment, a category like “great service” is such an intermediary variable as it is driven by more specific ones like “friendliness”. 

The model then can find out that friendliness is the key behind “great service”. A conventional driver analysis would have totally missed the importance of friendliness because categories are not independent. 

On average, Causal Machine Learning doubles the explanatory power of conventional driver analysis. This means it reduces the risks of wrong decisions by 50%.

The cx-ai.com is a solution that leverages Causal Machine Learning, provides CX and fiscal impact predictions as well as an ROI decision-making framework

 

👉 EXAMPLE: CREATIVE INSIGHTS

To understand how a commercial will succeed, it is not enough to measure how well it performs (this is the focus of copytesting today). Instead, you also need to measure what it does.

In a large syndicated study, we annotated (categorized) over 600 spots of 6 product categorize to describe what the TV spots actually are doing. 

Do they use a spokesperson? Do they use a problem-solution framework? Does it use a song that corresponds to the acting message? We coded the technical properties of a spot.

Then we coded the emotional message each spot was making. Each spot can be categorized into one of the dozens of topics like “it tastes good”, “it can be trusted”, “good for the family”, etc.

This data is then merged with copy testing data. With this data, Causa Machine Learning can now understand which tactics and which emotional messages work in your category.

We called the approach Causal.AI. It can not invent an actual creative conception. But it gives clear guiding rails about which strategies will work and which don’t.

 

👉 EXAMPLE: NEW PRODUCT INSIGHTS

When launching a new product, much can go wrong. Distribution, brand, packaging, promotion, first product experience, pricing – all this needs to be good enough. It’s a success chain at which the weakest link determines winning or losing.

Each step on its own as well as all together is an application for causal machine learning.

Before this, typically, you want to test a product concept and learn WHY it is not crushing the crowd. 

Test the concept with implicit response measure and then get feedback on the classical eight dimensions of product adoption. It will tell you what consumers think about the product but not (yet) why they don’t buy it.

It takes a causal machine learning model to measure how important those dimensions are. 

We ran the process for a new speaker concept. We learned the most crucial thing for marketers to look at was communicating why it was different from (uniqueness dimension) than the competition. 

Each product has its topics. It could be ease of use, appeal, utility, certainty, trust, or compatibility with the consumers’ lives.

Applying USM (causal machine learning) is essential to translate data into predictive insights that work.

 

👉EXAMPLE PRICING

Price.AI is a methodology that lends methods from psychology to measure unconscious attitudes in lightspeed. 

It tricks conscious minds by measuring reaction time on whether or not the shown price is fair or risky or attractive or with “want to buy” and so forth. AI then is trained to predict the willingness to buy. 

This AI helps to consider the attribute’s nonlinear link to purchase and lowers the required sample size.

In the end, the method delivers an accurate price demand function. It can be retrieved in an automated process with as low as 50 respondents. As such, pricing becomes not only precise but also scalable.

 

👉 EXAMPLE: SALES, MARKETING, MEDIA MIX MODELING

A MMM model based on causal machine learning solves all problems mentioned above. 

It automatically models channel interactions and nonlinear effects, especially those nobody is aware of.

Most importantly, it considers the indirect effect. The brand-building effect is an indirect causal effect. Any MMM model should include indicators of brand strength.

It also considers the biggest context and confounding factor: the creative quality. There is no ad impact if the ad is bad, no matter how much money you pour into the channel.

You don’t have data for that? If you do copy testing you do. Nowadays, you can even buy such information or teach a deep-learning AI that can predict it.

This are 5 questions you should challenge your MMM vendor with:

https://www.success-drivers.com/what-can-marketing-mix-modelling-provide/

 

👉 EXAMPLE: Price promotion effect

Understanding the impact of price promotion is a natural outcome of a holistic sales model. 

Causal machine learning enables holistic models with ease by adding predictive power at the same time.

Conceptually, sales must be modeled as an outcome of the price at the time (=price effect), the price of the past (=early purchase effect), the price of the future (=promotion anticipation effect), and all other circumstances. 

If the price of the future or the past predicts sales of today, we have the prove that purchases just shifted due to pricing)

 

👉 EXAMPLE: BRAND POSITIONING INSIGHTS

Brand positioning is a vast field. Depending on the approach, you may actually measure different things. 

No matter what you are measuring, these data can be grouped into final outcomes (e.g., purchase intention), intermediate outcomes (e.g., consideration, awareness, liking, etc.), drivers (image items, features, feature perceptions, etc.), and context (demographics, product usage, psychography, etc.).

The causal directions between variables are known for 95% of the paths based on marketing science. Causal direction tests can test the rest. This structure guides the model building.

Causal Machine Learning then does the legwork. 

The whole details of the T-Mobile case can be found here.

Winning With Better Insights

No matter what you do in marketing and sales, if your assumptions and insights about the customers are biased ….

….all your work, strategies, tactics, implementation work, and ad spending will be wasted.

This is why there is nothing more important than getting insights right from the start.

The most common misconceptions and misbelieves are these 5 fails:

FAIL #1 – BELIEVING WHAT CUSTOMERS SAY 

FAIL #2 – FOCUSSING ON OUTCOMES 

FAIL #3 – OVER-SIMPLIFICATION 

FAIL #4 – GUT OVER RIGOR 

FAIL #5 – LINEAR MINDSET INSTEAD OF CAUSAL THINKING 

There is an emerging technology readily available and already intensively tested. It provides a solution to those challenges: Causal Machine Learning, Causal AI, or USM.

It requires a causal mindset. It requires you to understand that everything relevant that decision-makers are looking for is causal insights. 

What are your thoughts on this? 

Do you want to engage in an exchange? Reach out, and let’s meet on a virtual coffee chat: book your spot here.

Cheers, 

Frank (connect here)

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Escape from La La Land

Escape from La La Land

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: October 29, 2021 * 9 min read

It’s all around us: Facts. They make us feel safe. Like Truman felt safe in his fake world. Yes, the real world is different than we think because ‘biased sampled facts lead to wrong knowledge’. In this article, I like to illustrate that nearly everything that we learn in business and in private life is severely biased. Everything we hear from customers, employees, and the market. The good news is: We can do something about it.

It belongs to the fundamentals that every insights professional knows. Biased samples can lead to biased results. Nothing new.

Nothing new? When Hiram Bingham discovered Machu Picchu in 1911, it was not new either. The locals knew this spot well, but they underestimated the role and importance of this spot.

Same with ‘filter bias’. It is not only all around us. It has a -sometimes- devastating effect on what we believe about the world.

Take social media. The term ‘filter bubble’ or ‘echo chamber’ are widely known for the impact that a filter can have. Social feed algorithms learn which content leads to engagement. I then only show the content that is engaging for you. 

Engaging content is most likely in line with your opinion, and it is most likely negative and alarming. The result is a polarization of unbalanced views and a fearful worldview.

In this LinkedIn article, I am describing the background in more detail, and I am proposing how feed-algorithms can be optimized to stop filter bubbles.

But for now, I like to shed light on the impact to businesses when underestimating the filter bias.

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Churner feedback

What would happen if you ask non-churner a similar question like “what needs to be improved”. What if they mention the same topics like churners? Would you still believe what churners most often mention as a reason is the true motivator for churn?

Churner feedback provides you a bias feedback by design. It builds on the assumption that churners know and articulate the true reason for churn. 

This assumption is more or less broken.

It can only be validated with an unbiased sample of churners and non-churners.

Customer experience feedback

Most companies do it. Asking an NPS or Satisfaction question and then asking WHY. Then companies assume what customers say is unbiased and can be taking as-is. 

They assume customers are willing and able to articulate why they are loyal reliably.

This assumption is broken too.

Human brains are notoriously lazy. If there is no strong incentive, customers’ brains spit out instant associations instead of well-thought-out replies. 

This is why restaurant customers most often mention “great taste”, insurance customer “great service” and speaker customer “great sound”.

It’s often an instant reaction without deeper rational processing. 

The process and context of interviewing itself provide a bias that can turn results upside down. Here is an article that goes deeper on this and how to unbias results.

Inner Loop of CX feedback

The “feed” of customer feedback is biased like a Facebook feed. 

Most companies are sending this feed to the frontline. It should enable the frontline to learn what customers think.

But it does not do the job. Instead, the frontline will learn something else. It will learn what customers spit out by instinct when asked. But not necessarily what will make them happy or more loyal.

CX feedback needs a causal analysis (or some kind of driver analysis) to judge its importance reliably. This article discusses how to solve the issue.

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Public Ratings

Public ratings on Amazon, Google Maps, Google Play, Trustpilot, Capterra, and much more are a great and free source of feedback for many businesses and local branches.

Yes, sure, it is biased in multiple ways

  1. Extremes: It takes some frustration or excitement to take the time and give unasked feedback. Certainly, the concept has a bias towards the extremes

  2. Fake reviews: Competitors have an interest in bombarding your reputation, and some businesses manage their reviews themselves in their favor.

  3. Duplicates: Providers like Bazaarvoice are collecting feedback from different ecom-sites to repost them automatically (if positive) to other ecom-sites. It leads to huge duplicates and bias in topic frequencies.

This means the sampling of rating feedback is largely biased.

What is not biased by the 3 filters is the relationship between rating and explanation. It enables you to understand still the impact of topics using causal or driver analysis.

Social listening

Social listing uses public conversions to measure what people talk about to indicate what is going on. 

But Social data is even more biased. 

80% of social conversions are of non-human origin. Those conversations that are human, are biased by the feed algorithm. This algorithm determines the reach of posting. Some opinions may be predicted to be less engaging and thus will get fewer eyeballs.

Compared to the Rating feedback, it’s harder to debias social feedback, because the reference is missing. 

One approach is to use existing brand tracker and machine learning algorithms to find the unknown link between social conversations (as an aggregate per time and region) onto brand tracking results. This website gives more details.

Leadership

Are you managing a team? Certainly, you ask for feedback regularly. Did you ever realize how biased this is?

I made this observation myself in my previous life as a Marketing Director. My team reported to me what great things they did and what mistakes the other departments were making. 

The information flow to upper management is like a Facebook feed. It is optimized for engagement. It’s certainly not optimized for you to learn the truth.

A biased feed of facts will inevitably result in wrong opinions.

Managers without a direct line to the front line (or other elaborate ways of truth discovery) will entirely lose grounding. 

I know many people who would support this with anecdotal evidence. I am sure you do too.

The phenomenon is also one reason why companies like McDonald’s have Upper Managements work in the front line regularly.

Besides this, it takes an Employee Experience Feedback program that deploys the same rigor in analytics and understands the true impact of topics out of unstructured feedback.

Keep Yourself Updated

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This Is Your Exit From La La Land

The most important step has already been done: you read this article. Being aware of the phenomenon will give you healthy doubts and awareness for the biases all around us.

Practical ways to tame the bias effects are typically modeling analysis work. It uses biased feedback as predictors and objective outcomes as a benchmark. Now, with machine learning, we can find the link between input and output. We can find this link that is unknown because the bias is unknown.

If you want to dive into more cutting-edge CX thinking, the CX Analytics Masters Course is for you. It’s free for enterprise insights professionals. If you are looking to discuss some of the advanced technics mentioned above with an expert, reach out at www.cx-ai.com 

Now I have a question: Was this article helpful?  Please DM me directly with any comments or questions 

Frank

"CX Standpoint" Newsletter

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Each month I share a well-researched standpoint around CX, Insights and Analytics in my newsletter.

+4000 insights professionals read this bi-weekly for a reason.

I’d love you to join.

“It’s short, sweet, and practical.”

Big Love to All Our Readers Around the World

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, Owner: (Registered business address: Germany), processes personal data only to the extent strictly necessary for the operation of this website. All details in the privacy policy.