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
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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
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?
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.
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