My local shop manager can anticipate my behavior in a way that makes me feel totally ill-disciplined, every single time.
I will get a WhatsApp message from him with pictures of several Ben & Jerry’s large ice cream tubs, accompanied by a cheerful message: “Sir! I just had a delivery, shall I send one?!”
My mind and my responses go through the same pattern each time:
“No thank you, sir.”…”I can’t this time, I should be losing weight.”
“But it has been a really difficult week.”
“So, I do deserve a treat while I’m watching the latest episode.”
“If I buy the whole tub today, I’ll just have a little bit and then keep the rest for the month.”
“Okay, I changed my mind! Please send me a tub of New York Fudge Brownie.”
With the best intentions, I just take little spoonfuls (small spoon) from the tub while watching TV and, I really don’t know how it happens, it just empties so quickly!
I compare this to the constant emails I get from Amazon telling me that they found something that I might like, and invariably, it’s more of the same. It’s the viral meme: “Dear Amazon, I bought a toilet seat because I needed one. Necessity, not desire. I do not collect them. I am not a toilet seat addict.”
So why do they do it?
Failure to Understand Human Utility: E-commerce systems are brilliant at recognizing that Product A is highly correlated with Product B. If you look at a toilet seat, the algorithm tags you with the “Bathroom Hardware” interest.
It cannot distinguish between a consumable you might buy weekly (like coffee) and a durable good you buy once a decade (like a toilet seat or a washing machine).
The “Data Silo” Blindspot: Often, the marketing engine that follows you around the internet with ads is detached from your actual purchase history. It registers that you spent 20 minutes intensively researching and clicking on Bemis Round Toilet Seats or Church Elongated Seats, but it doesn’t “know” you finally checked out. It just sees intense interest and assumes you are still hunting.
The Numbers Game: From a pure math perspective, ad space is incredibly cheap to run. Even if 99% of people are annoyed by being treated like “toilet seat collectors,” if just 1% of users are landlords, contractors, or people who realized they bought the wrong size, the automated ad pays for itself.
In other words, prediction does not necessarily tell you WHY the behavior occurs.
Yet, we love such a data approach because it shows we are being rational:
We work in a culture that is biased towards things that can be measured (the culture is effectively “if we cannot measure it, it is less real”).
A pressure to sell more of what already works. In other words, naturally favoring exploitation of known behavior to sell more units.
The culture doesn’t ask what our customers are experiencing, but instead we ask, “Can I defend this approach to my boss?”
And scariest of all, the customer becomes a mathematical object of optimization. The language in meetings becomes very revealing! Words such as target, conversion, propensity, funnel, acquisition, lifetime value, churn prevention, etc. I’m not suggesting that any of these are inherently wrong, but collectively they encourage the view of a customer as a statistic to be incrementally pushed. “How can we make them behave in a way that we want,” rather than asking, “That’s interesting? Why are they behaving this way in the first place?”
Organizations that are customer behavior-led have very distinct cultures from above:
They have a great tolerance for “not knowing.” Senior people will say, “We don’t understand this behavior,” without losing credibility. Curiosity is rewarded more than certainty, and everyone is expected to challenge the category itself. Senior people ask more open questions and listen much more.
Frontline employees are respected and listened to. This is the number one test. Organizations that patronize their sales staff with HQ-designed “selling scripts” will guarantee to miss out on what is really going on.
And meeting language shifts from targeting to understanding. Words such as context, need, job, friction, motivation, trigger, barrier to change, workaround, are more common. In other words, the customer is treated as a person in a subjective context, not a data point.
If I were to directly compare the two, I would say: prediction cultures seek internal confidence. Behavior-understanding cultures seek external explanation.
The former may sell more in the short term, but only the latter can innovate in the long term.
How to combine the two?
Open up your favorite AI and ask it to explain “extreme users” and “extreme non-users” in design thinking. This is a way that an organization can gain insight through a data collection model while still looking at outliers of behavior which may lead to insights and innovation for the entire customer base.
I delivered these examples to Etihad senior leadership during innovation training. In each case, they are using the same features of the Etihad lounge product but have different “Jobs to Be Done” (please see my previous article):
Alex the Arrived flyer. Someone who’s traveled a lot for work has finally got that Gold Card! How do they expect to be treated and recognized on their first lounge visit?
Maya the Protective New Mother. Maya has to travel without her partner with a new baby. She’s anxious and doesn’t want the baby to disturb other flyers. She has never flown business class. What are the expectations she might have of the lounge staff?
Peter the Gate Hawk. Peter is totally paranoid about missing his flight and always tends to find himself with no time. How will you use data and the flight board to reassure him?
All of these “extremes” can be identified and served with empathy. This approach encourages moving away from the “typical customer.” Yet the data collected will deliver new ways to service the entire customer base.
Working with your favorite AI to find extreme users in your business category is the first step to understanding motivations and then using data to go deeper.
It is not surprising the local shop understands my need for ice cream. Note, however, it was not Netflix that came up with the expression “Netflix and chill.” It was users who understood their context after a hard day’s work. What other contexts will you find in your category?
(Anand Vengurlekar is an award-winning Senior Lecturer in Innovation and Design Thinking at INSEAD, Bayes Business School, IE/FT, PWC, Korn Ferry, and others. He focuses on mentoring individuals and teams to be more innovative under stressful circumstances and high-pressure deadlines.)
