People-Measurement, Visit Segmentation and Intentionality

People-Measurement, Visit Segmentation and Intentionality

By Gary Angel


October 17, 2023

Segmentation and Shopper Measurement

Visit segmentation is the single most important technique in both digital analytics and people-measurement. It’s critical to both good reporting and deep analytics and in most behavioral analytic applications, it replaces the use of traditional demographic segmentation.

What makes visit segmentation so important in behavioral analytics?

At the highest level, visit segmentation matters because it’s the best tool we have for capturing intent. People are intentional creatures. We do things for reasons. And those reasons are the most powerful explanation there is for behavior.

When I walk into a store, I steer a course to one side or another. I do this (usually) not because I am, like molecules in a liquid, moving chaotically. Nor am I pulled by the gravitational force of some massive display. The forces that steer my course are mental and are all about intentions. Sometimes those intentions are explicit. I enter a convenience store and I am looking to buy a Diet Coke. Sometimes my intentions are focused but not that specific (I am looking to buy a cold beverage for the road). Sometimes my intentions are quite vague (I just want to stretch my legs but maybe I’ll get something).

These intentions will largely determine my behavior. They are not the only determinant of my behavior. The environment matters as does my knowledge of that environment and contextual information that surrounds my intentions. If a store doesn’t have Diet Coke or charges $4 for a can, my intentions may alter or be entirely thwarted. If I see a cooler, I may head toward it only to discover that it is all beer. I may be tempted at the cashwrap to buy a pack of Oreos or even a bag of chips though I had no intention of doing so when I entered the store.

Behavior is an amalgam of intention, knowledge and environment – and it should always be read as such.

Still, there is NOTHING so explanatory of my behavior in a store as my intentions. Why did I walk to the cooler in the back? Because I wanted a Diet Coke. Why didn’t I linger at the beer there? Because I wanted a Diet Coke. Why did I then proceed to the second wall of coolers and buy a can of Diet Coke. Repeat after me – because I wanted a Diet Coke. And why did I add a small pack of Oreo’s at the register? Because I am weak-willed, sugar-toothed, moron whose intentions were defeated by my environment.

So if you want to understand why people did what they did in your location, you need a way to capture and describe intentions.

One way to do that is to ask people. Survey research is a powerful adjunct to behavioral analytics precisely because it is the best way to illuminate intentionality. But as powerful as survey research is, it is limited in scope. We can only ask a very small set of people what drove their behavior. For everyone else, what we have is their behavior. What we need to do is map intentions onto behavior.

And that’s what visit segmentation does. It maps intentionality onto behavioral patterns and in doing so, it helps describe behavior in the most meaningful way possible. In doing this, it can cast a powerful light on a range of location performance issues. Did people whose intent was to buy a Diet Coke consistently go to the wrong place? That’s a problem in store layout. What product is a Diet Coke buyer most likely to buy with their soda? That’s a layout opportunity. The intelligence can only be derived once we’ve attributed the underlying intention to the shopper.

In digital analytics, we came to believe that it was almost impossible to do good reporting or analytics except within the context of a visit segmentation and the same is very much true in people-measurement. Intention is ubiquitous, powerful and impossible to ignore.

In the next post, we’ll explain why visit segmentation is ESSENTIAL in reporting and why people who talk about Key Performance Indicators (KPIs) nearly always get it wrong.

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