Judge customers by behavior, not fur color

To a stranger, my two dogs look alike. To me, they couldn’t be more different. They came from different dog shelters and are more than two years apart (that’s 14+ human years). Here they are: Maddie has her chin resting on Ally.


Ally is twitchy, a mama’s girl, frightened of loud noises, and getting creaky. Maddie is confident, independent, curious about the loud noise, and energetic. Ally loves other dogs and distrusts new people. Maddie adores all people and is suspicious around certain dogs. Their features and personalities couldn’t be more different. I’ve had some time to get to know them.

When we meet a stranger on a walk (particularly one who isn’t a dog owner), we often get: "They must be related." Our denials don’t seem to phase these people as they point to the obvious evidence: "...but they are exactly the same size and color."

Superficial judgements are natural - a first level of defense to categorizing and manage a complex world. However, it’s unhealthy to not try to dig deeper. For some businesses, superficial characteristics are as far as the analysis goes when segmenting or profiling customers. A better approach is to look at customer behaviors which provides a much more accurate reflection of interests and needs. Jim Novo, marketing consultant, agrees:

Customer behavior is a much stronger predictor of your future relationship with a customer than demographic information ever will be

Simple customer characteristics can be easy to come by; age, income, zip code are probably part of your basic customer database. In contrast, behavioral segmentation is a more initimating analytical challenge. Here’s the approach we’ve used successfully at Juice:

  1. Create individual pictures of customers that visually show their behaviors over time. The trick is to create a "visual language" that represents actions and is intuitive
  2. With a dash of Excel, SAS, and python code, we generate thousands of these pictures of individual customers
  3. We visually scan for common patterns of behaviors and the associated success/failure points (e.g. repurchase, upsell, churn, etc.)
  4. Finally, we work backwards from our new understanding of behaviors to segment customers based on statistical measures of behaviors.

This approach differs from traditional data mining-based approaches that drill down from the top looking for patterns. We start at a very granular level and looks for patterns (using the power of the human visual system). It may sound a little crazy, but we’ve found that it can be both insightful and highly predictive.