CRM And The City

Taking your CRM efforts and creating personas around user behavior is not a new idea. In fact, most marketers have already earned their stripes creating deep, targeted user profiles based on data they have and trends they see; the user falls into a particular bucket and they are marketed to accordingly, end of story.

Candace Bushnell, author and creator of the notorious “Sex and the City” franchise, once said that all of her main characters actually represented all facets of the female personality. Essentially, in her view we are all a little bit Carrie, Charlotte, Miranda and even Samantha. Bushnell’s aim was to shine a light on the many faces, quirks and personalities wrapped up in the modern woman.

This got me thinking, what if your end-user, which you fit neatly into one persona, actually fits into all personas in some form or fashion and is spread more across affinity rather than activity? Can your “Carrie” actually be a “Charlotte” and a “Samantha” too?

Making an Affinity Graph

The use of circles is commonplace now, from Google+ to those annoying call circles on cell phone plans. If you have one circle for a certain kind of customer, have you thought about conjoining circles? Concentric circles build affinity groups so that they actually make sub groups or “bleeds” of customers that carry similar traits but behave differently. (A separate but equal measurement, if you will.)  By using concentric groups, you can easily begin to plot customers who have commonalities of traits but perhaps have diversified demographics.

Understanding that all customers in your database have one common characteristic (your product or service), you may have hard squares around short definitions of them based upon purchase history, lead generation source, marketing efforts and even lifetime value.  However, if you begin to blur and blend the lines of the groups into each other around geography, length of time between purchases, gender, age, and type of products they are interested in and even social media activity you will begin to see new customer patterns evolve based on affinity.

This “affinity graph” is really your new blueprint of how to get new data out of the old data. Sure, last year’s Carrie looks a little more Miranda this year but is not quite as old as Samantha, yet it turns out Carrie and Charlotte look an awful lot alike in dim light. If you market Samantha and Charlotte the same way, could it be that she’s the same customer sitting in a “cusp” in your database of personas and user profiles that are more alike than you realize? Are you losing the Miranda customer because you had no idea she can be little Samantha-like sometimes?

Before you take a broad brush and paint all customers together, however, it should come with caution that you need to truly study your customers carefully, including non-impact habits such as time spent on site/store, multi-channel behavior, and even time zone. As you look across the ocean of customers, you have to try earmarking any behavior and criteria and then go back and subgroup some of that behavior to see your matches.

As you comb through this data what you will begin to see are affinities and patterns that tie certain customer groups together into subgroups. Looking at the common characteristics of subgroups (such as financing or loyalty program membership) may glean a whole new way to communicate. Perhaps you discover a whole new customer base or maybe we just all look alike with the lights off.

And after all that data review, you deserve a Cosmopolitan … fabulous!

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