AAA Auto Club South’s Ernesto Diaz and Kristin Rahn on Efficiently Building Predictive Models

AAA Auto Club South has always been an expert on helping its members efficiently get from point A to point B. So when the Tampa, Fla.-based affiliate of AAA had a chance to improve its own efficiency while building predictive models, the association locked in the opportunity.

In June 2009, AAA Auto Club South hired Britain-based integrated marketing solutions provider Portrait Software and began using Portrait Customer Analytics. By August, the affiliate had 6.7 million household and customer records added into the system.

Now, says Kristin Rahn, the affiliate's advanced analytics director, building predictive models takes less than a third of the time it once did. Plus, says Ernesto Díaz, the affiliate's managing director of insights, the association's staff doesn't need computer programming backgrounds to manage its modeling activities.

Díaz and Rahn elaborate on how the affiliate is improving its predictive modeling efficiency and what comes next.

Target Marketing: AAA Auto Club South built predictive models to increase renewals, reduce churn, and cross-sell and upsell its members. How did analytics software aid in that goal?
Ernesto Díaz: I think it would be reasonable to say that from a productivity perspective, the building of those models—the production curve, if you will—was pretty quick. So we're moving into the next phase now, to test against those attrition models some executions to see if we can impact the renewal rate with 10 percent different tiers of our member experience. So we have members that have been members for maybe just a couple of years, and the attrition curve there will be a little bit different than members who have been members for between, say, four and six years and so on. ... And then we've also done a little bit of work on just trying to recreate a segmentation schema that we built about a year or so ago. ...

TM: What data did AAA Auto Club South input to build these predictive models?
ED: We are using all of our active members' membership information. We are adding to that demographic information that we have available from our demographic data providers. We're also taking behavior information—so purchases, active policies and those kinds of things that they may have with us. ... There's another aspect of it that we have that I would probably subcategorize under behavior information, like things that they purchased that we call our "relationship index," which is really measuring engagement of members with different product lines.

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