Considering Farmers Insurance Group has been in business since 1928, it definitely has a lot of historical information about its auto, home and life insurance policy holders. The Los Angeles-based company that began with the intention of insuring the vehicles of rural farmers decided it was time to build on that data and take an intelligent look into the future through predictive modeling aimed at retaining customers.
The process already has yielded a 14 percent increase in Farmers' return on investment for its direct mail efforts. Shiv Gupta, Farmers' director of insight and innovations, says the insurance provider decided to tap a resource it had been using for years—Cary, N.C.-based software provider SAS Institute. About two years ago, Farmers began gathering data and using SAS Analytics to build the predictive model that came online nine months later.
"Our objective was to develop a predictive model for retention and lifetime value of every customer we have in our book of business," Gupta says. "And we have about 7 million households, and the transactions are about 20-fold of that. ... [The predictive model] provided us not only the ability to cleanse large volumes of data quickly and easily, restructure the data so that we could use it for analysis, but then also seamlessly go into the analysis stage and complete that analysis and batch work so that we could actually develop lifetime value numbers for every single household in Farmers—historically, five years back and today."
Gupta explains more of Farmers' thinking:
Target Marketing: How did Farmers Insurance reorganize its direct marketing strategy to have predictive modeling be the foundation?
Shiv Gupta: So, when we initially started asking the question, "Who are our best customers?" we were able to identify them rather easily, looking at history. Then we said, "Can we predict, day one, who our best customers will be?" And that's when the predictive modeling started to come in. ... Using the data that we collected when they walked in the door, we were able to develop a very strong model that helped us separate those customers who were going to be with us for a significantly lengthy amount of time and those who were likely to detrite quickly. Using that internal model and that data, we were able to map to external data that was provided to us by mailing vendors for our own customers. And through that mapping, we were able to develop a mailing campaign model. Obviously, that mailing campaign model doesn't provide as strong of a separation as our internal model. But, nonetheless, it's powerful enough to where we've been able to get significant improvement in ROI ...
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