Cabela's Moves from CHAID to CART (1,105 words)
by Kimberly Rengle
What do you do if your models are overestimating the level of response you'll get? That was exactly the problem that Cabela's, a cataloger of hunting, fishing and outdoor gear, was having at the end of 1996 when it determined that the models used for segmenting its mailing list were not stable and tended to over-estimate expected performance.
Cabela's relies on its spring and fall master catalogs, as well as other promotions, for showcasing and selling its entire product line. Although the mailings were still profitable, the company decided to explore ways of combining data-mining methods to create more effective models. Cabela's made the move from a CHAID-based system to CART, which can handle continuous variables and more closely detail relationships in the data, says Lynn Karrick, Cabela's database marketing manager.
Building Better Models
Cabela's manages its databases in-house and relies on historical customer data to determine mailing lists for specific catalogs and promotions. The database marketing team is tasked with extracting a 200,000-case subset from millions of records. This sample then serves as a foundation for building the predictive model and testing data that will determine recipients for the new mailing. Generally, Cabela's selects a customer database that targets a certain profitability level for the mailing, and the 1998 spring catalog was no exception. Unlike previous years, however, segmentation of this spring's database was further optimized using Salford Systems' CART regression tree software.
"I have always been able to construct good, profitable models for Cabela's, but our packaged system had data integrity problems and did not always give us stable performance within customer segments," Karrick says. "In addition, since our CHAID-based system could not handle continuous variables, such as a customer's amount of last purchase, our data was grouped into 'bucketed' segments that worked but weren't exactly robust."