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

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