Four Tips for Optimizing Your House File

By now you know that optimizing your house file is a proven way to improve profitability. But you may not know exactly how to go about honing the file. Jim Coogan, president of Sante Fe, NM-based Catalog Marketing Economics, offers four suggestions:

  • Find the best-of-the-best house buyers that you can mail even more frequently. The cream of the house file buyers is not always found in the traditional RFM (recency/frequency/monetary value) segmentation, Coogan says. Optimization can find very responsive names among RFM segments that would not typically be getting the maximum buyer frequency. “Optimizing your house file lets you add profitable names to your buyer files getting the most frequent mailings.”
  • Find the breakeven point for your house file reactivations. Coogan also suggests testing segments as separate test cells to calibrate your breakeven point for the house file. You may not be testing deep enough into your optimization deciles and have more profitable names left to mail. Or you may be mailing too deeply into optimization deciles and need to pull back circulation so that all the deciles being mailed are proving profitable. Remember to always ask for the detailed model summaries of your optimization models. These summaries give the average number of transactions, the number of catalogs purchased from, and the 12-month dollars spent inside the cooperative database and are a common sense roadmap to selecting segments for reactivation or suppression.
  • Divide your house file before you optimize. Don’t send your house file to a co-op database as one big group of names. Segment out older buyers as a separate reactivation group. Likewise, segment out catalog requesters and optimize them separately. Segment out one-time recent buyers for both reactivation and suppression. Segment buyers by channel — traditional call center versus Web versus multichannel.
  • Optimize for finding suppression opportunities. An unknown power of the optimization process is the ability to find households that are unresponsive because buyers have moved and the old address should be suppressed. Optimization can find large pockets of nonresponsive households within your most responsive RFM segments, and often these households have simply moved away and not mailing to those bad addresses will drop dollars to the bottom line.

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