Segmentation Strategies That Work
• Append cooperative database model scores.
• Don’t overlook other possible types of information, such as response channels, prior back-orders, promotion history, retail trade areas, address deliverability scores and gift purchasing.
Generate predictive attribute reports. By matching the file of customer characteristics to mailings and corresponding orders, you can create report tables to show sales per mailing performance for each variable from the campaign. The reports will look like the one shown in the chart on page 27.
Review your reports carefully; They’ll offer both a profile of your customers and a view of the characteristics that relate to response. During the review process, sort out those that are most highly predictive to be considered as finalists for your new segmentation architecture.
The short list of remaining variables must be looked at in combination with one another. Start with some simple cross-tabulation reports. It often works best to force recency and frequency, and continue to build from there. With every cross-tab combination, generate performance reports as if these criteria actually were used for the mailing.
• Set realistic expectations. It’s said that RFM is an 80-percent solution. Out of the APA project, you’ll uncover some refinements to enhance your current RFM selection criteria. But don’t expect RFM to be replaced; rather, it should remain the backbone of your selection parameters.
• Experiment with different rollup ranges (e.g., $0 to $15). Nothing states that $25, $50 and $75 must be the purchase dollar cutoff points you use, or that all four-time or greater multibuyers should be treated as a single customer group. Identify the ranges most appropriate for your catalog.
• Concentrate on the customer groups in which the greatest gains can be recognized. It may be best at this time to focus on the middle tiers within your housefile rather than on the loyal buyers at the top or the inactives at the bottom.