Sources of Profit From Your Database
1. For consumer magazines, determine the worst-performing ZIP codes for
• Using ZIP-level data and house data driven down to a ZIP-code level, you
can determine ZIP codes that should be eliminated from your outside list
mailings at the merge/purge stage.
2. Using promotion-history data, and demographic/psychographic data for
consumer magazines and D&B data for business publications, you can improve response.
• Models can be built that utilize promotion-history data to help you increase your response rate after the merge/purge process.
3. With your new database, identify customers for “cloning” to assist in more efficient acquisition strategies for both consumer and business publications.
1. Build conversion models to identify likely first-time renewals by source for each title or by affinity.
• Conversion models in both the consumer and business publication space provide a huge payoff since a model can effectively identify “renewers” in an otherwise poorly performing source.
• Efficiently converting subscribers and improving renewals at each contract has a compounding effect in the growth of a subscriber file.
2. Next, build models to determine “non-conversion” renewals by source for each title or by affinity.
• When you have many small titles on related topics, it may be more cost-effective to build a single model for all titles in that affinity rather than build separate models for each.
3. Determine a subscriber’s renewal timing.
• Alter renewal notice cycle to accommodate late/early renewers based on predictive models.
• Saves on mailings that would not receive responses.
4. Reallocate renewal efforts by model score within a renewal class.
• For subscribers scoring at the top of the model (those most likely to renew), increase renewal efforts; for those scoring lowest, reduce renewal efforts.
5. Determine a subscriber’s price sensitivity and “step up” pricing strategy.