Predictive Tactics: Five Database Modeling Strategies
If you think you’ve done all you can to improve the success rate of your list selections, you should stop reading right here. But if you suspect there’s more to be done, try database modeling. It’s a tool that can benefit nearly every catalog in terms of fine-tuning list selections — both on housefiles and outside lists.
Here are five tactics to consider for boosting the effectiveness of modeling:
Tactic #1: Recognize which kind of models can be most useful for you and will give you the biggest bang for the buck.
According to Bryce Connors, director of the Consulting Services division for Unica, a Lincoln, Mass.-based provider of enterprise marketing management solutions, four types of models are most applicable to catalogers:
• customer segmentation models, which break out customers based on their similar characteristics (e.g., behavior, demographics);
• response models, which predict who in the database is likely to respond to certain types of mailings and offers;
• models with cross-selling capability that predict which next product a customer likely will buy based on his or her past purchases; and
• lifetime value, which examines customers’ past behaviors and looks forward to predict who may be the most valuable customers in terms of sales over time.
The response model is the most basic, and therefore would likely be a starting point for many catalogers, Connors says. “This model gives you, the mailer, answers on whom to mail or not mail so you can narrow your audience, reduce costs and trim circulation. The mail vs. no mail — zero or 1 — outcome you learn from this type of model will give you the biggest bang for your initial modeling investment. You’ll see a difference right away in your response levels and reduced costs.”
Tactic #2: Use a model to help refine your mailing strategy to cut unwanted circulation.