Using the Right Kind of Data (2,213 words)
If everyone is already mailing these names (the case when companies focus on renting multi-buyers), competition within a cooperative database should not be an issue. The objective is to learn as much information about your customers to be able to make them the right offer at the right time, says Lynde.
When evaluating the efficacy of a cooperative, explains Lynde, many catalogers like the biggest files with the highest match rates so they can append more data to their customer records. However, match rates can be tricky in that the data you append can be old and thus not entirely useful.
A better criteria, he advises, might be the recency of data available for appending.
Define Your Priorities
Before you can build a model, you must determine what outcome you would like to reach. Do you want to increase response per catalog or up the average order size? What about reactivating dormant customers or cultivating customers who could be spending more money with your company?
Harte Hanks' Koslowsky explains that the data used for each model depends on the campaign objective for which names will be selected. For example, reactivation of customers could involve a test mailing to inactive accounts to obtain response data. Further analysis will then determine what kinds of people ordered and help build a model to isolate other customers who fit the mold.
Customer behavior changes are certainly important to consider when modeling, says Lynde, to identify opportunities that can be incorporated into future mailing efforts. A catalog company planning the fall/holiday book would want to carve out response data from similar time periods to determine the characteristics of consumers who buy during this season.
This is just the case for Camenzind and Oriental Trading Company. The marketing department has found that customers are specific to each title, so it develops models for each catalog to plan for more productive campaigns.