Versioning also can include completely different creative. Perhaps marketing has completed its analysis of first-time buyers and found customers whose first purchase was a solid-gold widget typically make a subsequent purchase of pink flamingo lawn ornaments. The creative team can develop a cover version featuring the lawn ornaments. You may find customers who buy running shoes typically buy running shorts; or customers who buy towels subsequently buy pillowcases. Analysis helps guide the product selection on the front cover. It is important to work with the creative team as they begin to lay out the catalog pages to ensure versioning is incorporated into the creative strategy—not a last minute, “just make it fit” idea.
Segment by Specific Price Points
Customers with only one purchase in their history have specific data that can be evaluated and then segmented for different marketing opportunities. One example is segmenting by specific price points. You can even use items that were bought at a discount or sale price; make sure these first-time buyers receive a sale catalog or promotional e-mail.
This strategy capitalizes on a prior buying pattern. With price sensitivity demonstrated, promotional pricing, two-for-one specials or value-based pricing on the catalog cover can be very successful.
Analysis is the key to uncovering the best strategies. Sometimes the analysis has to be done externally. Statistical modeling to identify customers who are most likely to make a purchase can be outsourced to a database vendor.
If statistical analysis isn’t an option, a different type of analysis is used during the merge/purge process. Prior to the merge/purge, identify one-time buyers by segment and place these names higher in the merge priority than outside rental lists. Once the merge/purge is run, review the duplicates (also known as “hits”) to the outside rental lists. In this way you can recognize recent buying activity and choose to mail the hits from the one-time buyer segment. Recent buying activity is a predictor of subsequent buying behavior, meaning the hits are more likely to purchase than non-hits.