Outside List Optimization

As a cataloger, it’s important to focus on ways to increase response rates from the prospect lists you use. You want to increase the revenue per catalog mailed.

Most likely, you adjust the common selects such as recency and dollars spent to improve results. This month, I’ll explore outside list optimization, another proven method to increase the effectiveness of your prospecting efforts.

There are two ways to use list optimization: selection and suppression. Each technique usually uses 10 percent to 20 percent of a given file.

Selection

This method is used for pre-merge lists. With this technique, you can use a compiled or subscriber file to capture affinity. Then use optimization to select the top 10 percent to 20 percent of the known catalog buyers.

For example, if you run a golf catalog, you might request 100,000 active subscriber names to optimize. For this universe, you can select the “best” 20,000 (i.e., 20 percent of the gross input) to mail.

Suppression

This method is used post-merge to identify and suppress rental singles. After the merge, you can optimize rental singles and suppress the worst-scoring 10 percent to 20 percent. This method should yield a 5- percent to 15-percent (or greater) lift. It’s a good idea to mail a 5,000 to 10,000 back-test cell to monitor the suppression results in order to measure the exact lift you achieve.

Most outside catalog lists include names whose overall buying patterns aren’t typical of that particular list’s characteristics. A high-end gift list, for example, may include people who made a purchase for a special occasion, but who usually make purchases from lower-ticket gift catalogs. Optimization helps by identifying those prospects who don’t match the typical buying patterns of the customers on your own list. Suppressing these names after the merge can raise the results of the entire mailing.

The basic concept of outside list optimization is to identify and suppress the weakest prospect names (often the rental singles) within a rental file, which are performing below an acceptable level. By going through this process of optimization, you’re able to increase response rates and the revenue per catalog mailed, which may generate more profit.

Catalogers typically will suppress 10 percent to 15 percent of a prospect list. This slight reduction in the number of names mailed generally provides enough lift in response to justify the mailing costs for that particular prospect list.

Five Common Uses

1. The most common use of list suppression is optimizing the rental singles that come out of a merge. This is where optimization can pay. The worst-scoring segments of the rental singles frequently perform at less than half the response rate of the average rental name.

2. Catalogers often replace the suppressed names with those from a balance model selected from a cooperative database. Most likely, these models will perform better than the names that have been suppressed. This allows the mailer not only to improve the overall performance of the outside names that are mailed but also to fine tune the number of names on the mail tape sent to the printer.

3. If your housefile and the prospect list you want to optimize both reside with the co-op database, the economics of doing a marginal list optimization are pretty good.

4. Outside list optimization can be effective when mailing to compiled lists. Here, a mailer rents a compiled file (i.e., nurses, hunting licenses, boat registrations) and negotiates a net-net arrangement with the compiler. These names are shipped to the cooperative database company, matched, scored and mailed. This is an excellent method for getting compiled names to work for a direct response catalog offer.

5. Another application is to overlay compiled data on a direct response file. Let’s assume, for example, that you’re a cataloger who markets supplies to churches. You can use data from a company such as R. L. Polk, which has a “bible devotional” element, that has been integrated with catalog RFM (recency, frequency and monetary) to craft a relevant model. Using third-party data such as this can help to expand the universe of names available for prospecting.

There are many applications for outside list optimization. And the economics of going through this process depend on factors such as list costs, optimization costs and the results you achieve. Like most other aspects of good cataloging, testing will be crucial to finding what works best for your offer.

Stephen R. Lett is president of Lett Direct, a catalog consulting firm specializing in marketing, circulation planning, forecasting and analysis. He can be reached at (317) 844-8228 or by e-mail at slett@lettdirect.com.