Small Catalogs Forum: Finding Gold in Segments of Dross

In the June issue of Catalog Age, I discussed using RFM — recency, frequency, and monetary value — as an effective tool to identify segments to mail. Using RFM, you may have identified segments that would not be profitable to mail to. But within these segments remain significant quantities of responsive names that are indeed worthy of mailing.

Identifying these worthwhile names within the weak-performing segments takes work. But to not mail these names is to leave gold on the table.

Enhancing names in the merge

The “interaction” of your house file with other lists in the merge/purge can help you to identify responsive names. Yet most mailers miss this opportunity, simply letting valuable names they have paid for or exchanged disappear. I recommend that you break every segment of your house file into two subsegments — names that remain unique to you, and those that also appear on an outside list. This will allow you to fully evaluate the effect of the interaction for all RFM segmentation.

The Intersect with Outside Lists chart (above right) shows the effect of this interaction on response for a typical home decor catalog. Buyers who had bought within the past 24 months and who also appeared on an outside list performed 1.94 times better than those who were unique to the home decor cataloger.

Just as interesting, customers who hadn’t bought for at least two years and were on an outside list performed 1.93 times better than those older buyers who didn’t appear on an outside list. These older buyers apparently not only have affinity with the home decor catalog but have also purchased from a competitor in the recent past. They have come alive again! So even if your RFM model indicated that mailing to names on your house file who hadn’t bought from you within the past two years would not be profitable, it might pay to isolate those older names that appear on an outside list and try mailing to them.

DMA Mail Preference Service

Hopefully, you are using the Direct Marketing Association’s Mail Preference Service (MPS) as a suppression file in the merge/purge against lists that you have rented. The MPS is a list of individuals who have registered with the DMA their desire to not receive unsolicited mail. It is critical that you apply this suppression to avoid wasting your catalogs by mailing those who do not want to receive them.

Most mailers do not apply MPS as a suppression against their own buyer or requester file — after all, you have the absolute right to mail your own house files unless you have received a request from a specific individual to be removed from your list. But we recommend that you isolate and test-mail those house files on the MPS.

The results may surprise you. You may find that you should suppress the mailing of certain house segments on the file — or not. The Intersect with DMA Pander File chart (next page) shows the response for our typical home decor catalog mailing for records on the house file that hit and did not hit the MPS. House file buyers who were registered had a response rate 2.01 times higher than those who were not registered. Likewise, registered requesters had a response rate 1.76 times higher. The buyers on the pander file may respond more because they receive fewer competing offers.


One of the first steps in the premerge process is to apply the National Change of Address (NCOA) file to your house file as a portion of the list hygiene processing. Among the buyers from our home decor catalog who had registered a change of address, response was 1.82 times higher than among those who had not. Requesters with an address change had a response rate 1.11 times higher than requesters who had registered a change. Incidentally, our experience with different types of catalogs shows that these results vary significantly — for instance, among apparel catalogs, the difference between buyers with address changes and those without is typically minimal.

Certain clients had also been successful in mailing to the customer’s old address, even though that customer no longer resides there. If you sold, say, water garden accessories to the person who had recently moved out, there is a high probability that the new resident will need your product shortly after moving in. Try sending the new resident a catalog with a well-written letter introducing your company and informing him that the previous resident found your catalog very useful in addressing features of the property.

Postmerge techniques

While the above techniques will identify significant numbers of names to mail that would otherwise go unmailed, you can further fine-tune your mailings using information from cooperative databases. This is true whether you are using RFM for mail decisions or more sophisticated modeling techniques. In either case, you will reach a point where how your buyers and requesters behave in terms of other catalog companies becomes the most important variable in determining future behavior.

Co-operative database the Abacus Alliance (from Broomfield, CO-based Abacus, a division of DoubleClick) has purchase data on more than 90 million households and 1,800 member companies. Abacus offers several products that can identify names on your house file to both suppress and/or mail:

Traditional optimization.

Assuming that you have put every name you own into the merge/purge and applied the techniques discussed above, you can send Abacus “batches” of names coming out of the merge that you have decided not to mail. Abacus will identify the dregs in these batches (the bottom 10%-35%) to suppress or the cream (the top 10%-35%) to mail.

How many batches you send depends partly on the size of your house file, since each batch should contain at least 70,000 names. (Smaller batches are possible but are less statistically exact.) Ideally, you’d want to send separate batches for your buyers and for your requesters.

Abacus ranks each batch into 20 segments of equal quantity, with a 21st segment consisting of records that did not match its database. The average mail order activity for each segment will be indicated by RFM. Initially, it’s wise to track results for the mailing or suppression of different ranges of segments to determine how many segments can be included for suppression or selection. Abacus prices this product at $0.045 per name suppressed or selected (whichever quantity is smaller), with a $1,000 minimum. You can include prospects in meeting the minimum, so this product can work for fairly small house files.

House file reactivation models.

Abacus’s house file reactivation models are effective, but this technique is only suitable for a minimal reactivation file size of more than 100,000 names. You should initially test-mail several ranges of segments with different keycodes to track differential response. Only by such testing can you determine if the process works for your file.

While the pricing of this model is also $0.045 per name selected or suppressed, whichever is less, you must apply the minimum charge of $1,000 to the single model and target file. For example, if you are not selecting or suppressing at least 22,222 from your buyer file, you are not fully using what you are paying for, and the process becomes less cost-effective.

For catalogs with nonperforming files large enough to apply reactivation models, we have found it useful to alternate the optimization process with modeling. We believe that both products are highly valuable, and each may find different incremental names to mail. Some mailers use the ranking of either optimization or modeling to tailor specific offers or discounts based on the need to lift response (for instance, segments that are less responsive might receive a deeper discount offer to entice them to buy).

Another co-op database, Experian’s Z-24, has transaction data from 600 member companies. We have found the file useful in creating successful prospecting models, but not reactivation models. Companies doing their merge/purge at Experian can apply another optimization product, DT-ABC, during the merge itself based on modeling both Z-24 data and demographic data from the Experian database. Our tests have shown DT-ABC to be effective.

I-Behavior and Prefer Network are two relatively new databases with, as yet, fewer members and transactions. While they offer reactivation products, we have yet to test them with a client. As these co-ops grow and mature, they may offer a viable product, since they model data at the specific product transaction level as opposed to the company level.

While segmenting by RFM is critical in making mailing decisions regarding entire segments of names, the approaches discussed above allow you either suppress potentially unresponsive names from segments you intend to mail or identify names to be mailed from segments that were, as a whole, undeserving of mailing.

A final recommendation

Because we have found these techniques so effective, we recommend placing your entire house file, no matter how old or how large, into most merges to identify the interaction with other lists in the merge. Following the merge, there is no penalty for sending Abacus large files for processing, since Abacus charges for what is selected or suppressed by batch (whichever is less). Given the escalating in-mail cost of catalogs, such processing is almost always cost-effective.

It’s important to note that we have found catalogs where some of these techniques have not worked. In a few of these cases, the catalog served such a niche that their customers’ activity with other catalogs was perhaps irrelevant. This underscores why, as with most other catalog marketing programs, you should always test first. It is relatively easy to hold out test panels to determine if these techniques are valid for your catalog.

In the October issue, we will discuss how you can use the merge/purge process to identify and isolate responsive and unresponsive prospect names. But remember that it is almost always more profitable to maximize the mailing opportunities of your existing house file than to mail more prospect lists.

John F. Lenser is president of Lenser, a list firm and consultancy in San Rafael, CA.

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