There is growing pressure the past year on list performance. It’s more about contribution to profit & overhead as it should be. This has caused catalogers to reduce circulation and to prospect less. As a result, 12-month buyer counts are down and there are fewer housefile names to leverage; a real catch-22. This month, I am focusing on how to improve list performance; e-mail and catalog.
eMail List Performance
eAppends are an excellent way to grow your housefile. This involves having a third party vendor append e-mail addresses to your own customer list where (a) their e-mail address is missing or (b) the e-mail bounces back as non-deliverable. In order to avoid any deliverability and black listing issues, follow e-append best practices. For example, your “Welcome Message” should be innocuous. I suggest using your logo and text with no personalization. The Welcome Message may carry an offer (example, some firms have attached an offer to drive retail store traffic) but it is not necessary. Use your follow up retention efforts to close the sale. A household level match will maximize the number of matches; a hit rate of 30% is not uncommon. (In every housefile match, it is best to flag each record “I” for individual and “H” for household.) Individual level matching results in higher quality records but a lower match rate, i.e., 10% to 15%. Once the “append” is complete, you will need a strategy in place for the appended records. Opt-outs are supplied with the appended records and they should be suppressed immediately. Appended records should receive a message from your company within a week of the completion of the append. I suggest a series of three to four messages to the appended records before they are added to the normal retention database messages. Tell your customers why they are hearing from you and why they should be glad about it. Be sure to let your Customer Service Representatives know an append has been facilitated and give them a copy of the welcome message so that they can address any customer services calls and/or issues. Append is truly a home run when executed properly. I’d suggest an increase in the use of your multi-channel efforts. Support your catalog with pre and post e-mails can yield an increase in response rates. Consider sending two or three post-mailbox follow-ups. Single web only buyers from pay-per-click sources, internet channel only buyers and catalog inquiries should all be optimized by the cooperative databases before you mail them. Web only buyers sourced from pay-per-click likely will not respond to your catalog mailings. If the consumer has no history of buying direct, save the postage.
Cooperative Database Performance
Resist the temptation to pull out of any cooperative databases. When a cataloger decides which cooperative database to keep using and which one to drop they use a multitude of factors. One factor is performance. Another is overall contribution. If a cataloger feels they are supplying “X” amount of buyers to a cooperative database, but only taking less than “X” amount of prospect names from the database they feel the contribution is not in their favor and that particular cooperative database should be dropped. Based on this rationale, it is a solid plan. It makes sense that catalogers want to protect their biggest asset in such difficult economic times – their own housefile. But how does this effect models and prospecting names overall within each cooperative database? The problem is that not every cataloger is pulling out of the same exact cooperative databases. Even within the same product category, certain cooperative database work well for some and not others. However, the names shared by all are still required to build an effective model and supply the best prospecting names. Here is a likely scenario which is currently happening across all cooperative databases: Cataloger “A” has decided to pull out of cooperative databases 1 and 2 based on the above mentioned criteria. However, what if the prospect names from Cataloger “B” work the best for Cataloger “A”, but Cataloger “B” has chosen to pull out of cooperative databases 3 and 4 and remain in cooperative databases 1 and 2? Both cataloger “A” and “B” will in turn lose some performance in their prospecting names. In essence, on top of everyone’s 12-month buyer counts decreasing and straight list rentals getting more difficult to get to perform well, now the cooperative databases are also being affected. As more catalogers continue to pull out of various cooperative databases, this situation will continue to downward spiral. In order for catalogers to keep their prospect names at their highest level to get through these tough times, it is imperative that everyone continues to stay within each cooperative database.
Outside List Performance
Maximizing outside list performance can be accomplished by either increasing the response rate or the average order size or both. I prefer to focus on improving the response rate because it will yield a great number of new buyers thus growing your 12-month buyer file faster. I wouldn’t be concerned about trying to increase the average order size to prospects. A deliberate attempt to increase the average order size can often be at the expense of the response rate. When prospecting, the goal should be to add as many new buyers to the housefile as possible by maximizing the response rate. Select outside lists based on “R” (recency of last purchase). You should also maximize rollouts before testing new lists. Double the usage each time (assuming there is enough list universe). After you have maximized your list continuations, fill-in with the lists you want to test. Looking for buyers who have purchased multiple times recently is also a good way to improve response rates. Use Marginal List Optimization to improve outside list performance. There are two ways to use list optimization: selection (used for pre-merge lists) and suppression (used post-merge to identify and suppress the rental singles, i.e., one time buyers). Both techniques use 10% to 20% of a given file. The majority of catalogers choose their list selections on what had worked well in the previous season as well as what is working best currently. This is not a “true” science since there are many factors that need to be taken into account when determining whether or not a list is performing well. Be certain you are doing the proper list analysis and use every technique possible to improve your list performance.