Strategy: Maximizing Outside List Performance
Most catalogers can’t break even on the initial order when they mail to prospects. That’s often why start-up catalogs have difficulty surviving. Catalogers, new and old, depend on repeat purchases for profitability, i.e., turning one-time buyers into customers who purchase two or more times.
This month, I’ll discuss ways to maximize outside list performance on the initial order to get closer to the incremental break-even point.
You can maximize outside list performance by increasing response and/or your average order size (AOS). I prefer to focus on improving the response rate because it yields a greater number of new buyers, thus growing your 12-month buyer file faster.
Don’t be concerned with trying to increase the AOS to prospects. A deliberate attempt to increase the AOS often results in a lower response rate. When prospecting, you want to maximize the response rate and add as many new buyers to your housefile as possible. The chart shows the advantage of increasing the response rate as opposed to the AOS.
In both results shown in the chart, the revenue per catalog (RPC) mailed is identical. But the response rates and AOS differ. From an operational standpoint, it might seem better to have fewer orders and the same RPC. From a marketing viewpoint, however, a higher response rate is preferable because it results in greater housefile growth. In the example in the chart, the higher response rate yielded 270 more new buyers.
Maximizing outside list performance isn’t easy and requires much testing.
Here are seven proven ways to increase the response rate to outside lists.
1. Use all of the cooperative databases, not just one or two. There are six consumer co-op databases to take advantage of: Abacus, NextAction, I-Behavior, Z-24, Prefer Network and Wiland Direct. Yes, there will be overlap from one database to another, but each co-op can identify names worth mailing, based on its modeling, that another co-op might not find. That’s because all the co-ops build statistical models differently.