Last week, we explained why determining who not to mail to as an important aspect of prospecting.
We at Lenser had been working with a large multichannel merchant that had seen a decline in response from its prospecting. A zip model revealed that many of the clients books were being mailed into zip codes that had never generated an order, applying a simple zip file to select outside list sources reduced unproductive.
Once we achieved our goal of suppressing the known bad zip codes and undesirable renters, we realized there was another step we could take to help weed out the less desirable names. The answer was to run an optimization on the potential prospect names, but not in the typical fashion in which only the rental singles are optimized. Again, we run into the same situation in that there are good names and there are bad names in every source, and prospect multis are not immune to this phenomenon. Therefore in the optimization we ran, everyone (multi or single) was fair game.
The caveat to this strategy is that the optimization was not your run-of-the-mill recency, frequency, and monetary (RFM) optimization, but rather a custom-built model by an outside vendor. This model takes into account many variables above and beyond typical RFM, and also includes house file data, which, taken all together, supplies us with a robust optimization tool to identify names that should not be mailed. This model is run post-merge, which enables us to look at it on an individual name basis, rather than at the list level. Remember, we are not marketing to a list; we are marketing to a person.
The optimization model was built to segment our merge output into deciles so we could easily shave off the bottom 10% of our prospecting file. Some may argue that this tactic is wasteful and not cost effective, since you have already paid for that name and all of its processing. It’s true that you are throwing away some money, especially if that name was actually paid for and didn’t come from an exchange. Yet it is much more cost effective to spend that $.20 for a name that will be suppressed since it won’t ever generate an order, compared to mailing a catalog for $.65 and not receiving an order. So by suppressing that name to begin with, you are saving yourself $.45 at the end of the day.
This all sounds fine and dandy on paper, but the model has to prove itself out in the real world to justify the process. Fortunately it did just that, as you can see in the chart below. Each of the lower deciles was re-keyed and mailed under its own source to track the validity of the suppression model. The model performed as expected and was able to identify 10% of our prospecting file that was going to yield minimal response.
Travis Seaton is director of circulation, specialty groups, for San Rafael, CA-based consultancy Lenser.