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Direct Selling: Making a Match

Perfecting matchback in an imperfect world

March 2008 By Steve Trollinger
No industry standard exists for matchback processing. That much is true. But in this article, you’ll learn methods that, when used appropriately, just might help you with the all-important task of better identifying the sources of unknown orders.

First, let’s identify the real problem with matchbacks. Working with various companies during the past several years has given us the opportunity to see the outcomes of a number of matchback processes. The results? Most consumer catalogs see match rates between 25 percent and 50 percent for unaccounted-for orders (i.e., orders that cannot be attributed directly to a catalog mailing, e-mail campaign, search marketing or pay-per-click program, or other trackable effort).

B-to-B mailers often see higher hit rates at the company level. But, not surprisingly, at the contact level, match rates often are even lower than with consumer mailers. Why is it that matchback doesn’t work better?

Lack of Address Standardization
Common matchkeys (discussed below) rely on algorithms that cannot process nuances in address entry, such as E instead of East or Trolinger instead of Trollinger, or order records that contain a company name in the last name or address 3 field.

The best way to produce immediate gains in matchback results is to evaluate and fine-tune your methods for address standardization. Every directional, roadway indicator, post office box, apartment or suite number must be represented the same way within the mail file and the response data. Once you know that every effort has been made to build a good foundation, developing a matchkey that accurately links responders to the mail file is critical.

The Key That Opens All
This article began by making the point that there is no industry standard for matchback processing, and that fact is perhaps no more apparent than in matchkey development. The matchkey is a string of data that you ultimately use to link records on your order file to records in the mail file. Matchkeys are necessary because exact matches on name, address and ZIP code are extraordinarily rare. The challenge lies in how the matchkey should be developed.

In matchkey development, every character counts. The length of your matchkey is up to you (or your service bureau; I’ll discuss that later), but it should be meaningful and balance the risks of over- and under-matching.

Over-matching is when too many records are indicated as matches given a particular matchkey. If, for example, you used the first two digits of a street number and ZIP code, the key 5866202 could potentially match several records in your mail file.
 

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The Business of Database Marketing covers all the bases for the typical business reader. It even includes a catalog of the 37 “Best Practices” and a roundup of some of the major “Dos and Don’ts” in making business sense of the world of database marketing. It will be the one...

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