Polish Your Data Dictionary Part 2

Last week Brent Bissell, president of Minneapolis-based consultancy Direct Target One, kicked off a two-part series on the importance of data best practices. Here he concludes his series.

During the course of the interviews I conducted to collect the information for this article, a number of people echoed the same theme: There has been a mass exodus of workers who really know lists and data hygiene protocols, not to mention the upper-level analytic techniques and the skills to know what to use under which conditions. When it comes to basic direct marketing data knowledge, as one interviewee put it, “what should be standard common sense becomes ‘shock and awe.’”

As a result, marketers have grown more than eager to turn over all their data management and marketing needs to a trusted data services provider. Overall there is a marked movement toward bundled services.

Clearly we are seeing a spending shift among data-hygiene customers. Are we seeing more response via multiple channels housed in the same database? Absolutely. Many firms that have sent catalogs out for years, if you now look at the response return solely through the mail effort, their catalogs look unprofitable. But once you factor in the percentage of Web orders that were driven by that catalog, you get a different—and healthier—bottom-line figure.

The holy grail of data seems to be individual-level data. As for SKU-level data, no-one’s really been able to prove that in large quantities the information works for direct marketers. Said one industry professional “It’s been a very niche thing. It’s kind of like saying, I caught one big fish using a boat anchor, so now I’ll catch a lot more fish using a boat anchor.”

If you are a highly specialized merchant, with a very tightly defined niche, then SKU-level data may work well for you. But for larger mailers if you try to roll that out in bulk, there are problems with the solution and it starts to break down.

Let’s say that both Orvis and Bloomingdale’s sell a blue shirt. But Orvis’s blue shirt is for fishermen, whereas Bloomingdale’s is a fashion-forward women’s shirt. In this case, unless the SKU detail is even more granular—and at least one company claims to offer that sort of detail—selecting “blue shirt” isn’t an effective use of data marketing.

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