Data integration and keeping the decision in mind

Some time ago I was at a warranty conference and there was an interesting discussion about registration cards. You know, those postcard sized mailers you are asked to return to register your product. They often have all sorts of demographic and interest questions – asked by the company to flesh out its 360-degree view of its customers.One of the speakers was asked about this and he argued that, in fact, companies should ask for the absolute minimum information on these cards.

Some time ago I was at a warranty conference and there was an interesting discussion about registration cards. You know, those postcard sized mailers you are asked to return to register your product. They often have all sorts of demographic and interest questions – asked by the company to flesh out its 360-degree view of its customers.One of the speakers was asked about this and he argued that, in fact, companies should ask for the absolute minimum information on these cards. This would, he said, increase response rates and would have little or no effect on the value of the data because all the demographic data could be purchased anyway once you had the list of customers and some basic information about them. In other words companies were identifying fewer customers because they were worrying too much about the amount of information they have about those customers. I took a couple of lessons away from this.

First, always consider the potential for external data to improve an internal process. Just because you want some data it does not mean you have to ask the customer for it. Buying external data and integrating it might be more cost-effective. And you might find you can infer the data analytically too, using historical records like purchases or returns to derive customer characteristics like preferences or approach to online purchasing.

Second it reminded me of the importance of beginning with the decision in mind. Too often I see companies embarking on data integration and quality initiatives designed to improve all their data – presumably so they can make better decisions – without really thinking through what those decisions are. If you begin, instead, with the decision, then you might find that you only need some of your data integrated, that some of it is good enough to make the decision (even though it is pretty dirty) or that some of the data you need has to be sourced from outside the company anyway.  If you don’t know which decision you wish to make or improve then you can’t know which data is truly important.