“Better than nothing? Absolutely, and for customer acquisition, sometimes all you can get. Best you can be? Not if you have the behavioral records of customers.”

What I’m addressing here is the tendency of some Marketing folks to use PRIZM clusters in an inappropriate way, that is, to take a geographic profile (say zip-level) and apply it to a household.

If you are marketing to the entire zip, this makes sense in terms of a model. But if the classification was based on a geo-set and you are now marketing to a single household (a customer or two), the classification is bound to be off.

In other words, it matters where the cluster came from. If it was created at the household level, then it will be accurate, and if that is useful, fine. If the cluster was created at some level of geography and then applied to the houshold, chances are it won’t be accurate.

Either way, if you have behavioral data, and behavior is what you seek, whatever model you are constructing should be based on this data before even considering PRIZM.

PRIZM is not a replacement for actual behavior but sure is useful as another measure—even more so when you don’t know anything else or have some many behavioral items that you need some means to aggregate. Think about it—what does someone do with thousands of click stream points? How do brand managers align that with their overall marketing, branding “and promotion strategies?

Like I said, I’m not sure I disagree – but at least we should get the facts right.

Demos can be useful to classify groups of folks when no behavioral data is present, as in many offline media buying situations. But if the end goal is a behavior of some kind rather than an “impression” against a nameless, faceless demo, behavior is what you need to model.

I would toss survey data (what they say) into the demo bucket as well as far as being predictive of behavior, but this post has already ruffled a lot of feathers. Let’s just say post survey tracking of “what I said I would do” versus “what I actually did” is critical. I have seen enough inverse correlation between the two to be very wary of acting on survey data alone. Part of the puzzle, not the answer, often the wrong answer.

You can see it in the numbers too. I find that it’s rare to be able to build a model on non-behavioral data (even when it includes geocodings) that has a gini coefficient higher than about 40%, whereas models built on behavioral data frequently get gini values of 70-85%, especially in credit risk.

Our genes, our location and so on all tell part of the story — maybe more than we’d like — but it remains the case that you get a much better understanding by looking at what people do, than at who they are.

I don’t have anything against geo-demographics, they simply are what they are. But after they gained momentum in the early 90’s there was an effort to sell them for every application as a “magic bullet” and they simply do not work well for certain applications. I know this idea is incredibly attractive to the many IT-centric marketers on the web, so thought I would provide some background.

I’m still leary of using geo-dems as a “match back” to provide color for a customer database due to the fragmentation issue – the penetration of customer accounts in any one geographic location is usually so small the margin of error could be quite large. The “You will need to augment it with good market research” comment by Diamond addresses this issue.