Why Demographic Data Just Won't Die

This is a really interesting perspective on what many, myself included, may now consider one of the relic's of predictive modeling: basic demographic data. This data is basic, sometimes clumsy--the data we used in college to learn the techniques of statistics, regression analysis, and econometrics. As analytics junkies today, we all strive to build models and tools to help us fit the contours of the populations we study and to levels much more precise than a zip code or an age group. In modeling, there is “power in numbers,” but there is also an aggregation danger at play when using broad metrics which capture individual behavior and preferences.

I have been posting for some time now on this blog about the frontiers of text-analytics and the raw potential inherent in such custom data mining approaches, that I fear I may have become too nano in my purview.

Behavioral modeling is definitely one of the sharper tools in our toolbox, but read this article and you may find yourself having a similar reaction that I did: reconsidering the benefits and devising new applications for using demographic data.

Demographics: The Targeting Construct That Wouldn't Die

Recently, our customers have communicated a message to us loud and clear. It is a message that may seem counterintuitive here in the 21st century, in the all-digital, micro-targeting, behavioral targeting, contextual targeting age.

Demographics, they tell us, are of paramount importance.

No, seriously. Demographics. Like age, gender, household income. I know; it’s as if I told you I was converting all my MP3s to 8-track, right?

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