Timing, Counting, & Choice. “Most real-world business problems are just some combination of those building blocks jammed together” – Peter Fader
Over at CIO Insight we have this very practical article on Data Mining by Fader. What it’s good for, what it’s not good for. If you have wondered how you might use this tool, especially if you are a Marketer, you should read this article.
I say the article is practical because even though there are many ways to create mathematical models of customer data, if the end result is not something a Marketer can use to actually increase Marketing Productivity, then you really cannot do much with the output. The models have to create leverage of some kind that can be used to take real world action. In other words, a model can be “technically correct” but completely useless to a Marketer.
For example, just because you can identify a segment doesn’t mean it is practical or viable to address that segment with a unique marketing treatment. And just because the segment has unique characteristics doesn’t mean those characteristics create any real marketing opportunity.
Key takeaways for Marketers from this article should be:
1. Too much data tends to mess up a model. This is especially true if you try jamming all kinds of demographic crap into a model that is trying to predict behavior. If you want behavior as an output, use behavioral variables in your models.
2. Data mining is a great classification tool; it is good at telling you why segments are different. But in order for this to be useful, you need actionable segments to begin with. For example, data mining can tell you the demographic differences between people likely to respond versus people not likely to respond – if there is a demographic difference. But you have to know this “likely to respond” element first. While we’re on this topic, the same idea holds true for surveys. If you want the survey output to be actionable, get to known behavioral segments first, then do your surveys of each segment.
Often, people use technical tools for the wrong Marketing reasons. I see this problem coming down the tracks in web analytics, people are getting so wrapped up in the minutia and the automation of testing they are missing out on the basic stuff. Just like the data mining wave got people off track and into the bushes with “collecting all the data so we can mine it”. But it doesn’t matter how much data you have, the tool does what it does and doesn’t do what it doesn’t do.
Check out the article What Data Mining Can and Can’t Do here.
Any thoughts from the Data Miners out there on this?