Monthly Archives: February 2010

Tortured Data – and Analysts

Fear and Loathing in WA

You may recall I wrote last year about the explicit or implicit pressure put on Analysts to “torture the data” into analysis with a favorable outcome.  In a piece called Analyze, Not Justify, I described how by my count, about 50% or so of the analysts in a large conference room admitted to receiving this kind of pressure at one time or another.

Since then, I have been on somewhat of a personal mission to try to unearth more about this situation.  And it seems like the problem is getting worse, not better.

I have a theory about why this situation might be worsening.

Companies that were early to adopt web analytics were likely to already have a proper analytical culture.  You can’t put pressure on an analyst to torture data  in a company with this kind of culture – the analyst simply will not sit still for it.  The incident will be reported to senior management, and the source of “pressure” fired.  That’s all there is to it.

However, what we could be seeing now is this: as #measure adoption expands, we find the tools in more companies lacking a proper analytical culture, so the incidents of pressure to torture begin to expand.  And not just pressure to torture, but pressure to conceal, as I heard from several web analysts recently.

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Control Groups in Small Populations

The following is from the January 2010 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: Thank you for your recent article about Control Groups.  Our organization launched an online distance learning program this past August, and I’ve just completed some student behavior analysis for this past semester.

Using weekly RF-Scores based on Recently and Frequently they’ve logged in to courses within the previous three weeks, I’m able to assess their “Risk Level”– how likely they are to stop using the program.  We had a percentage who discontinued the program, but in retrospect, their login behavior and changes in their login behavior gave strong indication they were having trouble before they completely stopped using it.

A: Fantastic!  I have spoken with numerous online educators about this application of Recency – Frequency modeling, as well online research subscriptions, a similar behavioral model.  All reported great results predicting student / subscriber defection rates.

Q: I’m preparing to propose a program for the upcoming semester where we contact students by email and / or phone when their login behavior gives indication that they’re having trouble.  My hope is that by proactively contacting these students, we can resolve issues or provide assistance before things escalate to the point they defect completely.

A: Absolutely, the yield (% students / revenue retained) on a project like this should be excellent.  Plus, you will end up learning a lot about “why”, which will lead to better executions of the “potential dropout” program the more you test it.

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