Tuesday, May 19, 2009

Bozoing Campaign Measurements -- II

Our next contestant comes from the telecom world.

What the group was doing was evaluating marketing campaigns over the course of several years. Does an attrition-prevention campaign have any effect after three years? This is an absolutely wonderful thing to do, of course, but not the way they went about it.

The campaigns were in a series of mailings that went out to customers that were about to go off contract, and the offer was a monetary reward to renew their contract for a year. Each campaign had a carefully selected control group.

The dead-obvious thing to do is to compare the treatment group vs. the control group, but that's not what got done. What happened was the analysis compared the whole control group to the customers in the treatment group that renewed their contract, because clearly "customers that didn't renew their contract weren't effected by the campaign".

Sound familiar?

Why doing analysis this way is a bad idea: before the mailing on contract renewal, customers are going to have a certain basic affinity towards the company. Some are going to love it, some are going to hate it, some are going to be on the fence. When the customers get the offer the ones that already hate the company will toss the offer, the ones that love the company will take free money for staying with a company they like, and the ones on the fence may or may not take the offer and have their future behavior change. So, to a good extent a retention program like this isn't changing behavior but instead is sorting the customers into buckets based on how they already feel about the company. Comparing "total control group" to "contract renewers" confounds two effects, one effect of the customers predisposition to the company and the second effect of having some customers renew their contracts for a reward. Moreover, this comparison doesn't actually answer the real question: does the program have a meaningful, measurable impact on churn? To answer the real question in the right way Keep Things Simple and Statistical and do a straight treatment vs. control.

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