How do you maximize response rates?

I might be worth thinking about this problem as a contrast to maximizing something else.

I've been thinking of response rate maximization as if it were a simple problem. "Always go after the case with the highest remaining probability of response." It has an intuitive appeal. But is it really that simple? We've been working really hard on this problem for many years. I think, in practice, our solutions are probably more complicated than that.

If you focus on the easy to respond cases early, will that really maximize the response rate? If we looked at the whole process, and set a target response rate, we might do something different to maximize the response rate. We might start with the difficult cases and then finish up with the easy cases. Groves and Couper (1998) made suggestions along these lines.

Greenberg and Stokes (1990) essentially work the problem out very formally using a Markov Decision model. They minimize calls and nonresponse rate. Their solution wasn't to "always call the easiest to interview case next."

Maximizing (or minimizing) something else is likely to lead to different actions, but it might be good to make less of a "straw man" out of maximizing the response rate.

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