One of the most important tasks in trying to model customer behavior is to precisely zero in on predictors which influence a behavioral outcome using advanced analytical models. For example, in the insurance industry, a company might want to identify the top five influence levers that determine whether or not a policyholder would cancel his policy. Some questions that arise are:
Have all the causal factors been figured into the analytical model for predicting a behavioral outcome (i.e., policy cancellations)?
How many predictors were obtained from field immersion vs. exit surveys vs. 90-day customer conversation mining?
What is the step-by-step methodology used to bubble up predictors?
What are the various best practices and techniques in place to harvest solid predictors that can then be statistically tested for significance?
This article outlines a step-by-step process of methodically harvesting the most influential set of predictors using six different methods.
http://www.b-eye-network.com/channels/5407/view/15002
Sunday, April 17, 2011
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