David Shepard Associates Blog

From time to time there are articles in the trade press and the academic press about the relative merits of Neural Nets, Logistic regression and what I’ll refer to as relatively simple but structured RFM analysis.  The usual conclusion is that given the decision to investigate a specific number of potential predictor variables, it’s not always true that neural nets will beat regression, or vice versa. The other conclusion is that both methods allow for consideration of more variables than RFM does, and by definition that’s true. However, if all you want to look at is RFM variables, then a simple RFM analysis may be fine, and an RFM analysis guided by a CHAID analysis, is probably the best way to handle this option, although the 125 cell technique suggested by some consultants works for many people. 

In reality, the decision to use one method over the other should be strongly influenced by other considerations,
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Part 3 of a Multi-Part Series

In our last article we concluded that segmentations based solely on demographic and behavioral data were relatively easy to build (using samples drawn from the customer file) and that it was relatively easy to project the results of the segmentation to the
entire customer database. 

Relatively easy as compared to what?

Relatively easy compared to segmentations based on surveys that attempt to get at the reasons why customers behave as they do. And,  we argued that while survey based research was extremely valuable, it was no means certain that we could find correlations between attitudes and behavior, and if such correlations did not exist, it would then be difficult if not impossible to accurately assign all of the customers in one’s database to the segments discovered by the research.
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