David Shepard Associates Blog

Part 4 of a Multi-Part Series

In our last article we discussed the issues relating to the choice of variables that go into a Cluster Analysis. The key points were: (1) choose  only those variables you want to segment around, i.e., don’t include variables that you think are irrelevant to your marketing strategy (2) standardize the variables so that scale (the size of the variables) does not become an unintended issue, $25,000 is not the same as $25m (3) consider giving more weight to some variables than others depending on your marketing objectives, and (4) use Principal Components Analysis to: (a) reduce the number of variables that will go into the solution, and(b) eliminate multi-co-linearity, the undesirable, from a modeling perspective, condition that arises because so many behavior and demographic variables are correlated with each other.

Now go run a Cluster Analysis on the data.

Not so fast.

More decisions have to be made and there’s no one right answer. 
Read the rest of this entry »

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,
Read the rest of this entry »

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.
Read the rest of this entry »

Part 2 of a Multi-Part Series

In our last article we defined the objective of a survey developed, attitudinal segmentation scheme that would separate our hypothetical chain store customers in to four clusters based on the following set of major dimensions: (1) price, (2) service (3) product depth (4) product quality (5) the overall shopping experience and (6) brand image, and asked the question: how would we go about doing it?

If we follow the traditional path we would probably hire a market research company to develop an “in-depth” questionnaire that would be presented to a random sample of a few thousand of our customers. (In the home or through direct mail, maybe even over the phone.)

The results of the survey would be processed and for sure the research firm will identify four or five major segments, let’s call them:
Read the rest of this entry »

Part 1 of a Multi-Part Series

The purpose of this series of articles is to help direct marketers understand the issues and choices involved in a customer segmentation project so that they can manage it effectively from conception to implementation. Along the way we’ll identify common pitfalls that stand in the way of success and suggest ways in which they can be avoided.

In this first issue we’ll discuss the important considerations that must be addressed before starting a segmentation project. In later articles we’ll discus the tools of segmentation: factor analysis, cluster analysis, discriminant analysis, CHAID and logistic regression.

The Goals of Segmentation

The fundamental goal of a segmentation project is to identify groups or segments or clusters (the terms are interchangeable) of customers that from a marketing perspective are meaningfully different from each other.
Read the rest of this entry »