David Shepard Associates Blog

I recently saw a pie chart that indicated companies were spending 50% of their direct marketing dollars on Customer Retention and the 50% on Customer Acquisition. Last year,  Customer Acquisition accounted for 60% of spending and only 40% on Retention.

Assuming that these numbers were true, in the sense that they were an accurate barometer of what’s happening in the direct marketing world (whatever that means) the implication would be very disturbing. While there is nothing wrong per se about spending on customer retention, you can’t grow a business that way – certainly not in terms of the number of customers, and almost certainly not in terms of revenues and eventually not in terms of bottom line profits.

A related finding was that 60% of the respondents reported that they would be spending more (across all channels) on direct marketing activities next year while the balance would be spending about the same.

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In our last publication on this topic we argued that segmentations based on a simultaneous combination of survey generated demographic, behavior and attitudinal data tend to produce a small number of interesting, fun to name but often fuzzy segments. Fuzzy in the sense that while the segments differ with respect to the average value of important segment defining variables, the spread around the averages can be significant.

For example, Segment 1 may include older married couples, who are frequent buyers, and who prefer to shop at retail; as opposed to members of an adjacent segment, Segment 2, who are also, on average, older married couples, also frequent shoppers, but who prefer to shop by mail.

Now, the problem is that included in Segment 1 will be some older singles and some younger married couples, who got into that segment because of their shopping patterns and their channel preferences closely matched those of members of segment 1.  This result is what we mean by a fuzzy segment.

So, what’s the problem with fuzzy segments?

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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. 
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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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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:
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