Securities Brokerage Case Study

This case study documents a predictive market segmentation model designed to identify and profile high-value brokerage customer segments as targets for special marketing communications efforts. The dependent variable for this ordinal CHAID model is brokerage account commission dollars during the past 12 months. Predictors include proprietary client data (various account status and trading behavior variables) as well as syndicated demographic and lifestyle variables.

We begin by splitting the client's entire customer file into a modeling sample and a validation sample. (Once the model is built using the modeling sample, we apply it to the validation sample to see how well it works on a sample other than the one on which it was built).

The resulting CHAID model has 55 segments. For reasons of confidentiality, we will not display the segmentation tree diagram here. However, the results are summarized in the following comb chart, showing the segment indexes (indexes of average dollar value), and in the associated Gains Chart and to defend using different or more aggressive therapeutic approaches for some patients.

This summary comb chart is a quick way to confirm that the segmentation model discriminates degrees of customer value quite well. The top segment has an index of just over 900, reflecting an average commission value of just over $1,000. In contrast, the bottom segment has an index of just two, reflecting an average commission of less than three dollars.

Next is the gains chart, which provides quantitative detail useful for financial and marketing planning. In the gains chart, we have highlighted the top 20% of the file in blue, the remaining above-average segments in green, the bottom 20% of the file in bright red the remaining below-average segments in dark red. Among other things, we can see that the top 20% of the file is worth an average of about $334 per account, which is nearly three times the average account value for the entire sample.

Gains Chart: Average Annual Brokerage Commission Dollars

We can use the data in the gains chart to perform various financial calculations. For example, by multiplying the size of one or more segments by the average segment dollar value, we get a total value. Using this information, we can better plan our communications/promotion budget. Similar calculations performed on the under-performing segments provide information about potential cost savings achieved by reducing marketing efforts directed at these under-performers.

In general, the best segments represent customers who are experienced, aggressive, self-directed traders. We can perform additional diagnostics on these segments to identify those customers who are below average for their segment. This means that they have the same or similar characteristics as their better-performing cohorts on the model's predictive variables, but are not providing as high a level of commission dollars. By using the demographic, lifestyle and trading behavior variables to define them, we can develop marketing and advertising communications strategies tailored to them, the goal being to convince them to trade at levels similar to their more lucrative counterparts.

There are many other decisions which the gains chart and the segmentation rules can help us make. For example, we might wish to conduct some market research among customers in under-performing segments, or among under-performing customers in the better segments. We can use the segment definitions to help us identify possible issues and question areas to include in the survey.

However, before we try to apply such a model, we perform a validation against a holdout sample, to confirm that it is a good model. In this example, our database was split into equal-sized modeling and validation samples. We can reconstruct the CHAID segmentation model on the validation sample, and examine the results. For example, when we perform correlation analyses on the segments from the two samples, we obtain a correlation of approximately 0.98 for the segment sizes and approximately .97 for the segments' average dollar value, indicating a very high degree of correspondence.