Customer Segmentation

Why customer segmentation? People with similar attributes tend to display similar patterns in various ways. This fact is particularly important in customer relationship management, marketing, and risk management. For example, people with certain life-styles tend to buy certain-types of products. Promoting products particularly targeted towards the demographic group can lead to successful marketing. In credit and insurance industry, good customer segmentation can lead to minimum exposure to risk involved in credits and insurances. Similarly, in catalog sales, customers can be selectively targeted to reduce marketing cost. Customer segmentation can be used in various ways.

Technically speaking, customer segmentation is a process that divides customers into smaller groups called segments. Segments are to be homogeneous within and desirably heterogeneous in between. In another words, customers of the same segments possess the same or similar set of attributes. But customers of different segments have differing sets of attributes. Segmentation process can be very complicated. Therefore, it's best to use advanced analytic tools.

Customer Segmentation for Trend Monitoring and Forecasting

Timely identification of newly emerging trends is very important to businesses. For example, sales patterns of various customer segments indicate market trends. Upward and downward trends in sales signify new market trends. The same can be applied to loans, mortgages, credits, and so on. Trend analysis and forecasting over well-designed customer segmentation is a powerful tool for monitoring and detecting newly emerging trends. For more, please read Trend Analysis and Forecasting.

Customer segmentation using decision tree

Decision tree divides populations into smaller segments repeatedly. At a node, it selects a single variable in such a way that segmentation boosts proportions of the largest categorical value in each resulting segment. There are many applications that this type of hierarchical segmentation is useful, especially for direct marketing and customer targeting. The following figure shows CMS decision tree;

Marketing Response Analysis with Gains & Profit Charts

Breakdown of populations into smaller segments induces segments having concentration of certain values. This provides means for cost-effective customer selection methods. For example, for catalog marketing, segments can be visualized with a special response and profit gains chart as shown at the right figure. The blue curve indicates response capture ratios. The green curve shows quantity (or volume) capture ratios. Similarly, the red curve describes profit and loss amounts. Steep rising curves at the left end are a good indication of good segmentation, since selecting a small number of left-end customer segments can capture most potential responses. This will lead to a small number of catalog mailings, and therefore result in efficient marketing campaigns. For more, read Direct Mail Catalog Marketing.

For more, please read Decision Tree Software. Software download is available from CMS Downloads. Try it out!