A Scoring and Choice Model for Multistage Cross-Selling in the Insurance Industry, Part 1

Radha would like to thank Ajeshkumar Vijaydas for contributing this column.

In the insurance industry, it is common to engage customers in repeat business by offering value-added services in the form of high-class customer service and promotions. Typically in the health insurance industry, companies can identify multiple cross-selling opportunities by crunching data in the underlying customer and transactional databases. These opportunities can be identified through predictive modeling and data mining techniques. High-end mathematical and statistical theories help analysts develop appropriate models for usage in predictive analytics.

In this column, I introduce a chain of data mining and predictive modeling techniques to identify the probable shopping patterns of customers buying related insurance products.

Initially, customers are segmented based upon their age, region, income, length of relationship and product coverage. The hypothetical variables are then derived from transactional data, past illness data and demographic data.

Consider individual and family health plans from different products. Start by creating different segments with the identified variables to form the linkage as a total number of active insurance coverage and independent variables from transactional data. Some examples of variables are:

Insured ID,
Plan name (product type);
Plan type;
Length of relationship;
Total insured amount;
Premium amount and socioeconomic status data like age, gender, region, ZIP codes employment status and years of experience;
Employed in public/private firm;
Income class; and
Past illness.

Three sets of plans are selling widely across a particular region and, from the total plan offerings, 15 types of plans are popular for modeling. SAS enterprise software is used for modeling under the SEMMA framework. Figure 1 explains the methodological framework for modeling in SAS Enterprise Miner.