SETTING UP PROFILES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Profiling is a familiar approach to many problems. It need not involve any sophisticated data analysis. Surveys, for instance, are one common method of building customer profiles. Surveys reveal what customers and prospects look like, or at least the way survey responders answer questions. Profiles are often based on demographic variables, such as geographic loca­ tion, gender, and age. Since advertising is sold according to these same vari­ ables, demographic profiles can turn directly into media strategies. Simple profiles are used to set insurance premiums.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

A 17-year-old male pays more for car insurance than a 60-year-old female. Similarly, the application form for a simple term life insurance policy asks about age, sex, and smoking—and not much more. Powerful though it is, profiling has serious limitations. One is the inability to distinguish cause and effect. So long as the profiling is based on familiar demographic variables, this is not noticeable. If men buy more beer than women, we do not have to wonder whether beer drinking might be the cause 54 Chapter 3 of maleness. It seems safe to assume that the link is from men to beer and not vice versa. With behavioral data, the direction of causality is not always so clear. Con­ sider a couple of actual examples from real data mining projects: People who have purchased certificates of deposit (CDs) have little or no money in their savings accounts. Customers who use voice mail make a lot of short calls to their own number.