Survival Data Mining

Intelligent Enterprise has an article this month titled Survival Data Mining for Customer Insight. If you haven't tried these techiniques out, be sure to read.

Survival data mining is...

In the medical world, doctors often want to understand which treatments help patients survive longer � and which have no effect at all (or worse). In the business world, the equivalent concern is when customers stop being customers. This is particularly true of businesses that have a well-defined beginning and end to the customer relationship. A good example is a subscription-based relationship, which may be found in a wide range of industries including insurance, communication, cable television, newspaper and magazine publishing, banking, and newly competitive utility markets.

The basis of survival data mining is hazard probability: that is, the chance that someone who has survived for a certain length of time (called customer 'tenure') is going to stop, cancel, or expire before the next unit of time. This definition assumes that time is discrete, and such discrete time intervals � days, weeks, or months � fit business needs. By contrast, traditional survival analysis in statistics usually assumes that time is continuous.