THE QUANTITATIVE EFFECT OF MARKETING FACTORS
 

 

 

 

 

 

 

 

 

 

 

 

 

 

In the world of customers, tens of thousands is the lower limit, since cus­ tomer databases often contain data on millions of customers and former customers. Much of the statistical background of survival analysis is focused on extracting every last bit of information out of a few hundred data points. In data mining applications, the volumes of data are so large that statistical con­ cerns about confidence and accuracy are replaced by concerns about managing large volumes of data. The importance of survival analysis is that it provides a way of understand­ ing time-to-event characteristics, such as: When a customer is likely to leave The next time a customer is likely to migrate to a new customer segment The next time a customer is likely to broaden or narrow the customer relationship The factors in the customer relationship that increase or decrease likely tenure The quantitative effect of various factors on customer tenure

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

These insights into customers feed directly into the marketing process. They make it possible to understand how long different groups of customers are likely to be around—and hence how profitable these segments are likely to be. They make it possible to forecast numbers of customers, taking into account both new acquisition and the decline of the current base. Survival analysis also makes it possible to determine which factors, both those at the beginning of customers’ relationships as well as later experiences, have the biggest effect on customers’ staying around the longest. And, the analysis can be applied to things other then the end of the customer tenure, making it possible to deter­ mine when another event—such as a customer returning to a Web site—is no longer likely to occur. A good place to start with survival is with visualizing customer retention, which is a rough approximation of survival. After this discussion, we move on to hazards, the building blocks of survival. These are in turn combined into Hazard Functions and Survival Analysis in Marketing survival curves, which are similar to retention curves but more useful. The chapter ends with a discussion of Cox Proportional Hazard Regression and other applications of survival analysis. Along the way, the chapter provides particular applications of survival in the business context. As with all statisti­ cal methods, there is a depth to survival that goes far beyond this introductory chapter, which is consciously trying to avoid the complex mathematics under­ lying these techniques.