GATHERING INFORMATION FROM NEW CUSTOMERS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

When a prospect first becomes a customer, there is a golden opportunity to gather more information. Before the transformation from prospect to cus­ tomer, any data about prospects tends to be geographic and demographic. Purchased lists are unlikely to provide anything beyond name, contact infor­ mation, and list source. When an address is available, it is possible to infer other things about prospects based on characteristics of their neighborhoods. Name and address together can be used to purchase household-level informa­ tion about prospects from providers of marketing data. This sort of data is use­ ful for targeting broad, general segments such as “young mothers” or “urban teenagers” but is not detailed enough to form the basis of an individualized customer relationship.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Among the most useful fields that can be collected for future data mining are the initial purchase date, initial acquisition channel, offer responded to, ini­ tial product, initial credit score, time to respond, and geographic location. We have found these fields to be predictive a wide range of outcomes of interest such as expected duration of the relationship, bad debt, and additional purchases. These initial values should be maintained as is, rather than being overwritten with new values as the customer relationship develops. Acquisition-Time Variables Can Predict Future Outcomes By recording everything that was known about a customer at the time of acquisition and then tracking customers over time, businesses can use data mining to relate acquisition-time variables to future outcomes such as cus­ tomer longevity, customer value, and default risk.