customers should get the incentive, which customers will stay without the incentive, and which customers should be allowed to walk.

Weeding out Bad Customers

In many industries, some customers cost more than they are worth. These might be people who consume a lot of customer support resources without buying much. Or, they might be those annoying folks who carry a credit card they rarely use, are sure to pay off the full balance when they do, but must still be mailed a statement every month. Even worse, they might be people who owe you a lot of money when they declare bankruptcy.

The same data mining techniques that are used to spot the most valuable customers can also be used to pick out those who should be turned down for a loan, those who should be allowed to wait on hold the longest time, and those who should always be assigned a middle seat near the engine (or is that just our paranoia showing?).

Revolutionizing an Industry

In 1988, the idea that a credit card issuers most valuable asset is the information it has about its customers was pretty revolutionary. It was an idea that Richard Fairbank and Nigel Morris shopped around to 25 banks before Signet Banking Corporation decided to give it a try.

Signet acquired behavioral data from many sources and used it to build predictive models. Using these models, it launched the highly successful balance transfer program that changed the way the credit card industry works. In 1994, Signet spun off the card operation as Capital One, which is now one of the top 10 credit card issuers. The same aggressive use of data mining technology that fueled such rapid growth is also responsible for keeping Capital Ones loan loss rates among the lowest in the industry. Data mining is now at the heart of the marketing strategy of all the major credit card issuers.

Credit card divisions may have led the charge of banks into data mining, but other divisions are not far behind. At Wachovia, a large North Carolina-based bank, data mining techniques are used to predict which customers are likely to be moving soon. For most people, moving to a new home in another town means closing the old bank account and opening a new one, often with a different company. Wachovia set out to improve retention by identifying customers who are about to move and making it easy for them to transfer their business to another Wachovia branch in the new location. Not only has retention improved markedly, but also a profitable relocation business has developed. In addition to setting up a bank account, Wachovia now arranges for gas, electricity, and other services at the new location.

And Just about Anything Else

These applications should give you a feel for what is possible using data mining, but they do not come close to covering the full range of applications. The data mining techniques described in this book have been used to find quasars, design army uniforms, detect second-press olive oil masquerading as extra virgin, teach machines to read aloud, and recognize handwritten letters. They will, no doubt, be used to do many of the things your business will require to grow and prosper for the rest of the century. In the next chapter, we turn to how businesses make effective use of data mining, using the virtuous cycle of data mining.

Lessons Learned

Data Mining is an important component of analytic customer relationship management. The goal of analytic customer relationship management is to recreate, to the extent possible, the intimate, learning relationship that a well-run small business enjoys with its customers. A companys interactions with its customers generates large volumes of data. This data is initially captured in transaction processing systems such as automatic teller machines, telephone switch records, and supermarket scanner files. The data can then be collected, cleaned, and summarized for inclusion in a customer data warehouse. A well-designed customer data warehouse contains a historical record of customer interactions that becomes the memory of the corporation. Data mining tools can be applied to this historical record to learn things about customers that will allow the company to serve them better in the future. The chapter presented several examples of commercial applications of data mining such as better targeted couponing, making recommendations, cross selling, customer retention, and credit risk reduction.

Data mining itself is the process of finding useful patterns and rules in large volumes of data. This chapter introduced and defined six common data mining tasks: classification, estimation, prediction, affinity grouping, clustering, and profiling. The remainder of the book examines a variety of data mining algorithms and techniques that can be applied to these six tasks. To be successful, these techniques must become integral parts of a larger business process. That integration is the subject of the next chapter, The Virtuous Cycle of Data Mining.