Nonrepayment of debt is one obvious example; newspapers subscriptions, telephone service, gas and electricity, and cable service are among the many services that are usually paid for only after they have been used.

Of course, customers who fail to pay for long enough are eventually cut off. By that time they may owe large sums of money that must be written off. With early warning from a predictive model, a company can take steps to protect itself. These steps might include limiting access to the service or decreasing the length of time between a payment being late and the service being cut off.

Involuntary churn, as termination of services for nonpayment is sometimes called, can be modeled in multiple ways. Often, involuntary churn is considered as a binary outcome in some fixed amount of time, in which case techniques such as logistic regression and decision trees are appropriate. Chapter 12 shows how this problem can also be viewed as a survival analysis problem, in effect changing the question from Will the customer fail to pay next month? to How long will it be until half the customers have been lost to involuntary churn?

One of the big differences between voluntary churn and involuntary churn is that involuntary churn often involves complicated business processes, as bills go through different stages of being late. Over time, companies may tweak the rules that guide the processes to control the amount of money that they are owed. When looking for accurate numbers in the near term, modeling each step in the business processes may be the best approach.

Improving Collections

Once customers have stopped paying, data mining can aid in collections. Models are used to forecast the amount that can be collected and, in some cases, to help choose the collection strategy. Collections is basically a type of sales. The company tries to sell its delinquent customers on the idea of paying its bills instead of some other bill. As with any sales campaign, some prospective payers will be more receptive to one type of message and some to another.

Determining Customer Value

Customer value calculations are quite complex and although data mining has a role to play, customer value calculations are largely a matter of getting financial definitions right. A seemingly simple statement of customer value is the total revenue due to the customer minus the total cost of maintaining the customer. But how much revenue should be attributed to a customer? Is it what he or she has spent in total to date? What he or she spent this month? What we expect him or her to spend over the next year? How should indirect revenues such as advertising revenue and list rental be allocated to customers?

Costs are even more problematic. Businesses have all sorts of costs that may be allocated to customers in peculiar ways. Even ignoring allocated costs and looking only at direct costs, things can still be pretty confusing. Is it fair to blame customers for costs over which they have no control? Two Web customers order the exact same merchandise and both are promised free delivery. The one that lives farther from the warehouse may cost more in shipping, but is she really a less valuable customer? What if the next order ships from a different location? Mobile phone service providers are faced with a similar problem. Most now advertise uniform nationwide rates. The providers costs are far from uniform when they do not own the entire network. Some of the calls travel over the companys own network. Others travel over the networks of competitors who charge high rates. Can the company increase customer value by trying to discourage customers from visiting certain geographic areas?

Once all of these problems have been sorted out, and a company has agreed on a definition of retrospective customer value, data mining comes into play in order to estimate prospective customer value. This comes down to estimating the revenue a customer will bring in per unit time and then estimating the customers remaining lifetime. The second of these problems is the subject of Chapter 12.

Cross-selling, Up-selling, and Making Recommendations

With existing customers, a major focus of customer relationship management is increasing customer profitability through cross-selling and up-selling. Data mining is used for figuring out what to offer to whom and when to offer it.

Finding the Right Time for an Offer

Charles Schwab, the investment company, discovered that customers generally open accounts with a few thousand dollars even if they have considerably more stashed away in savings and investment accounts. Naturally, Schwab would like to attract some of those other balances. By analyzing historical data, they discovered that customers who transferred large balances into investment accounts usually did so during the first few months after they opened their first account. After a few months, there was little return on trying to get customers to move in large balances. The window was closed. As a results of learning this, Schwab shifted its strategy from sending a constant stream of solicitations throughout the customer life cycle to concentrated efforts during the first few months.

A major newspaper with both daily and Sunday subscriptions noticed a similar pattern. If a Sunday subscriber upgrades to daily and Sunday, it usually happens early in the relationship. A customer who has been happy with just the Sunday paper for years is much less likely to change his or her habits.

Making Recommendations

One approach to cross-selling makes use of association rules, the subject of Chapter 9. Association rules are used to find clusters of products that usually sell together or tend to be purchased by the same person over time. Customers who have purchased some, but not all of the members of a cluster are good prospects for the missing elements. This approach works for retail products where there are many such clusters to be found, but is less effective in areas such as financial services where there are fewer products and many customers have a similar mix, and the mix is often determined by product bundling and previous marketing efforts.

Retention and Churn

Customer attrition is an important issue for any company, and it is especially important in mature industries where the initial period of exponential growth has been left behind. Not surprisingly, churn (or, to look on the bright side, retention) is a major application of data mining. We use the term churn as it is generally used in the telephone industry to refer to all types of customer attrition whether voluntary or involuntary; churn is a useful word because it is one syllable and easily used as both a noun and a verb.

Recognizing Churn

One of the first challenges in modeling churn is deciding what it is and recognizing when it has occurred. This is harder in some industries than in others. At one extreme are businesses that deal in anonymous cash transactions. When a once loyal customer deserts his regular coffee bar for another down the block, the barista who knew the customers order by heart may notice, but the fact will not be recorded in any corporate database. Even in cases where the customer is identified by name, it may be hard to tell the difference between a customer who has churned and one who just hasnt been around for a while. If a loyal Ford customer who buys a new F150 pickup every 5 years hasnt bought one for 6 years, can we conclude that he has defected to another brand?

Churn is a bit easier to spot when there is a monthly billing relationship, as with credit cards. Even there, however, attrition might be silent. A customer stops using the credit card, but doesnt actually cancel it. Churn is easiest to define in subscription-based businesses, and partly for that reason, churn modeling is most popular in these businesses. Long-distance companies, mobile phone service providers, insurance companies, cable companies, financial services companies, Internet service providers, newspapers, magazines,