information than simply whether the customer is expected to leave within 90 days. Having an estimate of remaining customer tenure is a necessary ingredient for a customer lifetime value model. It can also be the basis for a customer loyalty score that defines a loyal customer as one who will remain for a long time in the future rather than one who has remained a long time up until now.

One approach to modeling customer longevity would be to take a snapshot of the current customer population, along with data on what these customers looked like when they were first acquired, and try to estimate customer tenure directly by trying to determine what long-lived customers have in common besides an early acquisition date. The problem with this approach, is that the longer customers have been around, the more different market conditions were back when they were acquired. Certainly it is not safe to assume that the characteristics of someone who got a cellular subscription in 1990 are good predictors of which of todays new customers will keep their service for many years.

A better approach is to use survival analysis techniques that have been borrowed and adapted from statistics. These techniques are associated with the medical world where they are used to study patient survival rates after medical interventions and the manufacturing world where they are used to study the expected time to failure of manufactured components.

Survival analysis is explained in Chapter 12. The basic idea is to calculate for each customer (or for each group of customers that share the same values for model input variables such as geography, credit class, and acquisition channel) the probability that having made it as far as today, he or she will leave before tomorrow. For any one tenure this hazard, as it is called, is quite small, but it is higher for some tenures than for others. The chance that a customer will survive to reach some more distant future date can be calculated from the intervening hazards.

Lessons Learned

The data mining techniques described in this book have applications in fields as diverse as biotechnology research and manufacturing process control. This book, however, is written for people who, like the authors, will be applying these techniques to the kinds of business problems that arise in marketing and customer relationship management. In most of the book, the focus on customer-centric applications is implicit in the choice of examples used to illustrate the techniques. In this chapter, that focus is more explicit.

Data mining is used in support of both advertising and direct marketing to identify the right audience, choose the best communications channels, and pick the most appropriate messages. Prospective customers can be compared to a profile of the intended audience and given a fitness score. Should information on individual prospects not be available, the same method can be used

to assign fitness scores to geographic neighborhoods using data of the type available form the U.S. census bureau, Statistics Canada, and similar official sources in many countries.

A common application of data mining in direct modeling is response modeling. A response model scores prospects on their likelihood to respond to a direct marketing campaign. This information can be used to improve the response rate of a campaign, but is not, by itself, enough to determine campaign profitability. Estimating campaign profitability requires reliance on estimates of the underlying response rate to a future campaign, estimates of average order sizes associated with the response, and cost estimates for fulfillment and for the campaign itself. A more customer-centric use of response scores is to choose the best campaign for each customer from among a number of competing campaigns. This approach avoids the usual problem of independent, score-based campaigns, which tend to pick the same people every time.

It is important to distinguish between the ability of a model to recognize people who are interested in a product or service and its ability to recognize people who are moved to make a purchase based on a particular campaign or offer. Differential response analysis offers a way to identify the market segments where a campaign will have the greatest impact. Differential response models seek to maximize the difference in response between a treated group and a control group rather than trying to maximize the response itself.

Information about current customers can be used to identify likely prospects by finding predictors of desired outcomes in the information that was known about current customers before they became customers. This sort of analysis is valuable for selecting acquisition channels and contact strategies as well as for screening prospect lists. Companies can increase the value of their customer data by beginning to track customers from their first response, even before they become customers, and gathering and storing additional information when customers are acquired.

Once customers have been acquired, the focus shifts to customer relationship management. The data available for active customers is richer than that available for prospects and, because it is behavioral in nature rather than simply geographic and demographic, it is more predictive. Data mining is used to identify additional products and services that should be offered to customers based on their current usage patterns. It can also suggest the best time to make a cross-sell or up-sell offer.

One of the goals of a customer relationship management program is to retain valuable customers. Data mining can help identify which customers are the most valuable and evaluate the risk of voluntary or involuntary churn associated with each customer. Armed with this information, companies can target retention offers at customers who are both valuable and at risk, and take steps to protect themselves from customers who are likely to default.

From a data mining perspective, churn modeling can be approached as either a binary-outcome prediction problem or through survival analysis. There are advantages and disadvantages to both approaches. The binary outcome approach works well for a short horizon, while the survival analysis approach can be used to make forecasts far into the future and provides insight into customer loyalty and customer value as well.

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