the right questions, and making predictions about the future. This book describes tools and techniques that add intelligence to the data warehouse. These techniques help make it possible to exploit the vast mountains of data generated by interactions with customers and prospects in order to get to know them better.

Who is likely to remain a loyal customer and who is likely to jump ship? What products should be marketed to which prospects? What determines whether a person will respond to a certain offer? Which telemarketing script is best for this call? Where should the next branch be located? What is the next product or service this customer will want? Answers to questions like these lie buried in corporate data. It takes powerful data mining tools to get at them.

The central idea of data mining for customer relationship management is that data from the past contains information that will be useful in the future. It works because customer behaviors captured in corporate data are not random, but reflect the differing needs, preferences, propensities, and treatments of customers. The goal of data mining is to find patterns in historical data that shed light on those needs, preferences, and propensities. The task is made difficult by the fact that the patterns are not always strong, and the signals sent by customers are noisy and confusing. Separating signal from noise-recognizing the fundamental patterns beneath seemingly random variations-is an important role of data mining.

This book covers all the most important data mining techniques and the strengths and weaknesses of each in the context of customer relationship management.

The Role of the Customer Relationship Management Strategy

To be effective, data mining must occur within a context that allows an organization to change its behavior as a result of what it learns. It is no use knowing that wireless telephone customers who are on the wrong rate plan are likely to cancel their subscriptions if there is no one empowered to propose that they switch to a more appropriate plan as suggested in the sidebar. Data mining should be embedded in a corporate customer relationship strategy that spells out the actions to be taken as a result of what is learned through data mining. When low-value customers are identified, how will they be treated? Are there programs in place to stimulate their usage to increase their value? Or does it make more sense to lower the cost of serving them? If some channels consistently bring in more profitable customers, how can resources be shifted to those channels?

Data mining is a tool. As with any tool, it is not sufficient to understand how it works; it is necessary to understand how it will be used.

DATA MINING SUGGESTS, BUSINESSES DECIDE

This sidebar explores the example from the main text in slightly more detail. An analysis of attrition at a wireless telephone service provider often reveals that people whose calling patterns do not match their rate plan are more likely to cancel their subscriptions. People who use more than the number of minutes included in their plan are charged for the extra minutes-often at a high rate. People who do not use their full allotment of minutes are paying for minutes they do not use and are likely to be attracted to a competitors offer of a cheaper plan.

This result suggests doing something proactive to move customers to the right rate plan. But this is not a simple decision. As long as they dont quit, customers on the wrong rate plan are more profitable if left alone. Further analysis may be needed. Perhaps there is a subset of these customers who are not price sensitive and can be safely left alone. Perhaps any intervention will simply hand customers an opportunity to cancel. Perhaps a small rightsizing test can help resolve these issues. Data mining can help make more informed decisions. It can suggest tests to make. Ultimately, though, the business needs to make the decision.

What Is Data Mining?

Data mining, as we use the term, is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. For the purposes of this book, we assume that the goal of data mining is to allow a corporation to improve its marketing, sales, and customer support operations through a better understanding of its customers. Keep in mind, however, that the data mining techniques and tools described here are equally applicable in fields ranging from law enforcement to radio astronomy, medicine, and industrial process control.

In fact, hardly any of the data mining algorithms were first invented with commercial applications in mind. The commercial data miner employs a grab bag of techniques borrowed from statistics, computer science, and machine learning research. The choice of a particular combination of techniques to apply in a particular situation depends on the nature of the data mining task, the nature of the available data, and the skills and preferences of the data miner.

Data mining comes in two flavors-directed and undirected. Directed data mining attempts to explain or categorize some particular target field such as income or response. Undirected data mining attempts to find patterns or similarities among groups of records without the use of a particular target field or collection of predefined classes. Both these flavors are discussed in later chapters.

Data mining is largely concerned with building models. A model is simply an algorithm or set of rules that connects a collection of inputs (often in the form of fields in a corporate database) to a particular target or outcome. Regression, neural networks, decision trees, and most of the other data mining techniques discussed in this book are techniques for creating models. Under the right circumstances, a model can result in insight by providing an explanation of how outcomes of particular interest, such as placing an order or failing to pay a bill, are related to and predicted by the available facts. Models are also used to produce scores. A score is a way of expressing the findings of a model in a single number. Scores can be used to sort a list of customers from most to least loyal or most to least likely to respond or most to least likely to default on a loan.

The data mining process is sometimes referred to as knowledge discovery or KDD (knowledge discovery in databases). We prefer to think of it as knowledge creation.

What Tasks Can Be Performed with Data Mining?

Many problems of intellectual, economic, and business interest can be phrased in terms of the following six tasks:

Classification

Estimation

Prediction

Affinity grouping

Clustering

Description and profiling

The first three are all examples of directed data mining, where the goal is to find the value of a particular target variable. Affinity grouping and clustering are undirected tasks where the goal is to uncover structure in data without respect to a particular target variable. Profiling is a descriptive task that may be either directed or undirected.

Classification

Classification, one of the most common data mining tasks, seems to be a human imperative. In order to understand and communicate about the world, we are constantly classifying, categorizing, and grading. We divide living things into phyla, species, and general; matter into elements; dogs into breeds; people into races; steaks and maple syrup into USDA grades.