Examples of Behavior-Based Variables 575

Frequency of Purchase 575

Declining Usage 577 Revolvers, Transactors, and Convenience Users:

Defining Customer Behavior 580

Data 581

Segmenting by Estimating Revenue 581

Segmentation by Potential 583

Customer Behavior by Comparison to Ideals 585

The Ideal Convenience User 587

The Dark Side of Data 590

Missing Values 590

Dirty Data 592

Inconsistent Values 593

Computational Issues 594

Source Systems 594

Extraction Tools 595

Special-Purpose Code 595

Data Mining Tools 595

Lessons Learned 596

Chapter 18 Putting Data Mining to Work 597

Getting Started 598

What to Expect from a Proof-of-Concept Project 599

Identifying a Proof-of-Concept Project 599

Implementing the Proof-of-Concept Project 601

Act on Your Findings 602

Measure the Results of the Actions 603

Choosing a Data Mining Technique 605

Formulate the Business Goal as a Data Mining Task 605

Determine the Relevant Characteristics of the Data 606

Data Type 606

Number of Input Fields 607

Free-Form Text 607

Consider Hybrid Approaches 608

How One Company Began Data Mining 608

A Controlled Experiment in Retention 609

The Data 611

The Findings 613

The Proof of the Pudding 614

Lessons Learned 614

Index 615

Why and What Is Data Mining?

In the first edition of this book, the first sentence of the first chapter began with the words Somerville, Massachusetts, home to one of the authors of this book, . . . and went on to tell of two small businesses in that town and how they had formed learning relationships with their customers. In the intervening years, the little girl whose relationship with her hair braider was described in the chapter has grown up and moved away and no longer wears her hair in corn-rows. Her father has moved to nearby Cambridge. But one thing has not changed. The author is still a loyal customer of the Wine Cask, where some of the same people who first introduced him to cheap Algerian reds in 1978 and later to the wine-growing regions of France are now helping him to explore Italy and Germany.

After a quarter of a century, they still have a loyal customer. That loyalty is no accident. Dan and Steve at the Wine Cask learn the tastes of their customers and their price ranges. When asked for advice, their response will be based on their accumulated knowledge of that customers tastes and budgets as well as on their knowledge of their stock.

The people at The Wine Cask know a lot about wine. Although that knowledge is one reason to shop there rather than at a big discount liquor store, it is their intimate knowledge of each customer that keeps people coming back. Another wine shop could open across the street and hire a staff of expert oenophiles, but it would take them months or years to achieve the same level of customer knowledge.

Well-run small businesses naturally form learning relationships with their customers. Over time, they learn more and more about their customers, and they use that knowledge to serve them better. The result is happy, loyal customers and profitable businesses. Larger companies, with hundreds of thousands or millions of customers, do not enjoy the luxury of actual personal relationships with each one. These larger firms must rely on other means to form learning relationships with their customers. In particular, they must learn to take full advantage of something they have in abundance-the data produced by nearly every customer interaction. This book is about analytic techniques that can be used to turn customer data into customer knowledge.

Analytic Customer Relationship Management

It is widely recognized that firms of all sizes need to learn to emulate what small, service-oriented businesses have always done well-creating one-to-one relationships with their customers. Customer relationship management is a broad topic that is the subject of many books and conferences. Everything from lead-tracking software to campaign management software to call center software is now marketed as a customer relationship management tool. The focus of this book is narrower-the role that data mining can play in improving customer relationship management by improving the firms ability to form learning relationships with its customers.

In every industry, forward-looking companies are moving toward the goal of understanding each customer individually and using that understanding to make it easier for the customer to do business with them rather than with competitors. These same firms are learning to look at the value of each customer so that they know which ones are worth investing money and effort to hold on to and which ones should be allowed to depart. This change in focus from broad market segments to individual customers requires changes throughout the enterprise, and nowhere more than in marketing, sales, and customer support.

Building a business around the customer relationship is a revolutionary change for most companies. Banks have traditionally focused on maintaining the spread between the rate they pay to bring money in and the rate they charge to lend money out. Telephone companies have concentrated on connecting calls through the network. Insurance companies have focused on processing claims and managing investments. It takes more than data mining to turn a product-focused organization into a customer-centric one. A data mining result that suggests offering a particular customer a widget instead of a gizmo will be ignored if the managers bonus depends on the number of gizmos sold this quarter and not on the number of widgets (even if the latter are more profitable).

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