The leading introductory book on data mining, fully updated and
revised!

When Berry and Linoff wrote the first edition of Data Mining
Techniques in the late 1990s, data mining was just starting to
move out of the lab and into the office and has since grown to
become an indispensable tool of modern business. This new
edition--more than 50% new and revised-- is a
significant update from the previous one, and shows you how to
harness the newest data mining methods and techniques to solve
common business problems. The duo of unparalleled authors share
invaluable advice for improving response rates to direct marketing
campaigns, identifying new customer segments, and estimating credit
risk. In addition, they cover more advanced topics such as
preparing data for analysis and creating the necessary
infrastructure for data mining at your company.

* Features significant updates since the previous edition and
updates you on best practices for using data mining methods and
techniques for solving common business problems

* Covers a new data mining technique in every chapter along with
clear, concise explanations on how to apply each technique
immediately

* Touches on core data mining techniques, including decision
trees, neural networks, collaborative filtering, association rules,
link analysis, survival analysis, and more

* Provides best practices for performing data mining using simple
tools such as Excel

Data Mining Techniques, Third Edition covers a new data
mining technique with each successive chapter and then demonstrates
how you can apply that technique for improved marketing, sales, and
customer support to get immediate results.

GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management.

When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new editionmore than 50% new and revised is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.

• Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems
• Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately
• Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
• Provides best practices for performing data mining using simple tools such as Excel

Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

Introduction xxxvii

Chapter 1 What Is Data Mining and Why Do It? 1

What Is Data Mining? 2

Data Mining Is a Business Process 2

Large Amounts of Data 3

Meaningful Patterns and Rules 3

Data Mining and Customer Relationship Management 4

Why Now? 6

Data Is Being Produced 6

Data Is Being Warehoused 6

Computing Power Is Affordable 7

Interest in Customer Relationship Management Is Strong 7

Commercial Data Mining Software Products Have Become Available 8

Skills for the Data Miner 9

The Virtuous Cycle of Data Mining 9

A Case Study in Business Data Mining 11

Identifying BofA's Business Challenge 12

Applying Data Mining 12

Acting on the Results 13

Measuring the Effects of Data Mining 14

Steps of the Virtuous Cycle 15

Identify Business Opportunities 16

Transform Data into Information 17

Act on the Information 19

Measure the Results 20

Data Mining in the Context of the Virtuous Cycle 23

Lessons Learned 26

Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management 27

Two Customer Lifecycles 27

The Customer's Lifecycle 28

The Customer Lifecycle 28

Subscription Relationships versus Event-Based Relationships 30

Organize Business Processes Around the Customer Lifecycle 32

Customer Acquisition 33

Customer Activation 36

Customer Relationship Management 37

Winback 38

Data Mining Applications for Customer Acquisition 38

Identifying Good Prospects 39

Choosing a Communication Channel 39

Picking Appropriate Messages 40

A Data Mining Example: Choosing the Right Place to Advertise 40

Who Fits the Profile? 41

Measuring Fitness for Groups of Readers 44

Data Mining to Improve Direct Marketing Campaigns 45

Response Modeling 46

Optimizing Response for a Fixed Budget 47

Optimizing Campaign Profitability 49

Reaching the People Most Influenced by the Message 53

Using Current Customers to Learn About Prospects 54

Start Tracking Customers Before They Become Customers 55

Gather Information from New Customers 55

Acquisition-Time Variables Can Predict Future Outcomes 56

Data Mining Applications for Customer Relationship Management 56

Matching Campaigns to Customers 56

Reducing Exposure to Credit Risk 58

Determining Customer Value 59

Cross-selling, Up-selling, and Making Recommendations 60

Retention 60

Recognizing Attrition 60

Why Attrition Matters 61

Different Kinds of Attrition 62

Different Kinds of Attrition Model 63

Beyond the Customer Lifecycle 64

Lessons Learned 65

Chapter 3 The Data Mining Process 67

What Can Go Wrong? 68

Learning Things That Aren't True 68

Learning Things That Are True, but Not Useful 73

Data Mining Styles 74

Hypothesis Testing 75

Directed Data Mining 81

Undirected Data Mining 81

Goals, Tasks, and Techniques 82

Data Mining Business Goals 82

Data Mining Tasks 83

Data Mining Techniques 88

Formulating Data Mining Problems: From Goals to Tasks to Techniques 88

What Techniques for Which Tasks? 95

Is There a Target or Targets? 96

What Is the Target Data Like? 96

What Is the Input Data Like? 96

How Important Is Ease of Use? 97

How Important Is Model Explicability? 97

Lessons Learned 98

Chapter 4 Statistics 101: What You Should Know About Data 101

Occam's Razor 103

Skepticism and Simpson's Paradox 103

The Null Hypothesis 104

P-Values 105

Loo...