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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.
Autorentext
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.
Zusammenfassung
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 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.
Inhalt
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...