Data Mining Techniques For Marketing, Sales, and Customer Relationship Management (Second Edition)
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☆☆☆☆☆
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【下载次数】 |
80 次 |
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【作者】 |
Michael J.A. Berry & Gordon S. Linoff
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【出版社】 |
Wiley publishing,Inc.
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【文件格式】 |
PDF
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【ISBN】 |
0-471-47064-3
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【资料语言】 |
英文
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【文件大小】 |
13.65MB
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【上传时间】 |
2008-07-15
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【共享者】 |
gj05245515
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资料说明：
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About the Authors
Michael J. A. Berry and Gordon S. Linoff are well known in the data mining
field. They have jointly authored three influential and widely read books on
data mining that have been translated into many languages. They each have
close to two decades of experience applying data mining techniques to business
problems in marketing and customer relationship management.
Michael and Gordon first worked together during the 1980s at Thinking
Machines Corporation, which was a pioneer in mining large databases. In
1996, they collaborated on a data mining seminar, which soon evolved into the
first edition of this book. The success of that collaboration gave them the
courage to start Data Miners, Inc., a respected data mining consultancy, in
1998. As data mining consultants, they have worked with a wide variety of
major companies in North America, Europe, and Asia, turning customer databases,
call detail records, Web log entries, point-of-sale records, and billing
files into useful information that can be used to improve the customer experience.
The authors’ years of hands-on data mining experience are reflected in
every chapter of this extensively updated and revised edition of their first
book, Data Mining Techniques.
When not mining data at some distant client site, Michael lives in Cambridge,
Massachusetts, and Gordon lives in New York City.
xxi
Introduction
The first edition of Data Mining Techniques for Marketing, Sales, and Customer
Support appeared on book shelves in 1997. The book actually got its start in
1996 as Gordon and I were developing a 1-day data mining seminar for
NationsBank (now Bank of America). Sue Osterfelt, a vice president at
NationsBank and the author of a book on database applications with Bill
Inmon, convinced us that our seminar material ought to be developed into a
book. She introduced us to Bob Elliott, her editor at John Wiley & Sons, and
before we had time to think better of it, we signed a contract.
Neither of us had written a book before, and drafts of early chapters clearly
showed this. Thanks to Bob’s help, though, we made a lot of progress, and the
final product was a book we are still proud of. It is no exaggeration to say that
the experience changed our lives — first by taking over every waking hour
and some when we should have been sleeping; then, more positively, by providing
the basis for the consulting company we founded, Data Miners, Inc.
The first book, which has become a standard text in data mining, was followed
by others, Mastering Data Mining and Mining the Web.
So, why a revised edition? The world of data mining has changed a lot since
we starting writing in 1996. For instance, back then, Amazon.com was still
new; U.S. mobile phone calls cost on average 56 cents per minute, and fewer
than 25 percent of Americans even owned a mobile phone; and the KDD data
mining conference was in its second year. Our understanding has changed
even more. For the most part, the underlying algorithms remain the same,
although the software in which the algorithms are imbedded, the data to
which they are applied, and the business problems they are used to solve have
all grown and evolved.
xxiii
xxiv Introduction
Even if the technological and business worlds had stood still, we would
have wanted to update Data Mining Techniques because we have learned so
much in the intervening years. One of the joys of consulting is the constant
exposure to new ideas, new problems, and new solutions. We may not be any
smarter than when we wrote the first edition, but we do have more experience
and that added experience has changed the way we approach the material. A
glance at the Table of Contents may suggest that we have reduced the amount
of business-related material and increased the amount of technical material.
Instead, we have folded some of the business material into the technical chapters
so that the data mining techniques are introduced in their business context.
We hope this makes it easier for readers to see how to apply the
techniques to their own business problems.
It has also come to our attention that a number of business school courses
have used this book as a text. Although we did not write the book as a text, in
the second edition we have tried to facilitate its use as one by using more
examples based on publicly available data, such as the U.S. census, and by
making some recommended reading and suggested exercises available at the
companion Web site, www.data-miners.com/companion.
The book is still divided into three parts. The first part talks about the business
context of data mining, starting with a chapter that introduces data mining
and explains what it is used for and why. The second chapter introduces
the virtuous cycle of data mining — the ongoing process by which data mining
is used to turn data into information that leads to actions, which in turn
create more data and more opportunities for learning. Chapter 3 is a muchexpanded
discussion of data mining methodology and best practices. This
chapter benefits more than any other from our experience since writing the
first book. The methodology introduced here is designed to build on the successful
engagements we have been involved in. Chapter 4, which has no counterpart
in the first edition, is about applications of data mining in marketing
and customer relationship management, the fields where most of our own
work has been done.
The second part consists of the technical chapters about the data mining
techniques themselves. All of the techniques described in the first edition are
still here although they are presented in a different order. The descriptions
have been rewritten to make them clearer and more accurate while still retaining
nontechnical language wherever possible.
In addition to the seven techniques covered in the first edition — decision
trees, neural networks, memory-based reasoning, association rules, cluster
detection, link analysis, and genetic algorithms — there is now a chapter on
data mining using basic statistical techniques and another new chapter on survival
analysis. Survival analysis is a technique that has been adapted from the
small samples and continuous time measurements of the medical world to the
Introduction xxv
large samples and discrete time measurements found in marketing data. The
chapter on memory-based reasoning now also includes a discussion of collaborative
filtering, another technique based on nearest neighbors that has
become popular with Web retailers as a way of generating recommendations.
The third part of the book talks about applying the techniques in a business
context, including a chapter on finding customers in data, one on the relationship
of data mining and data warehousing, another on the data mining environment
(both corporate and technical), and a final chapter on putting data
mining to work in an organization. A new chapter in this part covers preparing
data for data mining, an extremely important topic since most data miners
report that transforming data takes up the majority of time in a typical data
mining project.
Like the first edition, this book is aimed at current and future data mining
practitioners. It is not meant for software developers looking for detailed
instructions on how to implement the various data mining algorithms nor for
researchers trying to improve upon those algorithms. Ideas are presented in
nontechnical language with minimal use of mathematical formulas and arcane
jargon. Each data mining technique is shown in a real business context with
examples of its use taken from real data mining engagements. In short, we
have tried to write the book that we would have liked to read when we began
our own data mining careers.
— Michael J. A. Berry, October, 2003
Acknowledgments xix
About the Authors xxi
Introduction xxiii
Chapter 1 Why and What Is Data Mining? 1
Analytic Customer Relationship Management 2
The Role of Transaction Processing Systems 3
The Role of Data Warehousing 4
The Role of Data Mining 5
The Role of the Customer Relationship Management Strategy 6
What Is Data Mining? 7
What Tasks Can Be Performed with Data Mining? 8
Classification 8
Estimation 9
Prediction 10
Affinity Grouping or Association Rules 11
Clustering 11
Profiling 12
Why Now? 12
Data Is Being Produced 12
Data Is Being Warehoused 13
Computing Power Is Affordable 13
Interest in Customer Relationship Management Is Strong 13
Every Business Is a Service Business 14
Information Is a Product 14
Commercial Data Mining Software Products
Have Become Available 15
v
vi Contents
How Data Mining Is Being Used Today 15
A Supermarket Becomes an Information Broker 15
A Recommendation-Based Business 16
Cross-Selling 17
Holding on to Good Customers 17
Weeding out Bad Customers 18
Revolutionizing an Industry 18
And Just about Anything Else 19
Lessons Learned 19
Chapter 2 The Virtuous Cycle of Data Mining 21
A Case Study in Business Data Mining 22
Identifying the Business Challenge 23
Applying Data Mining 24
Acting on the Results 25
Measuring the Effects 25
What Is the Virtuous Cycle? 26
Identify the Business Opportunity 27
Mining Data 28
Take Action 30
Measuring Results 30
Data Mining in the Context of the Virtuous Cycle 32
A Wireless Communications Company Makes
the Right Connections 34
The Opportunity 34
How Data Mining Was Applied 35
Defining the Inputs 37
Derived Inputs 37
The Actions 38
Completing the Cycle 39
Neural Networks and Decision Trees Drive SUV Sales 39
The Initial Challenge 39
How Data Mining Was Applied 40
The Data 40
Down the Mine Shaft 40
The Resulting Actions 41
Completing the Cycle 42
Lessons Learned 42
Chapter 3 Data Mining Methodology and Best Practices 43
Why Have a Methodology? 44
Learning Things That Aren’t True 44
Patterns May Not Represent Any Underlying Rule 45
The Model Set May Not Reflect the Relevant Population 46
Data May Be at the Wrong Level of Detail 47
Contents vii
Learning Things That Are True, but Not Useful
48
Learning Things That Are Already Known
49
Learning Things That Can’t Be Used
49
Hypothesis Testing
50
Generating Hypotheses
51
Testing Hypotheses
51
Models, Profiling, and Prediction
51
Profiling 53
Prediction
54
The Methodology 54
Step One: Translate the Business Problem
into a Data Mining Problem 56
What Does a Data Mining Problem Look Like? 56
How Will the Results Be Used? 57
How Will the Results Be Delivered? 58
The Role of Business Users and Information Technology 58
Step Two: Select Appropriate Data 60
What Is Available? 61
How Much Data Is Enough? 62
How Much History Is Required? 63
How Many Variables? 63
What Must the Data Contain? 64
Step Three: Get to Know the Data 64
Examine Distributions 65
Compare Values with Descriptions 66
Validate Assumptions 67
Ask Lots of Questions 67
Step Four: Create a Model Set 68
Assembling Customer Signatures 68
Creating a Balanced Sample 68
Including Multiple Timeframes 70
Creating a Model Set for Prediction 70
Partitioning the Model Set 71
Step Five: Fix Problems with the Data 72
Categorical Variables with Too Many Values 73
Numeric Variables with Skewed Distributions and Outliers 73
Missing Values 73
Values with Meanings That Change over Time 74
Inconsistent Data Encoding 74
Step Six: Transform Data to Bring Information to the Surface 74
Capture Trends 75
Create Ratios and Other Combinations of Variables 75
Convert Counts to Proportions 75
Step Seven: Build Models 77
viii Contents
Step Eight: Assess Models 78
Assessing Descriptive Models 78
Assessing Directed Models 78
Assessing Classifiers and Predictors 79
Assessing Estimators 79
Comparing Models Using Lift 81
Problems with Lift 83
Step Nine: Deploy Models 84
Step Ten: Assess Results 85
Step Eleven: Begin Again 85
Lessons Learned 86
Chapter 4 Data Mining Applications in Marketing and
Customer Relationship Management 87
Prospecting 87
Identifying Good Prospects 88
Choosing a Communication Channel 89
Picking Appropriate Messages 89
Data Mining to Choose the Right Place to Advertise 90
Who Fits the Profile? 90
Measuring Fitness for Groups of Readers 93
Data Mining to Improve Direct Marketing Campaigns 95
Response Modeling 96
Optimizing Response for a Fixed Budget 97
Optimizing Campaign Profitability 100
How the Model Affects Profitability 103
Reaching the People Most Influenced by the Message 106
Differential Response Analysis 107
Using Current Customers to Learn About Prospects 108
Start Tracking Customers before They Become Customers 109
Gather Information from New Customers 109
Acquisition-Time Variables Can Predict Future Outcomes 110
Data Mining for Customer Relationship Management 110
Matching Campaigns to Customers 110
Segmenting the Customer Base 111
Finding Behavioral Segments 111
Tying Market Research Segments to Behavioral Data 113
Reducing Exposure to Credit Risk 113
Predicting Who Will Default 113
Improving Collections 114
Determining Customer Value 114
Cross-selling, Up-selling, and Making Recommendations 115
Finding the Right Time for an Offer 115
Making Recommendations 116
Retention and Churn 116
Recognizing Churn 116
Why Churn Matters 117
Different Kinds of Churn 118
Contents ix
Different Kinds of Churn Model
119
Predicting Who Will Leave
119
Predicting How Long Customers Will Stay
119
Lessons Learned
120
Chapter 5 The Lure of Statistics: Data Mining Using Familiar Tools 123
Occam’s Razor
124
The Null Hypothesis
125
P-Values 126
A Look at Data 126
Looking at Discrete Values 127
Histograms 127
Time Series 128
Standardized Values 129
From Standardized Values to Probabilities 133
Cross-Tabulations 136
Looking at Continuous Variables 136
Statistical Measures for Continuous Variables 137
Variance and Standard Deviation 138
A Couple More Statistical Ideas 139
Measuring Response 139
Standard Error of a Proportion 139
Comparing Results Using Confidence Bounds 141
Comparing Results Using Difference of Proportions 143
Size of Sample 145
What the Confidence Interval Really Means 146
Size of Test and Control for an Experiment 147
Multiple Comparisons 148
The Confidence Level with Multiple Comparisons 148
Bonferroni’s Correction 149
Chi-Square Test 149
Expected Values 150
Chi-Square Value 151
Comparison of Chi-Square to Difference of Proportions 153
An Example: Chi-Square for Regions and Starts 155
Data Mining and Statistics 158
No Measurement Error in Basic Data 159
There Is a Lot of Data 160
Time Dependency Pops Up Everywhere 160
Experimentation is Hard 160
Data Is Censored and Truncated 161
Lessons Learned 162
Chapter 6 Decision Trees 165
What Is a Decision Tree? 166
Classification 166
Scoring 169
Estimation 170
Trees Grow in Many Forms 170
How a Decision Tree Is Grown 171
Finding the Splits 172
Splitting on a Numeric Input Variable 173
Splitting on a Categorical Input Variable 174
Splitting in the Presence of Missing Values 174
Growing the Full Tree 175
Measuring the Effectiveness Decision Tree 176
Tests for Choosing the Best Split 176
Purity and Diversity 177
Gini or Population Diversity 178
Entropy Reduction or Information Gain 179
Information Gain Ratio 180
Chi-Square Test 180
Reduction in Variance 183
F Test 183
Pruning 184
The CART Pruning Algorithm 185
Creating the Candidate Subtrees 185
Picking the Best Subtree 189
Using the Test Set to Evaluate the Final Tree 189
The C5 Pruning Algorithm 190
Pessimistic Pruning 191
Stability-Based Pruning 191
Extracting Rules from Trees 193
Taking Cost into Account 195
Further Refinements to the Decision Tree Method 195
Using More Than One Field at a Time 195
Tilting the Hyperplane 197
Neural Trees 199
Piecewise Regression Using Trees 199
Alternate Representations for Decision Trees 199
Box Diagrams 199
Tree Ring Diagrams 201
Decision Trees in Practice 203
Decision Trees as a Data Exploration Tool 203
Applying Decision-Tree Methods to Sequential Events 205
Simulating the Future 206
Case Study: Process Control in a Coffee-Roasting Plant 206
Lessons Learned 209
Chapter 7 Artificial Neural Networks 211
A Bit of History 212
Real Estate Appraisal 213
Neural Networks for Directed Data Mining 219
What Is a Neural Net? 220
What Is the Unit of a Neural Network? 222
Feed-Forward Neural Networks 226
TEAMFLY
Team-Fly?
Contents xi
How Does a Neural Network Learn Using
Back Propagation?
228
Heuristics for Using Feed-Forward,
Back Propagation Networks
231
Choosing the Training Set
232
Coverage of Values for All Features
232
Number of Features
233
Size of Training Set
234
Number of Outputs
234
Preparing the Data 235
Features with Continuous Values 235
Features with Ordered, Discrete (Integer) Values 238
Features with Categorical Values 239
Other Types of Features 241
Interpreting the Results 241
Neural Networks for Time Series 244
How to Know What Is Going on Inside a Neural Network 247
Self-Organizing Maps 249
What Is a Self-Organizing Map? 249
Example: Finding Clusters 252
Lessons Learned 254
Chapter 8 Nearest Neighbor Approaches: Memory-Based
Reasoning and Collaborative Filtering 257
Memory Based Reasoning 258
Example: Using MBR to Estimate Rents in Tuxedo, New York 259
Challenges of MBR 262
Choosing a Balanced Set of Historical Records 262
Representing the Training Data 263
Determining the Distance Function, Combination
Function, and Number of Neighbors 265
Case Study: Classifying News Stories 265
What Are the Codes? 266
Applying MBR 267
Choosing the Training Set 267
Choosing the Distance Function 267
Choosing the Combination Function 267
Choosing the Number of Neighbors 270
The Results 270
Measuring Distance 271
What Is a Distance Function? 271
Building a Distance Function One Field at a Time 274
Distance Functions for Other Data Types 277
When a Distance Metric Already Exists 278
The Combination Function: Asking the Neighbors
for the Answer 279
The Basic Approach: Democracy 279
Weighted Voting 281
xii Contents
Chapter 9
Chapter 10
Collaborative Filtering: A Nearest Neighbor Approach to
Making Recommendations 282
Building Profiles 283
Comparing Profiles 284
Making Predictions 284
Lessons Learned 285
Market Basket Analysis and Association Rules 287
Defining Market Basket Analysis 289
Three Levels of Market Basket Data 289
Order Characteristics 292
Item Popularity 293
Tracking Marketing Interventions 293
Clustering Products by Usage 294
Association Rules 296
Actionable Rules 296
Trivial Rules 297
Inexplicable Rules 297
How Good Is an Association Rule? 299
Building Association Rules 302
Choosing the Right Set of Items 303
Product Hierarchies Help to Generalize Items 305
Virtual Items Go beyond the Product Hierarchy 307
Data Quality 308
Anonymous versus Identified 308
Generating Rules from All This Data 308
Calculating Confidence 309
Calculating Lift 310
The Negative Rule 311
Overcoming Practical Limits 311
The Problem of Big Data 313
Extending the Ideas 315
Using Association Rules to Compare Stores 315
Dissociation Rules 317
Sequential Analysis Using Association Rules 318
Lessons Learned 319
Link Analysis 321
Basic Graph Theory 322
Seven Bridges of K?nigsberg 325
Traveling Salesman Problem 327
Directed Graphs 330
Detecting Cycles in a Graph 330
A Familiar Application of Link Analysis 331
The Kleinberg Algorithm 332
The Details: Finding Hubs and Authorities 333
Creating the Root Set 333
Identifying the Candidates 334
Ranking Hubs and Authorities 334
Hubs and Authorities in Practice 336
Contents xiii
Case Study: Who Is Using Fax Machines from Home?
336
Why Finding Fax Machines Is Useful
336
The Data as a Graph
337
The Approach
338
Some Results 340
Case Study: Segmenting Cellular Telephone Customers
343
The Data 343
Analyses without Graph Theory
343
A Comparison of Two Customers
344
The Power of Link Analysis 345
Lessons Learned 346
Chapter 11 Automatic Cluster Detection 349
Searching for Islands of Simplicity 350
Star Light, Star Bright 351
Fitting the Troops 352
K-Means Clustering 354
Three Steps of the K-Means Algorithm 354
What K Means 356
Similarity and Distance 358
Similarity Measures and Variable Type 359
Formal Measures of Similarity 360
Geometric Distance between Two Points 360
Angle between Two Vectors 361
Manhattan Distance 363
Number of Features in Common 363
Data Preparation for Clustering 363
Scaling for Consistency 363
Use Weights to Encode Outside Information 365
Other Approaches to Cluster Detection 365
Gaussian Mixture Models 365
Agglomerative Clustering 368
An Agglomerative Clustering Algorithm 368
Distance between Clusters 368
Clusters and Trees 370
Clustering People by Age: An Example of
Agglomerative Clustering 370
Divisive Clustering 371
Self-Organizing Maps 372
Evaluating Clusters 372
Inside the Cluster 373
Outside the Cluster 373
Case Study: Clustering Towns 374
Creating Town Signatures 374
The Data 375
Creating Clusters 377
Determining the Right Number of Clusters 377
Using Thematic Clusters to Adjust Zone Boundaries 380
Lessons Learned 381
Chapter 12
Chapter 13
Knowing When to Worry: Hazard Functions and
Survival Analysis in Marketing 383
Customer Retention 385
Calculating Retention 385
What a Retention Curve Reveals 386
Finding the Average Tenure from a Retention Curve 387
Looking at Retention as Decay 389
Hazards 394
The Basic Idea 394
Examples of Hazard Functions 397
Constant Hazard 397
Bathtub Hazard 397
A Real-World Example 398
Censoring 399
Other Types of Censoring 402
From Hazards to Survival 404
Retention 404
Survival 405
Proportional Hazards 408
Examples of Proportional Hazards 409
Stratification: Measuring Initial Effects on Survival 410
Cox Proportional Hazards 410
Limitations of Proportional Hazards 411
Survival Analysis in Practice 412
Handling Different Types of Attrition 412
When Will a Customer Come Back? 413
Forecasting 415
Hazards Changing over Time 416
Lessons Learned 418
Genetic Algorithms 421
How They Work 423
Genetics on Computers 424
Selection 429
Crossover 430
Mutation 431
Representing Data 432
Case Study: Using Genetic Algorithms for
Resource Optimization 433
Schemata: Why Genetic Algorithms Work 435
More Applications of Genetic Algorithms 438
Application to Neural Networks 439
Case Study: Evolving a Solution for Response Modeling 440
Business Context 440
Data 441
The Data Mining Task: Evolving a Solution 442
Beyond the Simple Algorithm 444
Lessons Learned 446
Contents xv
Chapter 14 Data Mining throughout the Customer Life Cycle
447
Levels of the Customer Relationship
448
Deep Intimacy
449
Mass Intimacy
451
In-between Relationships
453
Indirect Relationships
453
Customer Life Cycle
454
The Customer’s Life Cycle: Life Stages
455
Customer Life Cycle
456
Subscription Relationships versus Event-Based Relationships 458
Event-Based Relationships 458
Subscription-Based Relationships 459
Business Processes Are Organized around
the Customer Life Cycle 461
Customer Acquisition 461
Who Are the Prospects? 462
When Is a Customer Acquired? 462
What Is the Role of Data Mining? 464
Customer Activation 464
Relationship Management 466
Retention 467
Winback 470
Lessons Learned 470
Chapter 15 Data Warehousing, OLAP, and Data Mining 473
The Architecture of Data 475
Transaction Data, the Base Level 476
Operational Summary Data 477
Decision-Support Summary Data 477
Database Schema 478
Metadata 483
Business Rules 484
A General Architecture for Data Warehousing 484
Source Systems 486
Extraction, Transformation, and Load 487
Central Repository 488
Metadata Repository 491
Data Marts 491
Operational Feedback 492
End Users and Desktop Tools 492
Analysts 492
Application Developers 493
Business Users 494
Where Does OLAP Fit In? 494
What’s in a Cube? 497
Three Varieties of Cubes 498
Facts 501
Dimensions and Their Hierarchies 502
Conformed Dimensions 504
xvi Contents
Chapter 16
Star Schema 505
OLAP and Data Mining 507
Where Data Mining Fits in with Data Warehousing 508
Lots of Data 509
Consistent, Clean Data 510
Hypothesis Testing and Measurement 510
Scalable Hardware and RDBMS Support 511
Lessons Learned 511
Building the Data Mining Environment 513
A Customer-Centric Organization 514
An Ideal Data Mining Environment 515
The Power to Determine What Data Is Available 515
The Skills to Turn Data into Actionable Information 516
All the Necessary Tools 516
Back to Reality 516
Building a Customer-Centric Organization 516
Creating a Single Customer View 517
Defining Customer-Centric Metrics 519
Collecting the Right Data 520
From Customer Interactions to Learning Opportunities 520
Mining Customer Data 521
The Data Mining Group 521
Outsourcing Data Mining 522
Outsourcing Occasional Modeling 522
Outsourcing Ongoing Data Mining 523
Insourcing Data Mining 524
Building an Interdisciplinary Data Mining Group 524
Building a Data Mining Group in IT 524
Building a Data Mining Group in the Business Units 525
What to Look for in Data Mining Staff 525
Data Mining Infrastructure 526
The Mining Platform 527
The Scoring Platform 527
One Example of a Production Data Mining Architecture 528
Architectural Overview 528
Customer Interaction Module 529
Analysis Module 530
Data Mining Software 532
Range of Techniques 532
Scalability 533
Support for Scoring 534
Multiple Levels of User Interfaces 535
Comprehensible Output 536
Ability to Handle Diverse Data Types 536
Documentation and Ease of Use 536
Contents xvii
Availability of Training for Both Novice and
Advanced Users, Consulting, and Support 537
Vendor Credibility
537
Lessons Learned
537
Chapter 17 Preparing Data for Mining
539
What Data Should Look Like
540
The Customer Signature
540
The Columns 542
Columns with One Value 544
Columns with Almost Only One Value 544
Columns with Unique Values 546
Columns Correlated with Target 547
Model Roles in Modeling 547
Variable Measures 549
Numbers 550
Dates and Times 552
Fixed-Length Character Strings 552
IDs and Keys 554
Names 555
Addresses 555
Free Text 556
Binary Data (Audio, Image, Etc.) 557
Data for Data Mining 557
Constructing the Customer Signature 558
Cataloging the Data 559
Identifying the Customer 560
First Attempt 562
Identifying the Time Frames 562
Taking a Recent Snapshot 562
Pivoting Columns 563
Calculating the Target 563
Making Progress 564
Practical Issues 564
Exploring Variables 565
Distributions Are Histograms 565
Changes over Time 566
Crosstabulations 567
Deriving Variables 568
Extracting Features from a Single Value 569
Combining Values within a Record 569
Looking Up Auxiliary Information 569
Pivoting Regular Time Series 572
Summarizing Transactional Records 574
Summarizing Fields across the Model Set 574
xviii Contents
Chapter 18
Index
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
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
615
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