Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York [u.a.]
Wiley
2000
|
| Schriftenreihe: | Wiley computer publishing
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008887234&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVIII, 494 S. graph. Darst. |
| ISBN: | 0471331236 |
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Datensatz im Suchindex
| DE-BY-OTHR_call_number | F 03/QP 345 B534 |
|---|---|
| DE-BY-OTHR_katkey | 2440260 |
| DE-BY-OTHR_location | 02 |
| DE-BY-OTHR_media_number | 067001621966 |
| _version_ | 1831258018495332352 |
| adam_text | Acknowledgments xv
Introduction xvii
Part One Setting the Focus i_
Chapter 1 Data Mining in Context 5
What Is Data Mining? 7
What Can Data Mining Do? 8
Classification 8
Estimation 9
Prediction 10
Affinity Grouping or Association Rules 10
Clustering 10
Description and Visualization 11
The Business Context for Data Mining 11
Data Mining as a Research Tool 12
Data Mining for Process Improvement 13
Data Mining for Marketing 13
Data Mining for Customer Relationship Management 14
The Technical Context for Data Mining 14
Data Mining and Machine Learning 15
Data Mining and Statistics 15
Data Mining and Decision Support 16
Data Mining and Computer Technology 19
The Societal Context for Data Mining 19
Chapter 2 Why Master the Art? 21
Four Approaches to Data Mining 23
Purchasing Scores 24
Purchasing Software 24
Hiring Outside Experts 32
Developing In House Expertise 35
Lessons Learned 36
Chapter 3 Data Mining Methodology: The Virtuous Cycle Revisited 39
Two Styles of Data Mining 40
Directed Data Mining 40
Undirected Data Mining 42
The Virtuous Cycle of Data Mining 43
Identifying the Right Business Problem 44
Is the Data Mining Effort Necessary? 45
Is There a Particular Segment or Subgroup
That Is Most Interesting? 46
What Are the Relevant Business Rules? 46
What about the Data? 47
Verifying the Opinion of Domain Experts 47
Transforming Data into Actionable Results 48
Identify and Obtain Data 49
Validate, Explore, and Clean the Data 50
Transpose the Data to the Right Granularity 51
Add Derived Variables 52
Prepare the Model Set 53
Choose the Modeling Technique and Train the Model 54
Check Performance of the Models 54
Acting on the Results 57
Measuring the Model s Effectiveness 58
What Makes Predictive Modeling Successful? 59
Time Frames of Predictive Modeling 59
Modeling Shelf Life 60
Assumption 1: The Past Is a Good Predictor of the Future 61
Assumption 2: The Data Is Available 62
Assumption 3: The Data Contains What We Want to Predict 63
Lessons Learned 64
Chapter 4 Customers and Their Lifecycles 65
Who Is the Customer? 66
Consumers 66
Business Customers 67
Customer Segments 70
The Customer Lifecycle 72
Stages of the Lifecycle 73
Major Lifecycle Events 75
Data Appears at Different Times in the Lifecycle 78
The Customer s Lifecycle 79
Targeting the Right Customers at the Right Time 80
Budget Optimization 80
Campaign Optimization 82
Customer Optimization 86
Lessons Learned 90
Part Two The Three Pillars of Data Mining 91_
Chapter 5 Data Mining Techniques and Algorithms 99
Different Goals Call for Different Techniques 100
Different Data Types Call for Different Techniques 102
Three Data Mining Techniques 102
Automatic Cluster Detection 103
How K Means Cluster Detection Works 104
Consequences of Choosing Clustering 107
Decision Trees 111
How Decision Trees Work 112
How Decision Trees Are Built 113
Consequences of Choosing Decision Trees 119
Neural Networks 121
Training Neural Networks 125
Consequences of Choosing Neural Networks 127
Lessons Learned 129
Chapter 6 Data, Data Everywhere... 131
What Should Data Look Like? 132
The Rows 132
The Columns 134
Roles of Columns in Data Mining 138
Data for Data Mining 140
What Does Data Really Look Like? 141
Where Data Comes From 141
The Right Level of Granularity 150
Different Ways to Measure Data Values 153
How Much Data Is Enough? 157
Derived Variables 158
Issues in Working with Derived Variables 159
Handling Outliers 160
Combinations of Columns 162
Summarizations 163
Extracting Features from Single Columns 164
Time Series 167
Case Study: Defining Customer Behavior 169
Dirty Data 177
Missing Data 177
Fuzzy Definitions 179
Incorrect Values 180
Lessons Learned 181
Chapter 7 Building Effective Predictive Models 183
Building Good Predictive Models 184
A Process for Building Predictive Models 184
Lift as a Measure of Performance 186
Model Stability 191
The Challenge of Model Stability 192
Working with the Model Set 193
Divide and Conquer: Training, Test, and Evaluation Sets 193
How the Size of the Model Set Affects Results 194
How the Density of the Model Set Affects Results 195
Sampling 196
What Is Oversampling? 197
Modeling Time Dependent Data 201
Model Inputs and Model Outputs 203
Latency: Taking Model Deployment into Account 206
Time and Missing Data 209
Building Models that Easily Shift in Time 210
Naming Fields 212
Using Multiple Models 213
Multiple Model Voting 213
Segmenting the Input 218
Other Reasons to Combine Models 219
Experiment! 221
The Model Set 221
Different Types of Models and Model Parameters 223
Time Frame 223
Lessons Learned 224
Chapter 8 Taking Control: Setting Up a Data Mining Environment 227
Getting Started 228
What Is a Data Mining Environment? 228
Four Case Studies 229
What Makes a Data Mining Environment Successful? 229
Case 1: Building Up a Core Competency Internally 230
Data Mining in the Insurance Industry 231
Getting Started 231
Case 2: Building a New Line of Business 235
Going Online 235
The Environment 236
The Prospect Data Warehouse 236
The Next Step 237
Case 3: Building Data Mining Skills on Data Warehouse Efforts 240
A Special Kind of Data Warehouse 240
The Plan for Data Mining 240
Data Mining in IT 241
Case 4: Data Mining Using Tessera RME 242
Requirements for an Advanced Data Mining Environment 242
What Is RME? 243
How RME Works 244
How RME Helps Prepare Data 246
How RME Supports Sampling 247
How RME Helps in Model Development 248
How RME Helps in Scoring and Managing Models 249
Lessons Learned 251
Part Three Case Studies 253
Chapter 9 Who Needs Bag Balm and Pants Stretchers 261
The Vermont Country Store 262
How Vermont Country Store Got Where It Is Today 263
Predictive Modeling at Vermont Country Store 265
The Business Problem 265
The Data 267
The Technical Approach 270
Choice of Software Package 270
The Baseline—RFM and Segmentation 271
The Challengers—Neural Networks, Decision Trees,
and Regression 273
Determining What Would Have Happened 276
Calculating the Return on Investment 276
The Future 276
Expected Benefits 277
Lessons Learned 277
Chapter 10 Who Gets What? Building a Best Next Offer Model
for an Online Bank 279
Gaining Wallet Share 279
The Business Problem 281
The Data 282
From Accounts to Customers 284
Defining the Products to Be Offered 287
Approach to the Problem 290
Comparable Scores 291
If It Walks Like a Duck,... 292
Pitfalls of This Approach 292
Building the Models 294
Building a Decision Tree Model for Brokerage 296
Building the Rest of the Models 306
Getting to a Cross Sell Model 307
In a More Perfect World 308
Lessons Learned 308
Chapter 11 Please Don t Go! Churn Modeling in
Wireless Communication 311
The Wireless Telephone Industry 312
A Rapidly Maturing Industry 313
Some Differences from Other Industries 314
The Business Problem 316
Project Background 316
Specifics about This Market 317
What Is Churn? 318
Why Is Churn Modeling Useful? 319
Three Goals 320
Approach to Building the Churn Model 322
The Project Itself 325
Building a Churn Model: A Real Life Application 326
The Choice of Tool 326
Segmenting the Model Set 326
The Final Four (Models) 327
Choice of Modeling Algorithm 331
The Size and Density of the Model Set 338
The Effect of Latency (or Taking Deployment into Account) 339
Translating Models in Time 340
The Data 341
The Basic Customer Model 341
From Telephone Calls to Data 343
Historical Churn Rates 344
Data at the Customer and Account Level 345
Data at the Service Level 345
Data Billing History 346
Rejecting Some Variables 346
Derived Variables 347
Lessons about Building Churn Models 349
Finding the Most Significant Variables 349
Listening to the Business Users 349
Listening to the Data 350
Including Historical Churn Rates 351
Composing the Model Set 352
Building a Model for the Churn Management Application 354
Listening to the Data to Determine Model Parameters 354
Understanding the Algorithm and the Tool 355
Lessons Learned 355
Chapter 12 Converging on the Customer: Understanding Customer
Behavior in the Telecommunications Industry 357
Dataflows 358
What Is a Dataflow? 359
Basic Operations 360
Dataflows in a Parallel Environment 361
Why Are Dataflows Efficient? 363
The Business Problem 364
Project Background 364
Important Marketing Questions 365
The Data 366
Call Detail Data 366
Customer Data 368
Auxiliary Files 372
A Voyage of Discovery 372
What Is in a Call Duration? 372
Calls by Time of Day 374
Calls by Market Segment 378
International Calling Patterns 382
When Are Customers at Home? 384
Internet Service Providers 387
Private Networks 388
Concurrent Calls 391
Lessons Learned 393
Chapter 13 Who Is Buying What? Getting to Know
Supermarket Shoppers 395
An Industry in Transition 396
Supermarkets as Information Brokers 396
Shifting the Focus from Products to Customers 399
Three Case Studies 401
Analyzing Ethnic Purchasing Patterns 402
Business Background 402
The Data 402
A Triumph for Visualization 405
A Failed Approach 407
Just the Facts 407
Who Buys Yogurt at the Supermarket? 410
Business Background 411
The Data 411
From Groceries to Customers 416
Finding Clusters of Customers 419
Putting the Clusters to Work 421
Who Buys Meat at the Health Food Store? 424
Association Rules for Market Basket Analysis 424
People are More Interesting Than Groceries 429
Lessons Learned 432
Chapter 14 Waste Not Want Not: Improving
Manufacturing Processes 435
Data Mining to Reduce Cost at R. R Donneley 436
The Technical Problem 436
The Business Problem 437
The Data 438
Inducing Rules for Cylinder Bands 442
Change on the Shop Floor 444
Long Term Impact 445
Reducing Paper Wastage at Time Inc. 445
The Business Problem 446
The Data 449
Approach to the Problem 452
Types of Waste 453
Addressable Waste 455
Inducing Rules for Addressable Waste 456
Data Transformation 456
Data Characterization and Profiling 459
Decision Trees 459
Association Rules 462
Putting It All Together 463
Lessons Learned 464
Chapter 15 The Societal Context: Data Mining and Privacy 465
The Privacy Prism 466
Is Data Mining a Threat? 468
The Expectation of Privacy 470
The Importance of Privacy 471
Information in the Material World . 476
Information in the Electronic World 477
Identifying the Customer 478
Putting It All Together 480
The Promise of Data Mining 483
Index 485
|
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| id | DE-604.BV013044597 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T10:40:29Z |
| institution | BVB |
| isbn | 0471331236 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008887234 |
| oclc_num | 247195138 |
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| physical | XVIII, 494 S. graph. Darst. |
| publishDate | 2000 |
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| publisher | Wiley |
| record_format | marc |
| series2 | Wiley computer publishing |
| spellingShingle | Berry, Michael J. A. Linoff, Gordon Mastering data mining the art and science of customer relationship management Data Mining Data mining gtt Entreprises - Réseaux d'ordinateurs - Gestion Marketing - Informatique Relatiemarketing gtt Datenverarbeitung Unternehmen Business enterprises Computer networks Management Data mining Marketing Data processing Kundenmanagement (DE-588)4236865-0 gnd Kundenbetreuung (DE-588)4297543-8 gnd Beziehungsmanagement (DE-588)4326109-7 gnd Kundenanalyse (DE-588)4304338-0 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4236865-0 (DE-588)4297543-8 (DE-588)4326109-7 (DE-588)4304338-0 (DE-588)4428654-5 |
| title | Mastering data mining the art and science of customer relationship management |
| title_auth | Mastering data mining the art and science of customer relationship management |
| title_exact_search | Mastering data mining the art and science of customer relationship management |
| title_full | Mastering data mining the art and science of customer relationship management Michael J. A. Berry ; Gordon Linoff |
| title_fullStr | Mastering data mining the art and science of customer relationship management Michael J. A. Berry ; Gordon Linoff |
| title_full_unstemmed | Mastering data mining the art and science of customer relationship management Michael J. A. Berry ; Gordon Linoff |
| title_short | Mastering data mining |
| title_sort | mastering data mining the art and science of customer relationship management |
| title_sub | the art and science of customer relationship management |
| topic | Data Mining Data mining gtt Entreprises - Réseaux d'ordinateurs - Gestion Marketing - Informatique Relatiemarketing gtt Datenverarbeitung Unternehmen Business enterprises Computer networks Management Data mining Marketing Data processing Kundenmanagement (DE-588)4236865-0 gnd Kundenbetreuung (DE-588)4297543-8 gnd Beziehungsmanagement (DE-588)4326109-7 gnd Kundenanalyse (DE-588)4304338-0 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining Data mining Entreprises - Réseaux d'ordinateurs - Gestion Marketing - Informatique Relatiemarketing Datenverarbeitung Unternehmen Business enterprises Computer networks Management Marketing Data processing Kundenmanagement Kundenbetreuung Beziehungsmanagement Kundenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008887234&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT berrymichaelja masteringdataminingtheartandscienceofcustomerrelationshipmanagement AT linoffgordon masteringdataminingtheartandscienceofcustomerrelationshipmanagement |