Building data mining applications for CRM:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
McGraw-Hill
2000
|
| Schriftenreihe: | Datamanagement
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVII, 510 S. graph. Darst. |
| ISBN: | 0071344446 |
Internformat
MARC
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| 100 | 1 | |a Berson, Alex |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Building data mining applications for CRM |c Alex Berson ; Stephen Smith ; Kurt Thearling |
| 264 | 1 | |a New York [u.a.] |b McGraw-Hill |c 2000 | |
| 300 | |a XXVII, 510 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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Datensatz im Suchindex
| _version_ | 1804128124120596480 |
|---|---|
| adam_text | Preface xix
Acknowledgements xxv
¦¦ 1 THE IMPACT OF DATA MINING ON CRM
Introduction 2
Chapter 1 Customer Relationships 3
Introduction 4
What Is Data Mining? 6
An Example 7
Relevance to a Business Process 8
Data Mining and Customer Relationship Management 10
How Data Mining Helps Database Marketing 11
Scoring 11
The Role of Campaign Management Software 12
Increasing Customer Lifetime Value 12
Combining Data Mining and Campaign Management 13
Evaluating the Benefits of a Data Mining Model 13
Chapter 2 Data Mining and Data Warehousing —
A Connected View 15
Introduction 16
Data Mining and Data Warehousing — the Connection 16
Data Warehousing Overview ] 8
Data Warehousing ROI 19
Operational and Informational Data Stores 20
Definition and Characteristics of a Data Warehouse 26
Data Warehouse Architecture 29
Data Mining 33
Data Mining Defined 33
Data Mining Application Domains 34
Data Mining Categories and Research Focus 36
Chapter 3 Customer Relationship Management 41
Introduction 42
The Most Profitable Customer 42
Customer Relationship Management 43
The Customer Centered Database 46
Managing Campaigns 47
Contents
The Evolution of Marketing 49
Closed Loop Marketing 49
The CRM Architecture 50
Next Generation CRM 51
Foundation — The Technologies and Tools 53
¦¦ 2 FOUNDATION—THE TECHNOLOGIES AND TOOLS
Introduction 53
Chapter 4 DataWarehousing Components 55
Introduction 56
Overall Architecture 56
Data Warehouse Database 58
Sourcing, Acquisition, Cleanup and Transformation Tools 59
Metadata 60
Access Tools 63
Accessing and Visualizing Information 64
Tool Taxonomy 67
Query and Reporting Tools 68
Applications 69
OLAP Tools 69
Data Mining Tools 70
Data Marts 72
Data Warehouse Administration and Management 75
Impact of the Web 76
Approaches to Using the Web 78
Design Options and Issues 79
XML 82
Chapter 5 Data Mining 89
What Is Data Mining? 90
The Mining Analogy 90
What Data Mining Isn t 9
Statistics 9
OLAP 91
Data Warehousing 92
Data Mining Has Come of Age 92
The Motivation for Data Mining Is Tremendous 94
Learning from Your Past Mistakes 9^
Data Mining? Don t Need It — I ve Got Statistics 97
Measuring Data Mining Effectiveness — Accuracy, Speed, Cost 97
Contents j^
Embedding Data Mining into Your Business Process 101
The More Things Change, the More They Remain the Same 102
Discovery versus Prediction 103
Gold in Them Thar Hills 103
Discovery — Finding Something That You Weren t Looking for 104
Prediction 105
Overfitting 105
State of the Industry 106
Targeted Solutions 106
Business Tools 107
Business Analyst Tools 107
Research Analyst Tools 108
Data Mining Methodology 109
What Is a Pattern? What Is a Model? 109
visualizing a Pattern 1 ] 2
A Note on Terminology 113
A Note on Terminology 1 14
A Note on Knowledge and Wisdom 116
Sampling 1 ] 7
Random Sampling I ] 8
Validating the Model I 19
Picking the Best Model 120
The Types of Data Mining Applications 121
Chapter 6 Classical Techniques: Statistics, Neighborhoods,
and Clustering 123
The Classics 124
What Is Different between Statistics and Data Mining? 125
What Is Statistics? 126
Data, Counting, and Probability 127
Histograms 128
Statistics for Prediction 131
Linear Regression ] 31
What If the Pattern In My Data Doesn t
Look Like a Straight Line? 133
Nearest Neighbor 134
A Simple Example of Clustering 135
A Simple Example of Nearest Neighbor 135
How to Use Nearest Neighbor for Prediction 136
Where Is the Nearest Neighbor Technique Used In Business? 137
Using Nearest Neighbor for Stock Market Data 137
Why Voting Is Better — K Nearest Neighbors 138
Contents
How Can the Nearest Neighbor Tell You
How Confident It Is with the Prediction? 139
Clustering 139
Clustering for Clarity 140
Finding the Ones That Don t Fit In—Clustering for Outliers 141
How Is Clustering Like the Nearest Neighbor Technique? 141
How to Put Clustering and Nearest
Neighbor to Work for Prediction 142
Is There Another Correct Way to Cluster? 143
How Are Tradeoffs Made When Determining Which
Records Fall into Which Clusters? 144
Clustering Is the Happy Medium between Homogeneous
Clusters and the Fewest Number of Clusters. 145
What Is the Difference between Clustering and
the Nearest Neighbor Prediction? 46
What Is an n Dimensional Space?
Do I Really Need to Know This? 47
How Is the Space for Clustering and
Nearest Neighbor Defined? 48
Hierarchical and Non Hierarchical Clustering 48
Non Hierarchical Clustering 51
Hierarchical Clustering 52
Choosing the Classics 54
Chapter 7 Next Generation Techniques: Trees, Networks and Rules 155
The Next Generation 56
Decision Trees 56
What Is a Decision Tree? 56
Viewing Decision Trees as Segmentation with a Purpose 157
Applying Decision Trees to Business 58
Where Can Decision Trees Be Used? 58
Using Decision Trees for Exploration 59
Using Decision Trees for Data Preprocessing 60
Decision Trees for Prediction 60
The First Step Is Growing the Tree 60
The Difference between a Good Question
and a Bad Question 6
When Does the Tree Stop Growing? 62
Why Would a Decision Tree Algorithm Stop
Growing the Tree If There Wasn t Enough Data? 62
Decision Trees Aren t Necessarily Finished after the
Tree Is Grown 63
Contents ^j
ID3 and an Enhancement — C4.5 164
CART — Growing a Forest and Picking the Best Tree 164
CART Automatically Validates the Tree 165
CART Surrogates Handle Missing Data 165
CHAID 166
Neural Networks 166
What Is a Neural Network? 166
Don t Neural Networks Learn to Make Better Predictions? 168
Are Neural Networks Easy to Use? 168
Applying Neural Networks to Business 169
Where to Use Neural Networks 170
Neural Networks for Clustering 171
Neural Networks for Outlier Analysis 171
Neural Networks for Feature Extraction 172
What Does a Neural Net Look Like? 173
How Does a Neural Net Make a Prediction? 174
How Is the Neural Net Model Created? 175
How Complex Can the Neural Network Model Become? 176
Hidden Nodes Are Like Trusted Advisors to the Output Nodes 1 76
The Learning That Goes On in the Hidden Nodes 1 77
Sharing the Blame and the Glory
throughout the Organization 178
Different Types of Neural Networks I 79
Kohonen Feature Maps 180
How Much Like a Human Brain Is the Neural Network? 180
Combatting Overfitting — Getting a Model
You Can Use Somewhere Else 181
Explaining the Network 182
Rule Induction ] 83
Applying Rule Induction to Business ] 84
What Is a Rule? 184
What to Do with a Rule 186
Caveat: Rules Do Not Imply Causality 188
Types of Databases Used for Rule Induction 188
The General Idea 191
The Business Importance of Accuracy and Coverage 191
Trading Off Accuracy and Coverage Is Like
Betting at the Track 192
How to Evaluate the Rule 192
Defining Interestingness 194
Other Measures of Usefulness ] 95
Rules versus Decision Trees 196
Contents
Another Commonality between Decision Trees and
Rule Induction Systems 198
Which Technique and When? 198
Balancing Exploration and Exploitation 199
Chapter 8 When to Use Data Mining 201
Introduction 202
Using the Right Technique 202
The Data Mining Process 202
How Decision Trees Are Like Nearest Neighbor 206
How Rule Induction Is Like Decision Trees 206
How to Do Link Analysis with a Neural Network 208
Data Mining in
the Business Process 208
Avoiding Some Big Mistakes in Data Mining 210
Understanding the Data 211
The Case for Embedded
Data Mining 215
The Cost of a Distributed Business Process 217
The Best Way to Measure a Data Mining Tool 219
The Case for Embedded Data Mining 222
How to Measure Accuracy, Explanation, and Integration 224
Measuring Accuracy 224
Measuring Explanation 227
Measuring Integration 228
What the Future Holds for Embedded Data Mining 229
¦¦3 THE BUSINESS VALUE
Introduction 231
Chapter 9 Customer Profitability 233
Introduction 234
Why Calculate Customer Profitability? 235
The Effect of Loyalty on Customer Profitability 236
Customer Loyalty and the Law of Compound Effect 236
What Is Customer Relationship Management? 237
Optimizing Customer Profitability through Data Mining 238
Predicting Future Profitability 239
Predicting Customer Profitability Transitions 240
Using Customer Profitability to Guide Marketing 240
Why Revenue Isn t Enough 243
Contents XJJJ
Incremental Customer Profitability 243
What Is Incremental Customer Profitability? 244
Telling Your Sales Force to Stop Selling 245
How Do I Get Organizational Buy in? 247
Surrogates Are Often Worse Than Nothing at All 248
The Holy Grail 249
How Do You Measure the Value of Data Mining? 250
Chapter 10 Customer Acquisition 251
Introduction 252
How Data Mining and Statistical Modeling Change Things 253
Defining Some Key Acquisition Concepts 254
It All Begins with the Data 256
Test Campaigns 258
Evaluating Test Campaign Responses 259
Building Data Mining Models Using Response Behaviors 259
Chapter 11 Cross selling 263
Introduction 264
How Cross selling Works 265
Steps in the Process 266
The Analysis Begins 268
Modeling 269
Scoring 269
Optimization 270
Multiple Offers 276
Chapter 12 Customer Retention 277
Introduction 278
Churn in the Cellular
Telephone Industry 279
Data Mining Using CART to Predict Churn 280
Data Mining Techniques to Use 282
Case Study — Customer Retention for Mobile Phones 282
The Data 283
Defining the Target to Be Predicted 283
The Data Mining Implementation 284
The Data Mining Model 284
The Business Implementation 287
The Results 291
The Performance of the Control Groups 292
Contents
Lessons Learned 293
A Surprising Result 295
Changing the Target of the Predictive Model 295
Other Data Sources May Be Helpful 296
Considering Customer Value 296
Considering Save Teams and Other Marketing Efforts 296
Customer Retention in Other Industries 297
Chapter 13 Customer Segmentation 299
Introduction 300
What Is Segmentation? 300
What Is the Value of Segmentation? 301
How Is It Different from One to One Marketing? 301
What Is Data driven Segmentation? 302
How Is Data Driven Segmentation Performed? 303
What Are the Different
Uses of Segmentation? 305
Understanding Your Business and Executing a Strategy 306
Demographic Segmentation 306
Psychographic Segmentation 306
Targeted Segmentation 307
How Can Segmentation Be Performed? 307
What Are Some of the Questions to Ask
of a Segmentation Methodology? 308
How Is Data Mining Used for Segmentation? 308
Integrating Data driven Segmentation 310
Introducing and Removing Segmentation Schemes 311
A Segmentation Is a Common Corporate Language 311
Getting It Right 312
Changing the Segmentation 312
Case Study: The Pharmaceutical Industry 313
Industry Background 313
References 315
¦Hi 4 THE KEYS TO BUILDING THE SOLUTION
Introduction 317
Chapter 14 Building the Business Case 319
Introduction 320
Data Mining Is Complex — If There Is Not
a Business Case, Your Project May Stall 320
Contents XV
How Will You Know That You ve Succeeded? 321
A Fundamental Shift in Business Strategy 321
Uncovering the Needs for Data Mining in Your Company 322
Poorly Executed CRM or Simple Campaign Management 322
Poorly Matched Customer Investment to Customer Value 323
Inability to Transition Customers to Higher Value States 323
Defining the Business Value 324
Increased Revenue 324
Profit 324
Decreased Costs 325
Return on Investment (ROI) 325
Competitive Advantage 326
Early Adopter 327
The Costs 327
The Data 327
Pricing the Infrastructure 328
Pricing the Personnel 329
Costs to Sustain 330
Containing Costs: Leveraging Existing Investments 331
Build the Business Case 332
Chapter 15 Deploying Data Mining for CRM 333
Introduction 334
10 Steps in Launching a Data Mining Application 334
Define the Problem 335
Find Something That Matters 336
Define the Deliverables 337
Pick Something Well Defined and Small 337
Understand the Existing CRM Process 338
Define the User 339
Build a Profile of Each User 340
Use a QuickStart Program to Educate Your Future
User and Elicit Needs and Desires 341
Define the Data 343
Locate the Data Dictionary 343
Locate the Data Librarians 345
Define the Metrics 345
Now, Really Define the Data 346
Assess Levels of Data Integrity 346
Validate Data Sources 347
Scope the Project 347
Contain Scope Creep through a Launch Document 347
Contents
Scope Data Cleansing 348
Scope Data Movement, Modeling and Storage 348
Scope Data Mining 349
Scope Experimental Design and Measurement 349
Trial 350
Don t Wait Too Long 350
Start Small, but Go End to End 351
Quality Assurance 352
Make Quality Assurance a Process 352
Validate and Communicate Model Results 353
Education 353
Launch 354
Select Your Initial Users 355
Keep Things under Wraps Until All Results Are In 355
Help Your Users Interpret the Results 356
Continuation 356
Conclusion — Making Data Mining
Part of Your Business Process 357
Chapter 16 Collecting Customer Data 359
Introduction 360
The Three Types of Customer Data 360
Descriptive 362
Promotional 362
Transactional 363
Collecting Customer Data 364
Internal Sources 364
Web Data 366
Connecting Customer Data 366
Data Warehouses and Data Marts 366
Data Pumps and Connectors 367
Long Distance Connections 368
Customer Data and Privacy 370
Privacy and Data Mining 371
Guidelines for Privacy 373
Anonymity and Identity Information 373
Detailed versus Consolidated Data 374
Information Used for Targeting or for Measurement 375
Combined Sources 376
The Anonymous Architecture 376
Legal Issues Associated with Data Mining 377
Contents XVII
Chapter 17 Scoring Your Customers 381
Introduction 382
The Process 383
Scoring Architectures and Configurations 386
Preparing the Data 388
Direct Mapping 390
Offset Mapping 390
Integrating Scoring with Other Applications 391
Creating the Model 392
Dynamically Scoring the Data 393
Chapter 18 Optimizing the CRM Process 395
Introduction 396
Improved Customer Profitability through Optimization 396
What Is Optimization? 396
Why Not Optimize Customer Relationships? 399
To Optimize Something, You Must Have Control over It 400
Why Now? 401
Optimized CRM 403
The Complete Loop 404
Optimal CRM Process: Measure, Predict, Act 405
What Marketing Optimization Is Not 407
Using Data Mining to Optimize Your CRMS 408
Optimization Techniques 409
Simulated Annealing and Neural Networks 41 |
Chapter 19 Overview of Data Mining and CRM Tool Markets 413
Introduction 414
Data Mining Marketplace 414
Taxonomy of Data Mining Tools 41 5
Tool Assessment: Attributes and Methodology 416
Tool Evaluation 419
Clementine (SPSS) 419
4Thought and Scenario (Cognos) 422
Darwin (Oracle) 426
Database Mining Workstation (HNC) 429
Decision Series (Neovista) 431
Enterprise Miner (SAS) 434
Intelligent Miner (IBM) 437
Contents
KnowledgeSEEKER and Knowledge Studio (Angoss) 441
Model 1 and Pattern Recognition Workbench (Unica) 443
Other Data Mining Tools 447
CRM Tools 448
Personalization Tools 449
Campaign Management/Marketing Tools 452
Sales Automation and Customer Service Tools 454
Chapter 20 Conclusion: Next Generation of Information Mining
and Knowledge Discovery for Effective Customer
Relationship Management 457
Business Intelligence and Information Mining 458
Text Mining and Knowledge Management 460
Benefits of Text Mining 461
Text Mining Technologies 462
Internet Searching 462
Text Analysis 463
Semantic Networks and Other Techniques 465
Text Mining Products 466
Using the Power of the Human Brain 472
Conclusion 478
Knowledge Management 479
Customer Relationship Management
in the e Business World 479
Application Services Providers 481
Data Visualization and Information Design 497
CRM and Database Marketing 497
Data Warehousing 498
Application Services Providers 446
Glossary 485
References 495
Index 449
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| spelling | Berson, Alex Verfasser aut Building data mining applications for CRM Alex Berson ; Stephen Smith ; Kurt Thearling New York [u.a.] McGraw-Hill 2000 XXVII, 510 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datamanagement Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Entscheidungsunterstützungssystem (DE-588)4191815-0 s DE-604 Smith, Stephen Verfasser aut Thearling, Kurt Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009103544&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Berson, Alex Smith, Stephen Thearling, Kurt Building data mining applications for CRM Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Data Mining (DE-588)4428654-5 gnd |
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| title | Building data mining applications for CRM |
| title_auth | Building data mining applications for CRM |
| title_exact_search | Building data mining applications for CRM |
| title_full | Building data mining applications for CRM Alex Berson ; Stephen Smith ; Kurt Thearling |
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