A complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing. It guides readers through all the phases of the data mining process, presenting a solid data mining methodology, data mining best practices and recommendations for the use of the data mining results for effective marketing. It answers the crucial question of 'what data to use' by proposing mining data marts and full lists of KPIs for all major industries.Data mining algorithms are presented in a simple and comprehensive way for the business users along with real-world application examples from all major industries.The book is mainly addressed to marketers, business analysts and data mining practitioners who are looking for a how-to guide on data mining. It presents the authors' knowledge and experience from the "data mining trenches", revealing the secrets for data mining success.
Author(s): Konstantinos Tsiptsis, Antonios Chorianopoulos
Edition: 1
Year: 2010
Language: English
Pages: 372
Data Mining Techniques in CRM......Page 5
CONTENTS......Page 9
ACKNOWLEDGEMENTS......Page 15
The CRM Strategy......Page 17
What Can Data Mining Do?......Page 18
Unsupervised Models......Page 19
Customer Segmentation......Page 20
Direct Marketing Campaigns......Page 21
Market Basket and Sequence Analysis......Page 23
The Next Best Activity Strategy and ‘‘Individualized’’ Customer Management......Page 24
The Data Mining Methodology......Page 26
Summary......Page 29
Supervised Modeling......Page 33
Predicting Events with Classification Modeling......Page 35
Evaluation of Classification Models......Page 41
Marketing Applications Supported by Classification Modeling......Page 48
Setting Up a Voluntary Churn Model......Page 49
Finding Useful Predictors with Supervised Field Screening Models......Page 52
Predicting Continuous Outcomes with Estimation Modeling......Page 53
Unsupervised Modeling Techniques......Page 55
Segmenting Customers with Clustering Techniques......Page 56
Reducing the Dimensionality of Data with Data Reduction Techniques......Page 63
Finding ‘‘What Goes with What’’ with Association or Affinity Modeling Techniques......Page 66
Discovering Event Sequences with Sequence Modeling Techniques......Page 72
Detecting Unusual Records with Record Screening Modeling Techniques......Page 75
Machine Learning/Artificial Intelligence vs. Statistical Techniques......Page 77
Summary......Page 78
Principal Components Analysis......Page 81
How Many Components Are to Be Extracted?......Page 83
What Is the Meaning of Each Component?......Page 91
Does the Solution Account for All the Original Fields?......Page 94
Proceeding to the Next Steps with the Component Scores......Page 95
Recommended PCA Options......Page 96
Clustering Techniques......Page 98
Data Considerations for Clustering Models......Page 99
Clustering with K-means......Page 101
Recommended K-means Options......Page 103
Clustering with the TwoStep Algorithm......Page 104
Recommended TwoStep Options......Page 106
Clustering with Kohonen Network/Self-organizing Map......Page 107
Recommended Kohonen Network/SOM Options......Page 109
The Number of Clusters and the Size of Each Cluster......Page 112
Cohesion of the Clusters......Page 113
Separation of the Clusters......Page 115
Understanding the Clusters through Profiling......Page 116
Profiling the Clusters with IBM SPSS Modeler’s Cluster Viewer......Page 118
Additional Profiling Suggestions......Page 121
Selecting the Optimal Cluster Solution......Page 124
An Introduction to Decision Tree Models......Page 126
The Advantages of Using Decision Trees for Classification Modeling......Page 137
One Goal, Different Decision Tree Algorithms: C&RT, C5.0, and CHAID......Page 139
Recommended CHAID Options......Page 141
Summary......Page 143
Designing the Mining Data Mart......Page 149
The Time Frame Covered by the Mining Data Mart......Page 151
The Mining Data Mart for Retail Banking......Page 153
Product Status......Page 154
Monthly Information......Page 156
Bank Transactions......Page 157
Lookup Information......Page 159
Product Codes......Page 160
Transaction Types......Page 161
The Customer ‘‘Signature’’ – from the Mining Data Mart to the Marketing Customer Information File......Page 164
Creating the MCIF through Data Processing......Page 165
Derived Measures Used to Provide an ‘‘Enriched’’ Customer View......Page 170
The MCIF for Retail Banking......Page 171
The Mining Data Mart for Mobile Telephony Consumer (Residential) Customers......Page 176
Transforming CDR Data into Marketing Information......Page 178
Current Information......Page 179
Customer Information......Page 180
Rate Plan History......Page 181
Outgoing Usage......Page 183
Incoming Usage......Page 185
Lookup Information......Page 186
Service Types......Page 187
The MCIF for Mobile Telephony......Page 188
The Mining Data Mart for Retailers......Page 193
Customer Information......Page 195
Transactions......Page 196
Purchases by Product Groups......Page 198
The Product Hierarchy......Page 199
The MCIF for Retailers......Page 200
Summary......Page 203
An Introduction to Customer Segmentation......Page 205
Segmentation in Marketing......Page 206
Segmentation Types in Consumer Markets......Page 207
Value-Based Segmentation......Page 209
Behavioral Segmentation......Page 210
Propensity-Based Segmentation......Page 211
Loyalty Segmentation......Page 212
Socio-demographic and Life-Stage Segmentation......Page 214
Needs/Attitudinal-Based Segmentation......Page 215
Segmentation in Business Markets......Page 216
Business Understanding and Design of the Segmentation Process......Page 219
Data Understanding, Preparation, and Enrichment......Page 221
Evaluation and Profiling of the Revealed Segments......Page 224
Tips and Tricks......Page 227
Segmentation Management Strategy......Page 229
Business Understanding and Design of the Segmentation Process......Page 232
Grouping Customers According to Their Value......Page 234
Deployment of the Segmentation Solution......Page 235
Designing Differentiated Strategies for the Value Segments......Page 236
Summary......Page 239
Segmentation for Credit Card Holders......Page 241
Designing the Behavioral Segmentation Project......Page 242
Building the Mining Dataset......Page 243
Selecting the Segmentation Population......Page 244
The Segmentation Fields......Page 246
Revealing the Segmentation Dimensions......Page 249
Identification and Profiling of Segments......Page 253
Using the Segmentation Results......Page 272
Behavioral Segmentation Revisited: Segmentation According to All Aspects of Card Usage......Page 274
The Credit Card Case Study: A Summary......Page 279
Why Segmentation?......Page 280
Segmenting Customers According to Their Value: The Vital Few Customers......Page 283
Using Business Rules to Define the Core Segments......Page 284
Selecting the Segmentation Fields......Page 287
Identifying the Segmentation Dimensions with PCA/Factor Analysis......Page 290
Profiling of Segments......Page 292
Setting the Business Objectives......Page 299
Segmentation in Retail Banking: A Summary......Page 304
Mobile Telephony......Page 307
Mobile Telephony Core Segments – Selecting the Segmentation Population......Page 308
Behavioral and Value-Based Segmentation – Setting Up the Project......Page 310
Segmentation Fields......Page 311
Value-Based Segmentation......Page 316
Value-Based Segments: Exploration and Marketing Usage......Page 320
Preparing Data for Clustering – Combining Fields into Data Components......Page 323
Identifying, Interpreting, and Using Segments......Page 329
Segmentation Deployment......Page 342
The Fixed Telephony Case......Page 345
Summary......Page 347
Segmentation in the Retail Industry......Page 349
The RFM Analysis......Page 350
The RFM Segmentation Procedure......Page 354
RFM: Benefits, Usage, and Limitations......Page 361
Grouping Customers According to the Products They Buy......Page 362
Summary......Page 364
FURTHER READING......Page 365
INDEX......Page 367
