Data Mining Techniques
in CRM
Inside Customer Segmentation
Konstantinos Tsiptsis
CRM 6- Customer Intelligence Expert, Athens, Greece
Antonios Chorianopoulos
Data Mining Expert, Athens, Greece
WILEY
CONTENTS
ACKNOWLEDGEMENTS xiii 1 DATA MINING IN CRM 1
The CRM Strategy 1 What Can Data Mining Do? 2
Supervised/Predictive Models 3 Unsupervised Models 3 Data Mining in the CRM Framework 4
Customer Segmentation 4 Direct Marketing Campaigns 5 Market Basket and Sequence Analysis ' 7
The Next Best Activity Strategy and "Individualized" Customer Management 8
The Data Mining Methodology 10 Data Mining and Business Domain Expertise 13 Summary 13
2 AN OVERVIEW OF DATA MINING TECHNIQUES 17
Supervised Modeling 17
Predicting Events with Classification Modeling 19
Evaluation of Classification Models 25 Scoring with Classification Models 32
Marketing Applications Supported by Classification Modeling 32 Setting Up a Voluntary Churn Model 33 Finding Useful Predictors with Supervised Field Screening Models 36 Predicting Continuous Outcomes with Estimation Modeling. 37
Unsupervised Modeling Techniques 39
Segmenting Customers with Clustering Techniques 40 Reducing the Dimensionality of Data with Data Reduction Techniques 47 Finding "What Goes with What" with Association or Affinity Modeling
Techniques 50 Discovering Event Sequences with Sequence Modeling Techniques 56 Detecting Unusual Records with Record Screening Modeling Techniques 59
Machine Learning/Artificial Intelligence vs. Statistical Techniques 61 Summary 62
viii CONTENTS
3 DATA MINING TECHNIQUES FOR SEGMENTATION 65
Segmenting Customers with Data Mining Techniques 65 Principal Components Analysis 65
PC A Data Considerations 67 How Many Components Are to Be Extracted? 67 What Is the Meaning of Each Component? 75 Does the Solution Account for All the Original Fields? 78 Proceeding to the Next Steps with the Component Scores 79 Recommended PC A Options 80
Clustering Techniques 82
Data Considerations for Clustering Models 83 Clustering with K-means 85
Recommended K-means Options 87
Clustering with the TwoStep Algorithm 88
Recommended TwoStep Options 90
Clustering with Kohonen Network/Self-organizing Map 91
Recommended Kohonen Network/SOM Options 93 Examining and Evaluating the Cluster Solution 96
The Number of Clusters and the Size of Each Cluster 96 Cohesion of the Clusters 97 Separation of the Clusters 99
Understanding the Clusters through Profiling 100
Profiling the Clusters with IBM SPSS Modeler's Cluster Viewer 102 Additional Profiling Suggestions 105
Selecting the Optimal Cluster Solution 108 Cluster Profiling and Scoring with Supervised Models 110 An Introduction to Decision Tree Models 110
The Advantages of Using Decision Trees for Classification Modeling 121 One Goal, Different Decision Tree Algorithms: CirRT, C5.0, and CHAID 123
Recommended CHAID Options 125 Summary 127
4 THE MINING DATA MART 133
Designing the Mining Data Mart 133 The Time Frame Covered by the Mining Data Mart 135 The Mining Data Mart for Retail Banking 137
Current Information 138
Customer Information 138 Product Status 138
Monthly Information 140
Segment and Group Membership 141 Product Ownership and Utilization 141 Bank Transactions 141
Lookup Information 143
CONTENTS ix
Transaction Channels 145 Transaction Types 145
The Customer "Signature" -from the Mining Data Mart to the Marketing
Customer Information File 148
Creating the MCIF through Data Processing 149 Derived Measures Used to Provide an "Enriched" Customer View 154
The MCIF for Retail Banking 155
The Mining Data Mart for Mobile Telephony Consumer (Residential) Customers 160
Mobile Telephony Data and CDRs 162
Transforming CDR Data into Marketing Information 162
Current Information 163
Customer Information 164 Rate Plan History 165
Monthly Information 167 Outgoing Usage 167 Incoming Usage 169 Outgoing Network 170 Incoming Network 170 Lookup Information 170 Rate Plans 171 Service Types 171 Networks 172
The MCIF for Mobile Telephony 172
The Mining Data Mart for Retailers 177
Transaction Records 179 Current Information 179
Customer Information 179
Monthly Information 180
Transactions 180 Purchases by Product Groups 182
Lookup Information 183
The Product Hierarchy 183
The MCIF for Retailers 184
Summary 187
5 CUSTOMER SEGMENTATION 189
An Introduction to Customer Segmentation 189
Segmentation in Marketing 190 Segmentation Tasks and Criteria 191
Segmentation Types in Consumer Markets 191
Value-Based Segmentation 193 Behavioral Segmentation 194 Propensity-Based Segmentation 195 Loyalty Segmentation 196 Socio-demographic and Life-Stage Segmentation 198
CONTENTS
Needs/'Attitudinal-Based Segmentation 199
Segmentation in Business Markets 200 A Guide for Behavioral Segmentation 203
Behavioral Segmentation Methodology 203
Business Understanding and Design of the Segmentation Process 203 Data Understanding, Preparation, and Enrichment 205 Identification of the Segments with Cluster Modeling 208 Evaluation and Profiling of the Revealed Segments 208 Deployment of the Segmentation Solution, Design, and Delivery of
Differentiated Strategies 211
Tips and Tricks 211
Segmentation Management Strategy 213 A Guide for Value-Based Segmentation 216
Value-Based Segmentation Methodology 216
Business Understanding and Design of the Segmentation Process 216 Data Understanding and Preparation -Calculation of the Value Measure 218 Grouping Customers According to Their Value 218 Profiling and Evaluation of the Value Segments 219 Deployment of the Segmentation Solution 219 Designing Differentiated Strategies for the Value Segments 220 Summary 223
SEGMENTATION APPLICATIONS IN BANKING 225
Segmentation for Credit Card Holders 225
Designing the Behavioral Segmentation Project 226 Building the Mining Dataset 227 Selecting the Segmentation Population 228 The Segmentation Fields 230 The Analytical Process 233
Revealing the Segmentation Dimensions 233 Identification and Profiling of Segments 237
Using the Segmentation Results 256 Behavioral Segmentation Revisited: Segmentation According to All Aspects of
Card Usage 258 The Credit Card Case Study: A Summary 263
Segmentation in Retail Banking 264
Why Segmentation? 264 Segmenting Customers According to Their Value: The Vital Few Customers 267 Using Business Rules to Define the Core Segments 268 Segmentation Using Behavioral Attributes 271
Selecting the Segmentation Fields 271
The Analytical Process 274
Identifying the Segmentation Dimensions with PCA/Factor Analysis 274 Segmenting the "Pure Mass" Customers with Cluster Analysis 276 Profiling of Segments 276
CONTENTS xi
The Marketing Process 283
Setting the Business Objectives 283
Segmentation in Retail Banking: A Summary 288
7 SEGMENTATION APPLICATIONS IN TELECOMMUNICATIONS 291
Mobile Telephony 291
Mobile Telephony Core Segments - Selecting the Segmentation Population 292 Behavioral and Value-Based Segmentation - Setting Up the Project 294 Segmentation Fields 295 Value-Based Segmentation 300 Value-Based Segments: Exploration and Marketing Usage 304 Preparing Data for Clustering - Combining Fields into Data Components 307 Identifying, Interpreting, and Using Segments 313 Segmentation Deployment 326
The Fixed Telephony Case 329 Summary 331
8 SEGMENTATION FOR RETAILERS 333
Segmentation in the Retail Industry 333 The RFM Analysis ' 334
The RFM Segmentation Procedure 338 RFM: Benefits, Usage, and Limitations 345
Grouping Customers According to the Products They Buy 346 Summary 348
FURTHER READING 349 INDEX 351
