Data Mining Techniques in CRM

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1 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 A John Wiley and Sons, Ltd., Publication

2 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

3 viii CONTENTS 3 DATA MINING TECHNIQUES FOR SEGMENTATION 65 Segmenting Customers with Data Mining Techniques 65 Principal Components Analysis 65 PCA 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 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 Product Codes 144

4 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 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

5 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

6 xi The Marketing Process 283 Setting the Business Objectives 283 Segmentation in Retail Banking: A Summary 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 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

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