Statistical Methods in Customer Relationship Management

Dr. V. Kumar, Center for Excellence in Brand and Customer Management (CEBCM), and Director of the PhD Program in Marketing, J. Mack Robinson College of Business, Georgia State University, Atlanta, USA. Dr. J. Andrew Petersen, Marketing and Assistant Director for the Center of Integrated Marketing and Sales (CIMS), Kenan-Flagler Business School, University of North Carolina at Chapel Hill, USA.

“Academics and practitioners involved in the area of CRM as well as instructors of applied statistics and quantitative marketing courses will benefit from this book.”  (Zentralblatt MATH, 1 May 2013)

• Preface ix1 Customer relationship management 11.1 Introduction 11.2 What is CRM? 21.3 What is needed to implement CRM strategies? 31.3.1 Database 31.3.2 Technology 61.3.3 Metrics 71.4 Analytical methods 91.5 Conclusion 9References 102 CRM in action 112.1 Introduction 112.2 The importance of customer acquisition 132.3 The significance of customer retention 152.4 The impact of customer churn 172.5 The benefits of customer win-back 182.6 Conclusion 20References 203 Customer acquisition 223.1 Introduction 223.1.1 Data for empirical examples 273.2 Response probability 283.2.1 Empirical example: Response probability 323.2.2 How do you implement it? 343.3 Number of newly acquired customers and initial order quantity 353.3.1 Empirical example: Number of newly acquired customers 373.3.2 How do you implement it? 383.3.3 Empirical example: Initial order quantity 393.3.4 How do you implement it? 423.4 Duration/time 423.4.1 Empirical example: Duration/time 443.4.2 How do you implement it? 463.5 Firm’s performance (LTV, CLV, and CE) 473.5.1 Empirical example: Firm’s performance 493.5.2 How do you implement it? 523.6 Chapter summary 52Customer acquisition – SAS code 53Customer acquisition – SAS output 55References 614 Customer retention 634.1 Introduction 634.1.1 Data for empirical examples 664.2 Repurchase or not (stay or leave) 694.2.1 Will a customer repurchase? 694.2.2 When will a customer no longer repurchase? 714.2.3 Empirical example: Repurchase or not (stay or leave) 734.2.4 How do you implement it? 784.3 Lifetime duration 784.3.1 Empirical example: Lifetime duration 834.3.2 How do you implement it? 854.4 Order quantity and order size 854.4.1 How much (in $) will a customer order? 854.4.2 How many items will a customer order? 864.4.3 What is the average order size? 874.4.4 Empirical example: Order quantity 874.4.5 How do you implement it? 914.5 Cross-buying 914.5.1 Empirical example: Cross-buying 924.5.2 How do you implement it? 974.6 SOW 974.6.1 Empirical example: SOW 984.6.2 How do you implement it? 1014.7 Profitability (CLV) 1024.7.1 Empirical example: Profitability (CLV) 1034.7.2 How do you implement it? 1054.8 Chapter summary 105Customer retention – SAS code 106Customer retention – SAS output 111References 1195 Balancing acquisition and retention 1215.1 Introduction 1215.1.1 Data for empirical examples 1225.2 Acquisition and retention 1245.2.1 Empirical example: Balancing acquisition and retention 1285.3 Optimal resource allocation 1375.3.1 How do you implement it? 1405.4 Chapter summary 141Balancing acquisition and retention – SAS code 142Balancing acquisition and retention – SAS output 144References 1476 Customer churn 1496.1 Introduction 1496.1.1 Data for empirical examples 1506.2 Customer churn 1516.2.1 Empirical example: Customer churn 1566.2.2 How do you implement it? 1616.3 Chapter summary 161Customer churn – SAS code 162Customer churn – SAS output 163References 1647 Customer win-back 1667.1 Introduction 1667.1.1 Data for empirical examples 1677.2 Customer win-back 1687.2.1 Empirical example: Customer win-back 1697.2.2 How do you implement it? 1787.3 Chapter summary 179Customer win-back – SAS code 180Customer win-back – SAS output 182References 1858 Implementing CRM models 1868.1 Introduction 1868.2 CLV measurement approach 1878.3 CRM implementation at IBM 1908.3.1 IBM background 1908.3.2 Implementing a CLV management framework at IBM 1918.4 CRM implementation at a B2C firm 2028.4.1 The focal firm background 2028.4.2 Implementing the CLV management framework at a fashion retailer 2028.4.3 Process to implement the CLV management framework at a fashion retailer 2038.5 Challenges in implementing the CLV management framework 2198.5.1 Challenges in data collection and internal collaboration 2198.5.2 Challenges in implementing the customer-centric approach 220References 2229 The future of CRM 2239.1 Introduction 2239.2 Social media 2239.3 Mobile marketing 2269.4 Customized marketing campaigns 2279.5 Conclusion 229References 229Appendix A: Maximum likelihood estimation 230Appendix B: Log-linear model—an introduction 232Appendix C: Vector autoregression modeling 235Appendix D: Accelerated lifetime model 241Appendix E: Type-1 Tobit model 244Appendix F: Multinomial logit model 246Appendix G: Survival analysis – an introduction 249Appendix H: Discrete-time hazard 252Appendix I: Proportional hazards model 254Appendix J: Random intercept model 257Appendix K: Poisson regression model 260Appendix L: Negative binomial regression 262Appendix M: Estimation of Tobit model with selection 265Index 267