Statistical Methods in Customer Relationship Management

Statistical Methods in Customer Relationship Management:

• Provides an overview of a CRM system, introducing key concepts and metrics needed to understand and implement these models.
• Focuses on five CRM models: customer acquisition, customer retention, customer churn, and customer win back with supporting case studies.
• Explores each model in detail, from investigating the need for CRM models to looking at the future of the models.
• Presents models and concepts that span across the introductory, advanced, and specialist levels.

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.

Preface ix

1 Customer relationship management 1

1.1 Introduction 1

1.2 What is CRM? 2

1.3 What is needed to implement CRM strategies? 3

1.3.1 Database 3

1.3.2 Technology 6

1.3.3 Metrics 7

1.4 Analytical methods 9

1.5 Conclusion 9

References 10

2 CRM in action 11

2.1 Introduction 11

2.2 The importance of customer acquisition 13

2.3 The significance of customer retention 15

2.4 The impact of customer churn 17

2.5 The benefits of customer win-back 18

2.6 Conclusion 20

References 20

3 Customer acquisition 22

3.1 Introduction 22

3.1.1 Data for empirical examples 27

3.2 Response probability 28

3.2.1 Empirical example: Response probability 32

3.2.2 How do you implement it? 34

3.3 Number of newly acquired customers and initial order quantity 35

3.3.1 Empirical example: Number of newly acquired customers 37

3.3.2 How do you implement it? 38

3.3.3 Empirical example: Initial order quantity 39

3.3.4 How do you implement it? 42

3.4 Duration/time 42

3.4.1 Empirical example: Duration/time 44

3.4.2 How do you implement it? 46

3.5 Firm’s performance (LTV, CLV, and CE) 47

3.5.1 Empirical example: Firm’s performance 49

3.5.2 How do you implement it? 52

3.6 Chapter summary 52

Customer acquisition – SAS code 53

Customer acquisition – SAS output 55

References 61

4 Customer retention 63

4.1 Introduction 63

4.1.1 Data for empirical examples 66

4.2 Repurchase or not (stay or leave) 69

4.2.1 Will a customer repurchase? 69

4.2.2 When will a customer no longer repurchase? 71

4.2.3 Empirical example: Repurchase or not (stay or leave) 73

4.2.4 How do you implement it? 78

4.3 Lifetime duration 78

4.3.1 Empirical example: Lifetime duration 83

4.3.2 How do you implement it? 85

4.4 Order quantity and order size 85

4.4.1 How much (in $) will a customer order? 85

4.4.2 How many items will a customer order? 86

4.4.3 What is the average order size? 87

4.4.4 Empirical example: Order quantity 87

4.4.5 How do you implement it? 91

4.5 Cross-buying 91

4.5.1 Empirical example: Cross-buying 92

4.5.2 How do you implement it? 97

4.6 SOW 97

4.6.1 Empirical example: SOW 98

4.6.2 How do you implement it? 101

4.7 Profitability (CLV) 102

4.7.1 Empirical example: Profitability (CLV) 103

4.7.2 How do you implement it? 105

4.8 Chapter summary 105

Customer retention – SAS code 106

Customer retention – SAS output 111

References 119

5 Balancing acquisition and retention 121

5.1 Introduction 121

5.1.1 Data for empirical examples 122

5.2 Acquisition and retention 124

5.2.1 Empirical example: Balancing acquisition and retention 128

5.3 Optimal resource allocation 137

5.3.1 How do you implement it? 140

5.4 Chapter summary 141

Balancing acquisition and retention – SAS code 142

Balancing acquisition and retention – SAS output 144

References 147

6 Customer churn 149

6.1 Introduction 149

6.1.1 Data for empirical examples 150

6.2 Customer churn 151

6.2.1 Empirical example: Customer churn 156

6.2.2 How do you implement it? 161

6.3 Chapter summary 161

Customer churn – SAS code 162

Customer churn – SAS output 163

References 164

7 Customer win-back 166

7.1 Introduction 166

7.1.1 Data for empirical examples 167

7.2 Customer win-back 168

7.2.1 Empirical example: Customer win-back 169

7.2.2 How do you implement it? 178

7.3 Chapter summary 179

Customer win-back – SAS code 180

Customer win-back – SAS output 182

References 185

8 Implementing CRM models 186

8.1 Introduction 186

8.2 CLV measurement approach 187

8.3 CRM implementation at IBM 190

8.3.1 IBM background 190

8.3.2 Implementing a CLV management framework at IBM 191

8.4 CRM implementation at a B2C firm 202

8.4.1 The focal firm background 202

8.4.2 Implementing the CLV management framework at a fashion retailer 202

8.4.3 Process to implement the CLV management framework at a fashion retailer 203

8.5 Challenges in implementing the CLV management framework 219

8.5.1 Challenges in data collection and internal collaboration 219

8.5.2 Challenges in implementing the customer-centric approach 220

References 222

9 The future of CRM 223

9.1 Introduction 223

9.2 Social media 223

9.3 Mobile marketing 226

9.4 Customized marketing campaigns 227

9.5 Conclusion 229

References 229

Appendix A: Maximum likelihood estimation 230

Appendix B: Log-linear model—an introduction 232

Appendix C: Vector autoregression modeling 235

Appendix D: Accelerated lifetime model 241

Appendix E: Type-1 Tobit model 244

Appendix F: Multinomial logit model 246

Appendix G: Survival analysis – an introduction 249

Appendix H: Discrete-time hazard 252

Appendix I: Proportional hazards model 254

Appendix J: Random intercept model 257

Appendix K: Poisson regression model 260

Appendix L: Negative binomial regression 262

Appendix M: Estimation of Tobit model with selection 265

Index 267

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.