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Statistical Methods in Customer Relationship Management

Book Description

Statistical Methods in Customer Relationship Management focuses on the quantitative and modeling aspects of customer management strategies that lead to future firm profitability, with emphasis on developing an understanding of Customer Relationship Management (CRM) models as the guiding concept for profitable customer management. To understand and explore the functioning of CRM models, this book traces the management strategies throughout a customer's tenure with a firm. Furthermore, the book explores in detail CRM models for customer acquisition, customer retention, customer acquisition and retention, customer churn, and customer win back.

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
    1. 1 Need for this Book
    2. 2 Supplements to the Book
    3. 3 Organization of the Book
    4. Acknowledgments
  6. Chapter 1: Customer Relationship Management
    1. 1.1 Introduction
    2. 1.2 What is CRM?
    3. 1.3 What is Needed to Implement CRM Strategies?
    4. 1.4 Analytical Methods
    5. 1.5 Conclusion
    6. References
  7. Chapter 2: CRM in Action
    1. 2.1 Introduction
    2. 2.2 The Importance of Customer Acquisition
    3. 2.3 The Significance of Customer Retention
    4. 2.4 The Impact of Customer Churn
    5. 2.5 The Benefits of Customer Win-back
    6. 2.6 Conclusion
    7. References
  8. Chapter 3: Customer Acquisition
    1. 3.1 Introduction
    2. 3.2 Response Probability
    3. 3.3 Number of Newly Acquired Customers and Initial Order Quantity
    4. 3.4 Duration/Time
    5. 3.5 Firm's Performance (LTV, CLV, and CE)
    6. 3.6 Chapter Summary
    7. Customer acquisition – SAS code
    8. Customer acquisition – SAS output
    9. References
  9. Chapter 4: Customer Retention
    1. 4.1 Introduction
    2. 4.2 Repurchase or Not (Stay or Leave)
    3. 4.3 Lifetime Duration
    4. 4.4 Order Quantity and Order Size
    5. 4.5 Cross-buying
    6. 4.6 SOW
    7. 4.7 Profitability (CLV)
    8. 4.8 Chapter Summary
    9. Customer retention – SAS code
    10. Customer retention – SAS output
    11. References
  10. Chapter 5: Balancing Acquisition and Retention
    1. 5.1 Introduction
    2. 5.2 Acquisition and Retention
    3. 5.3 Optimal Resource Allocation
    4. 5.4 Chapter Summary
    5. 5.5 Balancing acquisition and retention – SAS code
    6. 5.6 Balancing acquisition and retention – SAS output
    7. References
  11. Chapter 6: Customer Churn
    1. 6.1 Introduction
    2. 6.2 Customer Churn
    3. 6.3 Chapter Summary
    4. Customer churn – SAS Code
    5. Customer churn – SAS Output
    6. References
  12. Chapter 7: Customer Win-back
    1. 7.1 Introduction
    2. 7.2 Customer win-back
    3. 7.3 Chapter Summary
    4. Customer win-back – SAS code
    5. Customer win-back – SAS output
    6. References
  13. Chapter 8: Implementing CRM Models
    1. 8.1 Introduction
    2. 8.2 CLV Measurement Approach
    3. 8.3 CRM Implementation at IBM
    4. 8.4 CRM Implementation at a B2C Firm
    5. 8.5 Challenges in Implementing the CLV Management Framework
    6. References
  14. Chapter 9: The Future of CRM
    1. 9.1 Introduction
    2. 9.2 Social Media
    3. 9.3 Mobile Marketing
    4. 9.4 Customized Marketing Campaigns
    5. 9.5 Conclusion
    6. References
  15. Appendix A: Maximum Likelihood Estimation
    1. References
  16. Appendix B: Log-linear Model—An Introduction
    1. References
  17. Appendix C: Vector Autoregression Modeling
    1. C.1 Unit-Root Testing: Are Performance and Marketing Variables Stable or Evolving?
    2. C.2 Cointegration Tests: Does a Long-Run Equilibrium Exist between Evolving Series?
    3. C.3 VAR Models: How to Capture the Dynamics in a System of Variables?
    4. C.4 Impulse-Response Function Derivation
    5. C.5 Impulse-Response Functions: Mathematical Derivations
    6. References
  18. Appendix D: Accelerated Lifetime Model
    1. References
  19. Appendix E: Type-1 Tobit Model
    1. References
  20. Appendix F: Multinomial Logit Model
    1. References
  21. Appendix G: Survival Analysis – An Introduction
    1. References
  22. Appendix H: Discrete-Time Hazard
    1. References
  23. Appendix I: Proportional Hazards Model
    1. References
  24. Appendix J: Random Intercept Model
    1. References
  25. Appendix K: Poisson Regression Model
    1. References
  26. Appendix L: Negative Binomial Regression
    1. References
  27. Appendix M: Estimation of Tobit Model with Selection
    1. References
  28. Index