Sale!

Statistical Methods in Customer Relationship Management 1st Edition by Kumar, Andrew Petersen 1119993202 9781119993209

Original price was: $50.00.Current price is: $35.00.

Statistical Methods in Customer Relationship Management 1st Edition V. Kumar Digital Instant Download

Author(s): V. Kumar, J. Andrew Petersen
ISBN(s): 9781119993209, 1119993202
Edition: 1
File Details: PDF, 3.39 MB
Year: 2012
Language: english
SKU: EB-4589170 Category: Tags: ,

Statistical Methods in Customer Relationship Management 1st Edition by Kumar, Andrew Petersen – Ebook PDF Instant Download/Delivery: 1119993202, 9781119993209

Full download Statistical Methods in Customer Relationship Management 1st Edition after payment

Product details:

ISBN 10: 1119993202

ISBN 13: 9781119993209

Author: V. Kumar, J. Andrew Petersen

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 Customer relationship management

1.1 Introduction

1.2 What is CRM?

1.3 What is needed to implement CRM strategies?

1.3.1 Database

1.3.1.1 Categories of databases

1.3.1.2 Sources of databases

1.3.2 Technology

1.3.3 Metrics

1.4 Analytical methods

1.5 Conclusion

References

2 CRM in action

2.1 Introduction

2.2 The importance of customer acquisition

2.3 The significance of customer retention

2.4 The impact of customer churn

2.5 The benefits of customer win-back

2.6 Conclusion

References

3 Customer acquisition

3.1 Introduction

3.1.1 Data for empirical examples

3.2 Response probability

3.2.1 Empirical example: Response probability

3.2.2 How do you implement it?

3.3 Number of newly acquired customers and initial order quantity

3.3.1 Empirical example: Number of newly acquired customers

3.3.2 How do you implement it?

3.3.3 Empirical example: Initial order quantity

3.3.4 How do you implement it?

3.4 Duration/time

3.4.1 Empirical example: Duration/time

3.4.2 How do you implement it?

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

3.5.1 Empirical example: Firm’s performance

3.5.2 How do you implement it?

3.6 Chapter summary

Customer acquisition – SAS code

Customer acquisition – SAS output

References

4 Customer retention

4.1 Introduction

4.1.1 Data for empirical examples

4.2 Repurchase or not (stay or leave)

4.2.1 Will a customer repurchase?

4.2.2 When will a customer no longer repurchase?

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

4.2.4 How do you implement it?

4.3 Lifetime duration

4.3.1 Empirical example: Lifetime duration

4.3.2 How do you implement it?

4.4 Order quantity and order size

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

4.4.2 How many items will a customer order?

4.4.3 What is the average order size?

4.4.4 Empirical example: Order quantity

4.4.5 How do you implement it?

4.5 Cross-buying

4.5.1 Empirical example: Cross-buying

4.5.2 How do you implement it?

4.6 SOW

4.6.1 Empirical example: SOW

4.6.2 How do you implement it?

4.7 Profitability (CLV)

4.7.1 Empirical example: Profitability (CLV)

4.7.2 How do you implement it?

4.8 Chapter summary

Customer retention – SAS code

Customer retention – SAS output

References

5 Balancing acquisition and retention

5.1 Introduction

5.1.1 Data for empirical examples

5.2 Acquisition and retention

5.2.1 Empirical example: Balancing acquisition and retention

5.2.1.1 Acquisition model

5.2.1.2 Duration model

5.2.1.3 Profit model

5.3 Optimal resource allocation

5.3.1 How do you implement it?

5.4 Chapter summary

Balancing acquisition and retention – SAS code

Balancing acquisition and retention – SAS output

References

6 Customer churn

6.1 Introduction

6.1.1 Data for empirical examples

6.2 Customer churn

6.2.1 Empirical example: Customer churn

6.2.2 How do you implement it?

6.3 Chapter summary

Customer churn – SAS code

Customer churn – SAS output

References

7 Customer win-back

7.1 Introduction

7.1.1 Data for empirical examples

7.2 Customer win-back

7.2.1 Empirical example: Customer win-back

7.2.1.1 Reacquisition model

7.2.1.2 Second duration model

7.2.1.3 SCLV Model

7.2.2 How do you implement it?

7.3 Chapter summary

Customer win-back – SAS code

Customer win-back – SAS output

References

8 Implementing CRM models

8.1 Introduction

8.2 CLV measurement approach

8.3 CRM implementation at IBM

8.3.1 IBM background

8.3.2 Implementing a CLV management framework at IBM

8.3.2.1 Stage 1: Model development

8.3.2.2 Stage 2: Model implementation

8.4 CRM implementation at a B2C firm

8.4.1 The focal firm background

8.4.2 Implementing the CLV management framework at a fashion retailer

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

8.4.3.1 Stage 1: Model development

8.4.3.2 Stage 2: Model implementation

8.4.3.3 Stage 3: Tactics and strategy recommendations

8.5 Challenges in implementing the CLV management framework

8.5.1 Challenges in data collection and internal collaboration

8.5.2 Challenges in implementing the customer-centric approach

8.5.2.1 Changes to operational elements

8.5.2.2 Changes to workforce elements

References

9 The future of CRM

9.1 Introduction

9.2 Social media

9.3 Mobile marketing

9.4 Customized marketing campaigns

9.5 Conclusion

References

Appendix A: Maximum likelihood estimation

Reference

Appendix B: Log-linear model—an introduction

Reference

Appendix C: Vector autoregression modeling

C.1 Unit-root testing: Are performance and marketing variables stable or evolving?

C.2 Cointegration tests: Does a long-run equilibrium exist between evolving series?

C.3 Var models: How to capture the dynamics in a system of variables?

C.4 Impulse-response function derivation

C.5 Impulse-response functions: Mathematical derivations

References

Appendix D: Accelerated lifetime model

Reference

Appendix E: Type-1 Tobit model

Reference

Appendix F: Multinomial logit model

References

Appendix G: Survival analysis – an introduction

References

Appendix H: Discrete-time hazard

Reference

Appendix I: Proportional hazards model

References

Appendix J: Random intercept model

References

Appendix K: Poisson regression model

Reference

Appendix L: Negative binomial regression

Reference

Appendix M: Estimation of Tobit model with selection

References

People also search:

statistical relationships examples

relationship management strategies pdf

customer relationship statistics

statistical methods in customer relationship management pdf

Tags: Kumar, Andrew Petersen, Statistical, Methods, Relationship