Lessons and Experiences from Industry and Research on how to become a Customer-Centric Organisation

A managerial guide to deal with today's challenges in CLV modeling!

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Get up to speed on measuring and modeling Customer Lifetime Value!

Managing Customer Lifetime Value provides marketeers, data science practitioners, business professionals and analytics managers with a comprehensive guide to understand, model, analyze, manage and deploy Customer Lifetime Value!

Providing state of the art industry and research insights based on the author’s extensive experience, this illustrated textbook has a well-balanced theory-practice focus and covers all essential topics.

Download a FREE SAMPLE CHAPTER.

Key Features:

Extensive coverage of all topics related to CLV modeling, starting from the data, the modeling, the monitoring and concluding with the deployment.
Various examples and case studies to highlight the topics discussed.
Key references to background literature for further clarification.
Code examples and case studies in R/Python.
An online website with various add-ons and recent developments: www.clvbook.com.

What Makes this Book Different?

This book is based on the unique complimentary experience of both authors having worked in (customer) analytics for more than 30 years combined, both in industry and academia. More specifically, both authors have co-authored more than 300 scientific publications and various books on the topics covered in this book and have worked with firms in different industries, including (online) retailers, financial institutions, manufacturing firms, insurance providers, NFP organisations, governments, etc. all over the globe estimating, validating, deploying, governing and monitoring analytical Customer Lifetime Value models.

The authors wrote this book with a very pragmatic focus in mind. In other words, the concepts, methods and techniques covered try to balance out a mix between sound and solid proven theories on the one hand and practical applicability on the other hand. Hence, we deliberately don't focus on overly complex techniques based on heavy mathematical underpinnings with limited to zero added business-value.

The book also comes with a web site which features various data sets and R/Python code to illustrate the techniques and approaches discussed. This will allow practitioners to efficiently and swiftly try out what they have learned in their own business areas.

Preface
- About this Book
- What Makes this Book Different?
- Who this Book is For?
- Structure of the Book
- Additional Learning Material
- About the Authors
Chapter 1: Introduction to Customer Lifetime Value
- Overview
- Setting the stage
- Definition
- Key Parameters
- Customer Equity
- Industry adoption
- Marketing Actions to Optimize CLV
- Approaches to model CLV
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 2: The CLV Analytical Toolkit
- Overview
- The Analytical Process Model
- Data Preprocessing
- Linear Regression
- Logistic Regression
- Decision Trees
- Ensemble methods
- Random Forests
- XGBoost
- Evaluating Predictive Analytical Models
- Clustering
- Association Rules
- Sequence Rules
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 3: The RFM Framework
- Overview
- Basic Idea
- Recency
- Frequency
- Monetary
- RFM correlations
- Operationalizing RFM
- RFM Usage
- RFM Extensions
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 4: Customer Acquisition
- Overview
- Basic Idea
- Target Definition
- Data
- Developing a customer acquisition model
- Evaluating customer acquisition models
- Closing thoughts
- Applications in Python/R
- Quiz
Chapter 5: Response Modeling
- Overview
- Basic Idea
- Marketing Campaigns
- Target Definition
- Data
- Feature Engineering
- Developing Response models
- Uplift Modeling
- Cross-, Up- and Down-Selling
- Campaign Management
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 6: Churn Prediction
- Overview
- Basic Idea
- Target Definition
- Data
- Developing Churn Prediction Models
- Social Networks
- Uplift Modeling
- Churn Prediction Versus Churn Prevention
- Profit-Driven Evaluation
- Profit Driven Classification
- Our Research on Churn Prediction
- Closing Thoughts
- Application in Python/R
- Quiz

Chapter 7: Markov Chains
- Overview
- Basic Idea
- Example
- Simulations
- Markov Reward Process
- Markov Decision Process
- Customer Heterogeneity
- Customer Migration Mobility
- Modeling Customer Migrations
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 8: Customer Journey Analysis
- Overview
- Basic Idea
- Challenges
- Process Mining
- On-Line Customer Journey Analysis
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 9: Probability Models
- Overview
- Basic Idea
- Pareto NBD Model
- Gamma/Gamma submodel
- CLV Model
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 10: Market Segmentation
- Overview
- Basic idea
- Criteria for successful market segmentation
- Segmentation bases
- Segmentation methods
- Rule based methods
- Clustering methods
- Mixture methods
- Artificial neural networks
- Determining the number of segments
- Elbow method
- Indices
- Cross validation
- Profiling
- Segmentation and profiling
- Using segmentation to improve customer scoring
- Application in Python/R
- Closing Thoughts
- Quiz
Chapter 11: Recommender Systems
- Overview
- Basic Idea
- Business Value
- Examples
- Impact
- Items And Users
- Personalized versus Unpersonalized Recommendations
- Challenges
- User Interest
- Rating Matrix
- Recommender System Workings
- Evaluating Recommender Systems
- User-User Collaborative Filtering
- Item-Item Collaborative Filtering
- User-User Versus Item-Item Collaborative Filtering
- Collaborative Filtering Evaluated
- Closing Thoughts
- Application in Python/R
- Quiz
Chapter 12: Deploying, Governing and Monitoring CLV Models
- Overview
- CLV Model Deployment
- CLV Model Governance
- Open Source versus Commercial Software
- CLV Model Documentation
- CLV Model Monitoring
- Privacy and Security
- Closing Thoughts
- Application in Python/R
- Quiz

Audience

This book is for anyone who is curious to know more about modeling Customer Lifetime Value or intrigued to make his/her organisation fully customer-centric. A first target audience consists of business practitioners across all industries where customers are considered a key asset. Example reader profiles are marketeers, customer/brand/channel/relationship managers, marketing and data scientists. Also consultants may find our book useful to help their clients in their CLV efforts. C-level executives (e.g., Chief Executive Officers, Chief Marketing Officers, Chief Analytics Officers, Chief Data Officers) as well as tactical and operational levels may benefit from reading this book to be more closely aligned with the data scientists, marketing modelers and analysts directly working on modeling CLV.

Secondly, the book can also used as a handbook by academics teaching courses on the topic, both undergraduate as well as postgraduate. It features various handy add-ons such as multiple choice questions at the end of each chapter, worked out case studies in Python and R, references to background literature and links to ON-LINE courses which can help facilitate the learning experience.

For those who are just starting to find their way around in analytics, we are convinced that this book can be an important guide to help you use it for CLV modeling, but would advice to first briefly refresh your knowledge on descriptive statistics (e.g., mean, standard deviation, confidence intervals, hypothesis testing) so as to maximize your reading experience.

R/Python examples

R Examples

Python Examples

Chapter 3 RFM Analysis

About the Authors

Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics. He co-authored more than 300 scientific papers and ten books. Bart received the OR Society's Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award for best EJOR paper. His research is summarized at dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy. Bart is listed in Stanford University's new Database of Top Scientists in the World. He was also named one of the World's top educators in Data Science by CDO magazine in 2021. He is also co-founder of BlueCourses (bluecourses.com), an on-line training platform providing courses on Machine Learning, Fraud Analytics, Credit Risk Modeling, Deep Learning, etc.

Professor Arno De Caigny is professor of business analytics at the triple crown accredited IÉSEG School of Management, Catholic university of Lille and member of the research laboratory LEM (UMR CNRS 9221). Before starting his academic career, he worked as an analytical consultant for Deloitte. His research focuses on improving decision-making in companies through the use of data and quantitative methods. He has vast experience in applying machine learning to solve challenges in the broad marketing domain. He has led numerous projects in various industries, such as financial services, retailing, software, that required customer lifetime value modeling to solve business problems. He has published in internationally renowned and peer-reviewed journals such as European Journal of Operational Research, Decision Support Systems, International Journal of Forecasting and Industrial Marketing Management. He also developed a custom machine learning algorithm that is both comprehensible and accurate, to improve customer retention decision making. This work is one of the top 10 most cited papers in European Journal of Operational Research since 2018.

We hope you enjoy reading through this book as much as we enjoyed writing it. We're always happy to hear feedback and remarks from our readers and can be contacted by email at:
— Bart Baesens, Bart.Baesens@kuleuven.be
— Arno De Caigny, A.De-Caigny@ieseg.fr