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bluecourses: BC8
Customer Lifetime Value Modeling

Customer Lifetime Value Modeling
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In this course, you learn the essentials of Customer Lifetime Value Modeling.
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About This Course

In this course, participants learn the essentials of Customer Lifetime Value (CLV) Modeling. We start by setting the stage and defining CLV and its key parameters. We then elaborate on RFM analysis, a key building block to many CLV models. We extensively zoom in on churn prediction: how to define it, predict it and evaluate the resulting analytical models. Next, we cover response modeling for both customer acquisition and deepening customer relationships. Markov chains are discussed as a very handy and intuitive approach for CLV modeling. We then review some probability models for CLV such as the Pareto/NBD model. Survival analysis is covered in depth since estimating accurate survival probabilities is key to calculating well-calibrated CLV values. We zoom in on profit-driven machine learning for CLV Modeling and discuss ProfLogit and ProfTree, both developed in our research group. We then briefly review some of our research on CLV modeling and churn prediction. The course concludes with some closing thoughts on the topic.

The course provides a sound mix of both theoretical and technical insights , as well as practical implementation details. These are illustrated by several real-life case studies and examples. Throughout the course, the instructors also extensively report upon their research and industry experience .

The course also features code examples in both R and Python and R/Python tutorials are also provided.

The course features more than 4 hours of video lectures, more than 100 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

See Uplift Modeling for Churn Prediction to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at: Bart@BlueCourses.com.

Price

The enrollment fee for this course is EUR 100 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Bart@BlueCourses.com. Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, R/Python scripts, quizzes and certificate).

Requirements

Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing). You should also have followed and completed our Machine Learning Essentials course.

Course Outline

  • Introduction
    • Instructor Team
    • Our Publications
    • Course Outline
    • R/Python Software
    • R/Python Tutorials
    • Disclaimer
  • Setting the stage
    • Customer Lifetime Value: Drivers
    • Customer Lifetime Value: Definition
    • Customer Lifetime Value: Key Parameters
    • Customer Equity
    • CLV Modeling: Example
    • CLV: Strategic Actions
    • Quiz
  • RFM Analysis
    • RFM Framework
    • Recency
    • Frequency
    • Monetary
    • RFM Interactions and Correlations
    • Operationalising RFM
    • RFM Usage
    • RFM Measurement Level
    • RFMPD
    • RFM Out of the Box
    • Closing Thoughts
    • Quiz
  • Churn Prediction
    • Churn Prediction: Basic Idea
    • Defining Churn
    • Types of Churn
    • Churn Prediction Analytics Model
    • Churn Prediction: Data
    • Churn Prediction: Feature Engineering
    • Churn Prediction: Target Definition
    • Churn Prediction: Analytical Techniques (with example in Python)
    • Churn Prediction: Social Network Effects
    • Analytical Churn Prediction: KPIs
    • Churn Prediction versus Churn Prevention
    • Uplift Modeling for Churn Prediction (with example in R)
    • Maximum Profit Measure: General Definition
    • Maximum Profit Measure for Churn
    • Expected Maximum Profit (EMP) (with example in R)
    • Expected Maximum Profit for Churn (EMPC)
    • Churn Prediction: Scientific Impact
    • Quiz
  • Response Modeling
    • Response Modeling: Basic Idea
    • Marketing Campaigns
    • Response Modeling: Definition of Target
    • Response Modeling: Data
    • Response Modeling: Analytical Techniques
    • Response Modeling: Evaluation
    • Response Modeling: Uplift Modeling
    • Quiz
  • Markov Chains
    • Markov Chains: Basic Idea
    • Markov Chains: Example
    • Markov Chains: Simulations (with example in Python)
    • Markov Reward Process (with example in Python)
    • Markov Decision Process Approach (with example in Python)
    • Markov Chains and Customer Heterogeneity
    • Customer Migration Mobility (with example in Python)
    • Modeling Customer Migrations
    • Markov Chains: Evaluation
    • Quiz
  • Survival Analysis
    • Survival Analysis
    • Censoring
    • Time Varying Covariates
    • Survival Distributions
    • Kaplan-Meier Analysis (with example in Python)
    • Accelerated Failure Time (AFT) Models
    • Proportional Hazards Model (Partial Likelihood) (with example in Python)
    • Discrete Survival Analysis
    • Competing Risks
    • Mixture Cure Modeling
    • Evaluating Survival Analysis Models
    • Quiz
  • Probability Models for CLV Modeling
    • Probability Models for CLV Modeling: Basic Idea
    • Pareto/NBD Submodel
    • Gamma/Gamma Submodel
    • CLV model
    • Probability Models for CLV Modeling: Evaluation
    • Quiz
  • Profit-Driven Machine Learning
    • ProfLogit: Profit Driven Logistic Regression (with example in Python)
    • ProfTree: Profit Driven Decision Trees (with example in R)
    • Quiz
  • Our Research on CLV Modeling
    • Our Scientific Research on CLV
    • Similarity Forests for Churn Prediction
    • Predicting Interpurchase Time in a Retail Environment
    • Profit based model selection using individual CLVs
    • Social Network Analytics for Churn Prediction in Telco
    • Dealing with Class Imbalance in Churn Prediction
    • Profit Driven Business Analytics
    • Predicting Time-To-Churn in Telco using Social Networks
    • Social Network Analysis for Churn
    • Churn Prediction with Bayesian Network Classifiers
    • Rule Induction for Churn Prediction
    • Monitoring and Backtesting Churn Models
    • Domain Knowledge Integration in Churn Prediction
    • Modeling Churn Using Customer Lifetime Value
    • Modified Pareto/NBD Approach for Predicting CLV
    • Bayesian Networks for Identifying Long-Life Customers
    • Bayesian Neural Networks for Repeat Purchase Modeling
    • Quiz
  • Closing Thoughts
    • Customer Accounting
    • Sample Bias
    • Model Risk
    • Deep Everything
    • Leader versus Follower
    • Privacy
    • Quiz

Course Staff

Prof. dr. Bart Baesens

Prof. dr. Bart Baesens

Bart was born in Bruges (West Flanders, Belgium) on February 27th, 1975. He speaks West-Flemish (which he is very proud of!), Dutch, French, a bit of German, some English and can order a beer in Chinese. He is married to Katrien Denys and has 3 kids (Ann-Sophie, Victor and Hannelore), and 2 cats (Felix and Simba). Besides enjoying time with his family, he is also a diehard Club Brugge soccer fan. Bart is a foodie and amateur cook. He loves drinking a good glass of wine (his favorites are white Viognier or red Cabernet Sauvignon) either in his wine cellar or when overlooking the authentic red English phone booth in his garden. His favourite pub is “In den Rozenkrans” in Kessel-Lo (close to Leuven) where you will often find him having a Gueuze Girardin 1882 or Tripel Karmeliet with a spaghetti of the house. Bart loves traveling and his favorite cities are: San Francisco, Sydney and Barcelona. He is fascinated by World War I and reads many books on the topic. He is not a big fan of being called professor Baesens (or even worse, professor Baessens), shopping (especially for clothes or shoes), pastis (or other anise-flavored drinks), vacuum cleaning (he can’t bare the sound), students chewing gum during their oral exam of Credit Risk Modeling (or had garlic for breakfast), long meetings (> 30 minutes), phone calls (asynchronous e-mail communication is a lot more efficient!), admin (e.g., forms and surveys) or French fries (Belgian fries are a lot better!). He is often praised for his sense of humor, although he is usually more modest about this. Bart is also a professor of Big Data and 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 has written more than 250 scientific papers, some of which have been published in well-known international journals (e.g., MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at top international conferences (e.g., ICIS, KDD, CAISE). He has received various best paper and best speaker awards. Bart is the author of 8 books: Credit Risk Management: Basic Concepts (Oxford University Press, 2009), Analytics in a Big Data World (Wiley, 2014), Beginning Java Programming (Wiley, 2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (Wiley, 2015), Credit Risk Analytics (Wiley, 2016), Profit Driven Business Analytics (Wiley, 2017), Web Scraping for Data Science with Python (Apress, 2018), and Principles of Database Management (Cambridge University Press, 2018). He sold more than 25.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean. His research is summarized at www.dataminingapps.com. For an overview of the courses he is teaching, see www.bartbaesens.com. He also regularly tutors, advises and provides consulting support to international firms regarding their big data, analytics and credit risk management strategy.

Prof. dr. Tim Verdonck

Prof. dr. Tim Verdonck

Tim was born in Merksem (Antwerp, Belgium) on February 19, 1983. He lives together with his girlfriend Nuria Baeten, his daughter Oona, his dog Ragna and two cats Nello and Patrasche (the names of the cats come from the novel A Dog of Flanders, which takes place in Hoboken and Antwerp, see www.visitantwerpen.be). He lives in Wilrijk (Antwerp, Belgium) and enjoys relaxing in his garden with his family. He loves travelling and his favorite cities are Barcelona and Vancouver. On holidays, he likes to dive (his favorite place is Sipadan in Malaysia), snowboard and wakeboard. His other favorite sports are tennis and football.

Tim Verdonck is also a professor of Statistics and Data Science at the Department of Mathematics of University of Antwerp (Belgium). He is affiliated to KU Leuven and has been an invited professor at the University of Bologna, teaching advanced non-life insurance in the Master of Quantitative Finance. He is chairholder of the BNP Paribas Fortis Chair on Fraud Analytics, the Allianz Chair on Prescriptive Business Analytics in Insurance and the BASF Chair on Robust Predictive Analytics. Tim has a degree in Mathematics and a PhD in Science: Mathematics, obtained at the University of Antwerp. During his PhD he successfully took the Master in Insurance and the Master in Financial and Actuarial Engineering, both at KU Leuven. His research interests are in the development and application of robust statistical methods for financial, actuarial and economic data sets. He is associate editor of Statistics: A Journal of Theoretical and Applied Statistics (Taylor & Francis) and Computational Statistics & Data Analysis (Elsevier). Tim is co-organizer of the Data Science Meetups in Leuven and managing partner at Boltzmann (www.boltzmann.be), a team of experts in machine learning that transform data into actionable insights.

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