The Bayesian Approach to Deterministic Customer Lifetime Value Models

The Bayesian Approach to Deterministic Customer Lifetime Value Models

Presented at the 2023 BayesiaLab Conference.


Identified factors that drive customer lifetime value and components that drive higher CLV to drive revenue and improve customer retention. Using data from this study, we determine the strength of the relationship between Opportunity, Lifetime Value, and Customer Ratings. Graphs, causal insights, and early warning signals are generated through network analysis. As a result, strategies are better planned, and gaps are identified and addressed more efficiently. Surveys are conducted regularly to understand how to serve our most valuable customers better. By improving customer experience (CX), we are able to increase their lifetime value (LTV). This method also enables us to make precise recommendations about how to improve Client Economics. The Bayesian implementation offers some advantages, which help in more precision and enhanced flexibility in bringing different data sets together with actionable insights.

Presentation Video

Presentation Slides

About the Presenters

Anand Wilson, Lead Data Science Consultant, Advanced Analytics, Course5 Intelligence

Anand has 9+ years of experience in applied artificial intelligence and data sciences. He has worked for marque clients such as Lenovo, Intel, Microsoft, Novartis, Novo Nordisk, GE, Mars Wrigley, PepsiCo, etc., enabling digital transformation using A.I.

In his current role, Anand focuses on developing and marketing solutions based on the Bayesian Network model theory, which enables us to quantify causality in an observational study. His major areas of work/research include Knowledge Modelling, Machine Learning with BayesiaLab, and Inference.

Anand comes from an applied statistics background. He has a master's degree in statistics. Anand carries an acute interest in machine reasoning, causal inference, and experimental designs, along with machine learning and data science.

Buvana Iyer, Principal Solution Architect, Discovery Solution, Course5 Intelligence

Over 15 years of international and domestic market experience with a proven track record of leading high-profile strategic projects on a fast-moving set of priorities and business initiatives to translate strategic organizational goals into clear operational plans and derive measurable results.

Buvana consults C-level clients and has led projects achieving org-level implementation of enterprise analytics, including Software Development, BI & Analytics, and ML & AI solutions (at scale), adopting DevOps philosophy with agile delivery. Expertise in leveraging both traditional statistics and machine learning techniques to create solutions and deliver business value.

Buvana comes from applied Mathematics background. She has a master's degree in Mathematics. Buvana carries an acute interest in predictive analytics, statistical modeling, machine reasoning, and experimental designs, along with machine learning and data science.\

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