From Probabilistic to Predictive: Methods for Mastering Customer Lifetime Value

The final chapter in a comprehensive, practical guide to real-world applications of CLV analysis & prediction

My iPad and I are back with more scrappy diagrams, in this, the final installment of my guide (for marketers and data scientists alike) to all things Customer Lifetime Value.

Welcome, once again, to my article series, “Customer Lifetime Value: the good, the bad, and everything the other CLV blog posts forgot to tell you.” It’s all based on my experience leading CLV research in a data science team in the e-commerce domain, and it’s everything I wish I’d known from the start:

• Part one discussed how to gain actionable insights from historic CLV analysis
• Part two covered real-world use cases for CLV prediction.
• Next we talked about methods for modelling historic CLV, including practical pros and cons for each.

This progression from use case examples to practical application brings us to today’s post on CLV prediction: which methods are available, and what can marketers and data scientists expect from each, when trying to apply them to their own data? We’ll look at probabilistic versus machine learning approaches, some pros and cons of each, and finish up with some thoughts on how to embark on your own CLV journey.