Congrats on your CLV prediction model — now what are you going to do with it?

An obsessively detailed guide to Customer Lifetime Value techniques and real-world applications

I’m a data scientist spilling everything I know about Customer Lifetime Value: how to model it, predict it, and actually make use of it. This is part two of three, and it’s all about use cases for CLV prediction. Your mission, should you choose to accept it, is to take my ideas and run with them. Image source: Author provided.

Call me crazy, but I’ve challenged myself to create the most extensive guide to Customer Lifetime Value (CLV) out there. Codenamed “everything the other tutorials left out”, I’m sharing all the ideas and learnings I gained while working on this topic in a real-world data science team, with imperfect data, and complicated client needs.

My last post featured an ever-overlooked topic: use-cases for historic CLV calculation. It went a little viral, so I guess I’m onto something. In this post I’ll discuss:

• Some essential terminology
• The goal of CLV prediction
• CLV prediction uses (going beyond the standard examples, I promise!)

And in upcoming posts, we’ll talk about CLV calculation and prediction methods, their pros and cons, and lessons learned on how to use them correctly.

There’s loads to cover, so let’s get into it!

Laying the Groundwork

Whether you’re a data scientist, analyst or marketer, you need domain knowledge when embarking on…