From analytics to actual application: the case of Customer Lifetime Value

Part one of a comprehensive, practical guide to CLV techniques and real-world use-cases

Warning: This might be the most extensive guide to CLV you’ll ever come across, and it’s all informed by my experience working with it. So strap in. (Or watch my 60 second summary, if you don’t mind missing the juicy bits). Source: Smarter Ecommerce.

Whether you’re a data scientist, a marketer or a data leader, chances are that if you’ve Googled “Customer Lifetime Value”, you’ve been disappointed. I felt that too, back when I was helping lead a new CLV research project in a data science team in the e-commerce domain. We went looking for state-of-the-art methods, but Google returned only basic tutorials with unrealistically manicured datasets, and marketing ‘fluff’ posts describing vague and unimaginative uses for CLV. There was nothing about the pros and cons of available methods when applied to real world data, and with real world clients. We learned all that on our own, and now I want to share it.

Presenting: all the stuff the CLV tutorials left out.

In this post, I’ll cover:

• What is CLV? (I’ll be brief, as this part you probably already know)
• Do you really need CLV prediction? Or can you start with historic CLV calculation?
• What can your company already gain from historic CLV information, especially when you combine it with other business data?