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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
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?
In the rest of the series, I’ll present:
- Uses for CLV prediction
- Methods for calculating and predicting CLV, and their advantages and disadvantages (my LinkedIn Learning course — Machine Learning in Marketing — also touches on this)
- Lessons learned on how to use them correctly.
And I’ll sprinkle some data science best-practices throughout. Sound like a plan? Great, let’s go!
What is Customer Lifetime Value?
Customer Lifetime Value is the value generated by a customer over their ‘lifetime’ with a retailer: that is, between their first and last purchase there. ‘Value’ can be defined as pure revenue: how much the customer spent. But in my e-commerce experience, I found that more mature retailers care less about short-term revenue than they do about long-term profit. Hence, they’re more likely to consider…
