Customer Lifetime Value and Its Application in Retail Analytics

Customer Lifetime Value (CLV) is a relatively new framework stemming from the idea of “treatment of customers as an asset”, in use at innovative companies like Harrah’s, IBM, and Capital One. The definition is a fairly natural one: CLV is the net present value of profit from all the future purchases a customer is going to make. Customer Equity is then defined to be the sum of CLVs over one’s customer base, and it is an intangible asset that can be tracked on a company’s balance sheet.

Here is a simple example to show how we can calculate CLVs. In practice, CLV calculations need to be done on a per-customer or per-segment basis to take customer heterogeneity into account.


Many business decisions in a retail business can be informed by CLV analysis, including:

  • Customer acquisition: Target higher-value customers
  • Customer revenue growth: Identify segments amenable to up-sell/cross-sell
  • Customer retention: Targeted spending on high-value at risk customers

The following diagram shows a typical customer lifecycle and what analytics are relevant a different stages.


The goal of the different analytical problems is to lift and drag out the tail of the customer lifetime value life-cycle curve as illustrated in this diagram.clv-add-value

It’s worth noting that the estimation of CLVs can be an inexact science and there are many assumptions that need to be made and many parameters that need to be tuned in practice. In that sense, CLV is no silver bullet but that doesn’t take away its usefulness as a mental model to help us frame and think about important aspects of retail analytics. Its application requires judgement and common sense, and we are guided as usual by these two balancing quotes:

If you can’t measure it, you can’t manage it.  — Peter Drucker

Not everything that can be counted counts, and not everything that counts can be counted.  — Albert Einstein


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