Visualising Customer Lifetime Value (LTV) With Python Seaborn
Why Analysing Customer Lifetime Value Trends Is Valuable
Note: The original article is available here.
When a business sells to customers, the reality is that a certain percentage of customers will cease buying from that business over a given period. This is what is known as churn.
The total revenue that a business can expect from a customer before they churn is known as customer lifetime value. Revenue remaining constant, the longer a customer keeps buying from a company - the higher their customer lifetime revenue (or LTV) will be.
LTV is a particularly important metric in industries such as telecommunications - which operates on the basis of a subscription model where the goal is to maximise ARPU (average revenue per user) while minimising churn (the percentage of customers that leave within a given period).
Example
Let’s take the following example. Suppose that a hypothetical telecommunications company shows the following ARPU and postpaid churn rate statistics by quarter:
Year Quarter ARPU Churn LTV
1 Q1 36 0.69 5217.39
1 Q2 38 0.68 5588.24
1 Q3 33 0.75 4400
1 Q4 37 0.78 4743.59
2 Q1 34 0.69 4927.54
2 Q2 38 0.68 5588.24
2 Q3 34 0.76 4473.68
2…