Visualising Customer Lifetime Value (LTV) With Python Seaborn

Why Analysing Customer Lifetime Value Trends Is Valuable

Michael Grogan
4 min readSep 15, 2023
Photo by StockSnap on Pixabay

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…

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Michael Grogan

Statistical Data Scientist | Python and R trainer | Financial Writer | michael-grogan.com