Visualising Customer Lifetime Value (LTV) With Python Seaborn

Why Analysing Customer Lifetime Value Trends Is Valuable

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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	Q4	34	0.77	4415.58
3	Q1	38	0.68	5588.24
3	Q2	33	0.69	4782.61
3	Q3	34	0.76	4473.68
3	Q4	35	0.79	4430.38
4	Q1	32	0.7	4571.43
4	Q2	37	0.65	5692.31
4	Q3	34	0.79	4303.79
4	Q4	36	0.8	4500

LTV by quarter is calculated as:

(ARPU/Churn (%)) x 100

Now, let us visualise this using heatmaps and boxplots generated using Python’s Seaborn visualisation library.

LTV Visualisation: Heatmap

Looking at this heatmap, we can see that customer lifetime value is highest in Q1 and Q2 on the whole.

analysis = pd.pivot_table(df, index= 'quarter', columns='year', values="ltv")
sns.heatmap(analysis, annot=True, cmap="coolwarm", vmin=4300, vmax=5600, fmt='g')
plt.title("LTV")
plt.show()

Source: Heatmap generated by author using Python’s seaborn library.