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 4500LTV 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()
