What is Your Customer’s Worth Over Their Lifetime?

What is Your Customer’s Worth Over Their Lifetime?

Estimate your customer’s Lifetime Value (LTV) in a non-subscription setting using Python to devise appropriate retention strategies and minimize churn

In one of my previous posts, we discussed Survival Analysis techniques to predict when our customers will churn together with customer-specific strategies to minimize customer attrition. That was in a contractual setting whereby the ‘death’ was the customer ending or not renewing their subscription.

In this post, we will look at the Buy Till You Die (BTYD) class of statistical models to analyze customers’ behavioral and purchasing patterns in a non-subscription business model to model and predict a customer’s lifetime value (CLV or LTV).

What is Customer LTV?

A customer’s Lifetime Value is the total net income or revenue that a company can expect to earn from its customers over their entire relationship — either at the individual customer level, cohort level, or over the entirety of its customer base — discounted to today’s dollar value using the Discounted Cashflow (DCF) method.

Dependent upon what data is available, LTV can represent either total revenue or net income, i.e., revenue minus costs. It is generally less cumbersome to use revenue numbers as customer-level historical sales numbers are readily available. Whereas, the calculation of customer-specific costs might require certain assumptions, making it a bit judgemental.

Why LTV?

In a non-contractual business model where there is no contract or subscription agreement between a company and its customers (e.g., e-commerce or retail), we can stop our purchases after any given transaction. Or we can come back after 12 months for a repeat purchase. Therefore, there is no distinct binary event that identifies whether a specific customer is still alive or not. This makes it practically impossible to effectively predict churn as a binary event through logistic regression or decision trees.

A better way to analyze our customers with no contractual agreement is to predict instead the monetary value that we can expect our customers to spend with us in the future together with the predicted churn probability — given their historical purchasing pattern and behavior.

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