February 23, 2024

How to Build a Churn Model

A churn model is an automated system that analyzes customer data and predicts their likelihood of leaving your business. This information can then be used to take action and prevent the loss of customers.

The first step in building a churn model is determining what data points are relevant to your specific business. This includes everything from demographic information (gender, age, location) to billing and purchase data, including the number of products a customer is subscribed to, their chosen pricing tier, and their total lifetime value (LTV).

Once you have all your data points, it’s time to start building a churn model. The key is to use a machine learning algorithm that can learn from your historical churn data and identify patterns of behavior that indicate when a customer will churn.

This process involves identifying the target variable, which is typically a binary indicator that indicates whether or not a customer is likely to churn (yes/no). It can also be an action that the user takes, such as subscription cancellation or closing a contract.

The ML algorithm then looks at all of the available explanatory variables (features) and determines how much of an effect each has on churn by performing regression analysis on them. The features are then weighted in order to find the best predictive value, allowing you to create a model that can identify which current users will most likely churn so that you can take proactive steps to prevent them from doing so.

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