Churn Prediction

What is churn prediction?

Churn prediction is the process of using historical data to identify which subscribers are most likely to cancel their subscriptions in the near future.

It relies on algorithms to spot patterns in how past customers behaved right before they left, and it flags active users who are starting to match those same patterns.

For example, if a customer stops using a key feature of your product and starts visiting your billing page, a churn prediction system will immediately flag them as a high risk.

Why is churn prediction important?

Acquiring new subscribers is expensive, so keeping the ones you already have is the fastest way to grow your revenue. 

When you wait for a customer to tell you they are leaving, it is usually too late to change their mind. Churn prediction gives your team the lead time they need to step in and fix the relationship while the customer is still paying. 

This helps you protect your recurring revenue and keeps your customer acquisition costs low.

How to implement churn prediction?

Churn prediction works by feeding your customer data into a model that learns to spot the difference between a healthy account and one that's about to leave so you can take action beforehand.

For instance, if the platform predicts a customer has an 80% chance of churning, it can automatically trigger a targeted email offer or create a high priority task for your customer success team.

The 3 basic steps are:

1. Gather your data

Start by collecting and analyzing your historical subscription data, product usage metrics, and support history using a Predictive AI model, like Churned.

2. Let the model learn what healthy looks like
The system learns what a healthy customer looks like versus a customer on the verge of leaving, and it assigns a risk percentage to every account in your system.

3. Connect risk scores to your existing tools
In practice, you can connect these risk levels to your existing CRM and marketing tools for save plays and retention campaigns.