Customer churn is a critical metric for any subscription-based business. While some churn is inevitable, proactively identifying at-risk customers gives you the chance to intervene, offer support, and potentially save the account. This guide explains how to use billing and usage data as an early warning system to predict and reduce churn.

Instead of waiting for a customer to click “cancel,” you can monitor the subtle signals they send through their payment history and product usage. These patterns often tell a clear story, allowing you to move from a reactive to a proactive retention strategy.

Churn prediction using billing data is the practice of analyzing financial and usage-related events to identify customers who are likely to cancel their subscriptions. It involves tracking key signals—like payment delays, drops in usage, or frequent downgrades—that correlate with future churn.

By monitoring these data points, you can build an early warning system. This system can range from a simple set of rules that flag concerning behavior to a sophisticated machine learning model that assigns a “churn score” to each user. The goal is to spot disengagement before it becomes permanent.

Your billing and product usage data is a rich source of information about customer health. The key is to know which signals to track. The following list details some of the most powerful predictors of churn.

• Payment failures and dunning: When a customer’s recurring payment fails, it’s a significant event. While often due to an expired card, repeated failures can indicate that the customer is de-prioritizing your service or facing financial issues.
• Declines in metered usage: For usage-based pricing models, a steady drop in consumption is a classic sign of disengagement. If a customer is using less of your product, they are getting less value from it and are more likely to churn.
• Subscription downgrades: A customer moving to a lower-priced plan is an explicit signal that they want to spend less. While not a churn event itself, it’s a precursor that indicates they are re-evaluating the tool’s value.
• Lack of engagement before renewal: A user who hasn’t logged in or used key features in the weeks leading up to their renewal date is at high risk. Their inactivity suggests the service is no longer part of their workflow.
• Failed payment retries: If a payment fails and the customer doesn’t update their details after several dunning emails, the probability of churn increases dramatically.
• Frequent plan switching or pausing: Customers who constantly change plans or pause their subscription may be struggling to find a good fit or questioning the product’s long-term value.

Building a churn prediction system doesn’t have to be overwhelmingly complex. You can start with a simple, rules-based approach and evolve to a more sophisticated model over time.

This method involves setting up simple “if-then” rules based on the signals listed above. It’s easy to implement and can provide significant value immediately.

How it works:

1. Define your triggers: Identify a few key negative signals (e.g., two failed payments in a row, a 50% drop in usage month-over-month).
2. Create rules: Combine these triggers into simple conditional rules.
3. Set up alerts: When a customer’s behavior matches a rule, automatically send an alert to your customer success team via email, Slack, or your CRM.

Example rule:IF a customer on the “Pro” plan has a failed payment AND their logins in the last 30 days are less than 2, THEN create a “High Churn Risk” ticket in our support system.

For businesses with more data, a machine learning model can provide more nuanced predictions. Instead of a simple alert, it assigns each customer a churn probability score (e.g., 0-100%).

How it works:

1. Collect historical data: Gather data on the billing and usage patterns of thousands of past customers, both churned and active.
2. Train a model: Use this data to train a classification model (like logistic regression or gradient boosting) to recognize the patterns that led to churn.
3. Score current customers: The model then analyzes the behavior of current customers in real-time and assigns them a churn score.
4. Prioritize outreach: Your customer success team can use these scores to focus their efforts on the highest-risk accounts first.

Building an effective early warning system comes with potential hurdles. Understanding them can help you create a more robust and fair process.

• Data quality: The system is only as good as the data it’s built on. Inconsistent or incomplete billing and usage data will lead to inaccurate predictions.
• Correlation vs. causation: A signal might correlate with churn but not cause it. For example, a failed payment might lock a user out, causing their usage to drop. The root cause was the payment failure, not the usage drop. Understanding the sequence of events is key.
• Defining “churn”: It’s critical to have a clear, consistent definition of what a “churn event” is. Is it when a user clicks cancel, when their subscription expires and isn’t renewed, or after a certain number of failed payments? This definition is the foundation of your analysis.
• Over-automation: An automated system should flag customers for human intervention, not trigger punitive actions. Automatically canceling an account after one failed payment can create a negative customer experience and cause unnecessary churn.

Building a churn prediction system requires access to clean, real-time event data for billing, subscriptions, and user activity. Kinde provides the foundational infrastructure that captures these critical signals.

You can use Kinde to feed your own early warning system by listening to events that signal potential churn. The primary mechanism for this is webhooks, which send a notification to your system whenever a specific event occurs in Kinde.

For example, you can configure a webhook to listen for events like:

• invoice.payment_failed
• subscription.updated (to detect downgrades)
• subscription.cancelled

By capturing these events, you can pipe real-time data into your rules-based system or predictive model. If you use a metered pricing model, Kinde’s ability to track usage is another vital source of data for your churn analysis, as drops in usage are a strong predictor of disengagement.

To learn more about how you can get this data from Kinde, see the following documentation:

• About webhooks
• Add metered usage for a customer via API