Churn signals are indicators that a customer may be at risk of canceling their subscription. By monitoring billing data and product engagement together, you can spot these warning signs early and intervene before it’s too late.

Every SaaS business wants to reduce churn. While exit surveys tell you why customers left, billing data can tell you who is likely to leave. It’s the financial footprint of a customer’s journey, and when that footprint changes, it’s often the first sign of trouble. Combining these financial signals with how a user interacts with your product gives you a powerful, predictive view of customer health.

Billing data is one of the most reliable sources for predicting churn because it’s tied directly to the customer’s financial commitment. Unlike product feedback or survey responses, it’s hard data that reflects a customer’s actual investment in your service.

Here’s why it’s so effective:

• It’s immediate: A failed payment or a plan downgrade is a real-time event. You don’t have to wait for a survey to know something is wrong.
• It’s honest: Customers might say they’re happy, but a sudden drop in usage or a credit card failure tells a more objective story.
• It’s actionable: When a payment fails, you have an immediate, concrete reason to reach out. When usage drops, you can offer training or support.

Your billing system generates a wealth of data. By focusing on a few key signals, you can build a surprisingly accurate picture of churn risk. These signals fall into two main categories: direct payment events and changes in subscription value.

A failed payment is the most urgent churn signal. This is often called “involuntary churn” because the customer may not have intended to cancel.

• First-time payment failure: This could be due to an expired card, insufficient funds, or a bank block. While often accidental, it’s a critical intervention point. If left unresolved, it leads to suspension and eventual cancellation.
• Repeated payment failures: If a customer’s payment fails multiple times over a few months, it signals a deeper issue. They may be deprioritizing your service or facing financial difficulty. This is a high-risk indicator.

For usage-based or metered billing models, a customer’s consumption is a direct proxy for the value they get from your product.

• Usage drop: A significant, sustained decrease in usage is a classic precursor to churn. If a team stops using a key feature or their data consumption plummets, they are actively disengaging from the product. They are cleaning out their desk before handing in their resignation.
• Usage spike: This might seem like a good thing, but a sudden, unexpected spike can lead to bill shock. A customer who gets a surprisingly high invoice may react by restricting usage or looking for a cheaper alternative.

How customers manage their plan tells you how they perceive its value.

• Plan downgrades: A customer moving to a lower-priced tier is explicitly reducing their financial commitment. This is a clear signal they are either using fewer features or trying to cut costs.
• Frequent plan switching: A customer who frequently switches between plans may be struggling to find the right fit or is uncertain about the long-term value of your product.
• Canceling add-ons: If a customer removes optional features or services from their subscription, they are trimming what they consider non-essential.

Billing data tells you what is happening, while product engagement data tells you why. Combining them creates a holistic view of customer health. Product engagement metrics provide the context behind the numbers.

For example:

• Failed Payment + Low Logins: A customer whose payment fails and who hasn’t logged in for 30 days is a very high churn risk. They’ve likely already abandoned the product.
• Usage Drop + Support Tickets: A drop in usage combined with a spike in support tickets suggests the customer is frustrated or stuck. They can’t get value from the product and may be preparing to leave.
• Plan Downgrade + Low Feature Adoption: A customer downgrades their plan and has never used the premium features of their previous plan. This indicates they were over-provisioned and are now right-sizing their subscription.

To get this data, you typically need to integrate your billing platform with a product analytics tool (like Mixpanel, Amplitude, or Pendo).

Once you start tracking these signals, you can build a simple system to act on them.

1. Create a Customer Health Score: Assign points to different events. A failed payment might be -10 points, a usage drop -5, and a login +1. Set thresholds for “Healthy,” “At-Risk,” and “Critical” scores.
2. Automate Alerts: Set up automated alerts for your customer success or support teams when a customer’s health score drops below a certain threshold.
3. Define Playbooks: Create simple, repeatable actions for each signal. For a failed payment, trigger a dunning email sequence. For a usage drop, have a customer success manager reach out to offer help.

Building a churn prediction system requires accessible, reliable billing and user data, which is where a platform like Kinde can help.

Kinde’s billing and user management infrastructure provides the foundational data needed to monitor these signals. You can track user authentication to see login frequency and use Kinde’s billing features to monitor subscription and usage data.

For example, Kinde’s support for metered billing allows you to programmatically report customer usage. You can use this data to spot the usage drops and spikes that often precede churn. By combining this with user login data from Kinde’s authentication logs, you can begin to build a basic but effective customer health profile.

While Kinde manages the subscription and usage data, dunning and payment recovery for failed payments are typically handled through its integration with Stripe. You would configure your dunning rules in Stripe and then use webhooks to send payment failure events back to your system to update your customer health score.

• Add metered usage for a customer via API
• Pricing models