Churn Prediction for Usage-Based Companies: What Works and Why It’s Hard

Not every customer walks away with a loud slam of the door. In usage-based businesses, churn can be slow, silent, and sometimes invisible, until it hits your revenue report.

Predicting churn is tricky enough. But for usage-based models, where customer activity isn’t tied to a predictable subscription, it’s even harder. That doesn’t mean it’s impossible.

Let’s walk through how usage-based companies can approach churn prediction in a way that makes sense for their model, their data, and their team.

Why churn is harder to spot in usage-based models

In subscription models, churn is usually clear. A customer cancels, downgrades, or stops paying. There’s a billing trigger.

In usage-based models, that clarity fades. A customer might simply stop logging in. Or they might reduce usage slowly over time. There may be no cancellation event at all, just a quiet drop in engagement and spend.

That means the signs of churn are behavioral, not transactional. Which means you need a different toolkit to see them coming.

Key signals to track

To predict churn effectively, you need to monitor patterns of change, not just raw totals. Here are a few data points that matter most in usage-based models:

• Declining usage frequency: Are users logging in less often? Are they pulling fewer reports or making fewer API calls?
• Drop in active users: If a team starts with 15 users and slips down to 3, that’s a red flag.
• Shorter or smaller sessions: A customer might still be active but engaging less deeply.
• Support activity spikes: More tickets, longer resolution times, or repeated issues can signal dissatisfaction.
• Credit balance hoarding: If prepaid customers stop using their available credits, they may be quietly leaving.

The important part isn’t just tracking these signals, it’s recognizing how they relate to each other and when they change.

Models that work for usage-based churn

There’s no one-size-fits-all model. But some methods work better for usage-based companies than others.

1. Logistic Regression

• Simple. Transparent. Good for binary outcomes.
• You can model churn likelihood based on key usage metrics and customer characteristics. It’s easy to explain, but struggles with complex behavior patterns.

2. Decision Trees

• Helpful when you need clear rules, like “If user has 2+ support tickets and sessions dropped 40%, churn risk is high.”
• Easy to visualize and communicate. But they can overfit if not managed well.

3. Survival Analysis

• Built for time-to-event modeling. Instead of asking if a customer will churn, it asks when.
• This is especially useful for usage-based companies where churn is gradual.

4. Behavioral Clustering

• This uses unsupervised learning to group customers by usage patterns.
• You can identify which clusters tend to fade out and focus retention on at-risk groups.

5. Ensemble Models

• Combine multiple models to balance strengths.
• This works well if you have rich data and want higher accuracy—though it comes at a cost in complexity and interpretability.

Avoiding common traps

Even with a good model, churn prediction can go off course. Watch out for these:

• Relying only on billing data: In usage-based models, billing is a lagging indicator. It reflects churn that’s already happened.
• Ignoring “partial churn”: A customer who drops usage by 80% is a churn risk, even if they’re still technically active.
• Not adjusting for seasonality: A drop in usage in December may not mean churn if that’s normal for your industry.
• Treating all churn the same: A startup pausing use is different from an enterprise account offboarding entirely. Flag them differently.

What to do with your predictions

Churn prediction is only useful if it leads to action. Here’s what to do once you’ve identified at-risk accounts:

• Trigger retention workflows: This could be a check-in from customer success, a helpful product email, or a prompt to explore new features.
• Offer flexible options: Downgrade paths, pause plans, or credit extensions can keep customers from leaving completely.
• Adjust your product: If certain features correlate with long-term usage, make sure new users discover them early.
• Loop in sales or renewals teams: Give your team a heads-up before revenue walks out the door.

Usage-based churn isn’t just about predicting when customers leave. It’s about recognizing when they start to drift, and acting before it’s too late.

By focusing on behavior over billing, building the right model, and tying your insights to real-world actions, you can retain more customers and keep your revenue steady.

If you’re using Salesforce, revVana can help you forecast usage-based revenue, track these changes in real-time, and layer churn intelligence into your pipeline.