The Metrics That Actually Predict Churn (And The Ones That Don't)
Unlocking the Right Data for Stronger Customer Retention
The Metrics That Actually Predict Churn (And the Ones That Don’t)
Churn is one of the most dreaded words in Customer Success (CS). Despite the abundance of tools, data, and resources available, predicting churn remains a challenge for many organizations. The key to avoiding this pitfall lies in using the right metrics. Too often, CS teams rely on traditional indicators that don’t provide a complete picture.
In this piece, I’ll break down the metrics commonly used to predict churn—and explain why they might be leading you astray—and, more importantly, offer alternatives that provide real insights into customer health and risk.
The Wrong Metrics: Vanity Metrics That Don’t Actually Predict Churn
Net Promoter Score (NPS) Alone
NPS is a tried-and-true metric for gauging customer sentiment, but as a sole predictor of churn, it falls short. A customer might respond with a "9" or "10" on an NPS survey but still be quietly disengaging with your product. Conversely, a "6" or "7" might not indicate imminent churn if the customer has strong connections with your support or sales teams.
Why it’s unreliable: NPS doesn’t measure actual usage or engagement. A customer who’s dissatisfied may still renew because they haven’t fully explored alternatives, or they might have hidden frustrations not captured by the survey.
Logins and Usage Frequency
Many teams rely on login frequency as a red flag for churn. If a customer is logging in less frequently, they’re assumed to be disengaging—right? Not necessarily. A shift to a more efficient or automated workflow might reduce the need for frequent logins. Or perhaps they’ve mastered the product, meaning less need to interact with it.
Why it’s unreliable: It’s an incomplete picture. You need to look deeper into usage patterns and how customers are interacting with your product, not just the frequency of their logins.
Customer Support Cases Opened
The number of support cases is often seen as a risk indicator. More cases opened might suggest more dissatisfaction, but this is a simplistic view. For some customers, frequent support interactions are a sign of engagement and a positive relationship with your team. Others, however, might just be barely scraping by, waiting for the product to fail before they reach out.
Why it’s unreliable: Not all support cases are created equal. The context behind the case matters—whether it’s a proactive inquiry, a technical problem, or a sign of customer frustration. It’s important to assess whether the support cases indicate a positive or negative relationship.
The Right Metrics: What Actually Predicts Churn
So, what does predict churn? The following metrics offer much more reliable indicators of customer health and satisfaction.
Product Usage Patterns and Feature Adoption
Instead of focusing solely on login frequency, dive deeper into how customers are using your product. Are they adopting key features? Are they using it in the way it was intended, or are they struggling to leverage its full capabilities? Low or stagnating feature adoption can indicate a risk of churn long before the customer stops using the product entirely.
Why it works: Through both my real-world experience as well as my time studying product analytics in courses such as Pendo’s Mind the Product series, I learned that product usage patterns offer the clearest insights into a customer’s engagement. By analyzing in-app behavior, we can track how customers are interacting with core features and identify early signs of disengagement. Features that are consistently underused or poorly adopted could signal that customers aren’t seeing the value they expected—and that’s when proactive outreach is critical.
Actionable Insight: Track and analyze usage of your most critical features, not just login frequency. Implement in-app product tours or targeted content to drive adoption.
Customer Health Scores Based on Multiple Factors
A comprehensive health score that incorporates factors like usage trends, engagement with support, and product satisfaction provides a more holistic view of a customer's risk of churn. Health scores help identify at-risk customers earlier, so you can take action before it’s too late.
Actionable Insight: Ensure your health score incorporates a variety of metrics—usage, engagement, sentiment, and support interactions—and calibrate it based on historical churn patterns.
Customer Success Engagement
Strong, proactive engagement with your customer success team is often an indicator that a customer is committed to success and invested in the relationship. Lack of engagement—particularly if a customer is just going through the motions—can signal a higher risk of churn.
Actionable Insight: Track engagement with your CS team. Are customers engaging proactively? Are they seeking out additional resources and value?
Customer ROI Realization
A customer who consistently sees value from your product is far less likely to churn. Therefore, regularly assessing whether customers are realizing the full ROI is a much more predictive metric. Customers who are getting results are more likely to renew and even expand their usage.
Actionable Insight: Measure customer success through tangible outcomes: Are customers meeting their KPIs? Are they using your product to achieve their business goals?
How to Use These Metrics to Predict and Prevent Churn
To effectively use these metrics, you need to set up systems that track them over time and ensure they’re actionable. Here’s how to do it:
Automate Tracking: Leverage automation tools to track product usage, feature adoption, and engagement. This data should flow into your CRM or customer success platform, providing a unified view of your customer base.
Monitor Early Signs of Disengagement: Don’t wait for customers to stop using your product. Look for early indicators like drops in feature adoption or engagement and act quickly to re-engage customers before they churn.
Communicate with Your Customers: Use insights from these metrics to drive proactive outreach. If you see a customer falling off, reach out with tailored support or training to get them back on track.
Conclusion: Rethink Your Churn Metrics
Predicting churn isn’t easy, but using the right metrics can make a world of difference. By focusing on comprehensive product usage data, a robust health score, proactive engagement, and ROI realization, you’ll be able to predict churn more accurately and, more importantly, take steps to prevent it.
Start today by revisiting the metrics that are driving your churn predictions—and ditch the ones that aren’t telling you the full story.