The Metric That Predicts Churn Before Your CFO Sees It

How Issue Recurrence Rate Became My Early Warning System for Revenue Risk

It's not the first support ticket that kills your Net Revenue Retention. It's the third one about the same problem.

By the time a customer is reaching out the third time, the tone has shifted. They're not asking for help anymore. They're documenting problems for their evaluation of alternatives. And by the time this pattern shows up in your usage data or renewal pipeline, it's too late. The customer has already mentally checked out.

The Pattern We're Not Always Tracking

Think about your own experience as a consumer. One bad interaction? You'll give the company another chance. Two similar issues? You start wondering if this is a pattern. Three times? You're actively looking at competitors, even if you haven't told anyone yet.

The research backs this up. Customers experiencing recurring issues show significantly higher churn risk than those with isolated problems. Support ticket patterns can reveal switching intent days before customers make the decision to leave. But many of us don't have a systematic way to quantify or track this yet.

Enter Issue Recurrence Rate

Issue Recurrence Rate (IRR) tracks how often customers contact support about the same or related issues over a specific timeframe. Not just identical tickets. Related problems that stem from the same underlying product gap or workflow friction.

Here's what makes it powerful: you can segment it by customer cohort, ARR tier, or product adoption stage. Suddenly, you're not just seeing "we have a lot of repeat tickets." You're seeing "customers in the $50K-$100K ARR range who adopted feature X in the last 90 days are contacting us 2.5 times on average about related authentication issues."

That's not a support metric. That's a revenue risk signal.

Why This Matters for Net Revenue Retention

Net Revenue Retention is typically a lagging indicator. You find out about contraction when customers downgrade or don't renew. By then, you're in damage control mode.

Issue Recurrence Rate is a leading indicator. It shows you the erosion happening in real time, before it impacts revenue.

When I started tracking IRR by customer cohort at scale, the patterns were undeniable. Customers with high recurrence rates (3+ contacts about related issues in 90 days) were churning or contracting at dramatically higher rates than those with isolated tickets. More importantly, the recurrence pattern showed up in support data 30-60 days before it appeared in usage metrics or engagement scores.

That's your intervention window. That's when Customer Success can step in with proactive outreach, when Product can prioritize the fix, when Engineering can see the business impact of unresolved technical debt.

What This Unlocks Cross-Functionally

For Customer Success: You're no longer reacting to renewal risk after the fact. High IRR becomes a trigger for proactive plays. "This account has contacted us 3 times in 60 days about billing workflows. Let's schedule a strategic check-in before their renewal cycle starts."

For Engineering: Suddenly, "this bug has been in the backlog for months" has a measurable revenue cost. One unresolved integration bug generating recurring tickets across 15 enterprise accounts? That's not a support problem. That's a retention risk with a dollar value attached.

For Product: Clear prioritization based on recurring friction, not just ticket volume. Five tickets about feature A might seem less urgent than 50 tickets about feature B. But if those 5 tickets are the same 3 customers contacting you repeatedly? That's a different level of urgency.

For Executives: A real-time health score for NRR that goes beyond lagging indicators. Track IRR by cohort, overlay it with ARR data, and you have a dashboard showing you which customer segments are experiencing friction that predicts contraction.

Using What You Already Have

The best part? You're already collecting this data. You're tracking ticket history, categorizing issues, tagging accounts. You just need to ask a different question of that data.

Instead of "how many tickets did we close this month?" ask "how many customers contacted us multiple times about related issues?" Instead of "what's our average resolution time?" ask "which product areas generate the most recurring contact from the same accounts?"

Your existing support platform already has customer ticket history, issue categorization, and timestamps. The framework is about connecting those data points to reveal the pattern of recurrence and segment it in ways that matter for revenue.

The Shift

Like many CS teams, I spent years operating in reactive mode. A ticket comes in, you solve it, you close it, you move on. Issue Recurrence Rate forced me to zoom out and ask: are we solving problems, or are we treating symptoms?

It's the difference between measuring activity and predicting outcomes. Between optimizing for speed and optimizing for retention. Between reporting metrics and influencing roadmaps.

Ready to Build This for Your Team?

Issue Recurrence Rate transforms how you spot revenue risk before it shows up in your renewal pipeline. If you want to learn how to implement this framework for your organization, I'd love to help. Contact me 

Because the customers quietly slipping away aren't showing up in your dashboards yet. But they're showing up in your support data right now.

Susan Priti is the founder of The Moxie Lab, specializing in customer experience transformation and AI-driven support strategy. She has pioneered AI adoption in customer support, led global CX transformations, and won a Silver Stevie Award for transforming customer support into customer experience.