I need to say something that might make CS leaders uncomfortable: most of what your team does before a renewal is valuable, but it’s listening to only one channel. Your EBRs, your health scores, your stakeholder maps. They capture what your customer is willing to tell you directly. What they don’t capture is the conversation happening everywhere else. And that’s usually where churn starts.
I know because I ran the standard playbook for years. EBRs, stakeholder mapping, health score reviews, and renewal prep meetings, where we rated our gut feeling on a scale of green to red. We had dashboards. We had strong CSMs who genuinely cared about their accounts. And we still got blindsided.
The $2M quarter is the one I can’t forget. Two enterprise accounts churned in the same 90-day window. Both were green in every system we had. One had an NPS of 72.
When I dug into what happened, I didn’t find a CS execution problem. I found a coverage gap. Every signal had been there. Just not in the places our process was designed to look. I sat in the post-mortem knowing we’d done everything our process asked us to do. That was the problem.
Later in this article, I’ll show you what both of those accounts would have looked like inside Renewal Fix. Before anyone on my team knew there was a problem.
What your EBR captures, and what it can’t
I’m not saying EBRs are useless. A well-run EBR builds relationship depth, gives your champion ammunition internally, and surfaces problems the customer is willing to raise directly. But even the best EBR has a structural limitation: it only captures what someone chooses to say out loud, in a meeting, to a vendor.
The real conversation about your product is happening in a Slack channel you’ll never see, in a procurement review you weren’t invited to, and in a 1:1 between your champion and their new boss who just joined from a company that used your competitor. The EBR gives you one essential channel. The danger is treating it as the only one.
The signals are everywhere. Just not in your CRM.
Here’s what was actually happening in those two accounts that churned on me.
Account one: their engineering team had filed 23 support tickets about API latency over four months. Not “the product is broken” tickets. Small, specific, technical complaints that got resolved individually. Nobody in CS ever saw them because they never escalated to “critical.” But lined up chronologically, the pattern was unmistakable: this team was losing patience, one resolved ticket at a time.
Account two: three of their five power users updated their LinkedIn profiles in the same two-week window. One started posting about a competitor’s product. Our champion’s title changed from “Head of” to “Senior Manager.” A quiet demotion nobody noticed because we were watching product usage dashboards, not org charts.
Every CS leader I know has lost an account and later found out the champion left months ago. The customer’s reaction is always the same: “We assumed you knew.” They expect you to track publicly available professional changes, the same information any recruiter monitors. Not tracking them isn’t respectful. It’s a blind spot.
Neither signal lived in our CRM. Neither showed up in our health score. They were sitting in plain sight in systems our CS team had no reason to check.
What your health score measures, and the lag problem
Health scores aren’t the problem. Treating them as the whole picture is. A typical health score aggregates NPS, login frequency, support ticket count, and feature adoption. Green means safe. Red means act. But these are lagging indicators. By the time login frequency drops, the decision to evaluate alternatives may already be in motion.
When I started tracking leading indicators alongside our existing health model, the difference was striking. Across roughly 300 mid-market accounts over 18 months, we found that support ticket velocity, specifically the rate of increase in non-critical tickets over a rolling 90-day window, predicted churn at T-90 at roughly 2x the accuracy of our composite health score. The signals that actually predict churn aren’t the ones most CS platforms are designed to track.
Building the Signal Coverage Model
The teams with the strongest renewal rates don’t abandon their existing processes. They add a signal layer on top. The highest-signal sources break into three tiers.
Tier 1: Support ticket patterns. Not the count, but the velocity, the sentiment trend, and whether the same team keeps filing. A steady trickle of “resolved” tickets from one engineering team is often a louder signal than a single P1 escalation. At scale, this becomes cohort-level complaint clustering across a segment.
Tier 2: People changes. Champion turnover, re-orgs, title changes, and new executives from a competitor’s customer base. The person who bought your product and the person renewing it are often not the same person. At scale, you’re watching for patterns of org instability across your book.
Tier 3: Competitive exposure. Whether your customer is being actively pitched, attending competitor events, or has team members engaging with competitor content online. At scale, you’re tracking which segments your competitors are targeting hardest.
The real challenge isn’t knowing what to track. It’s that these signals live in five or six different systems, and nobody’s job is to stitch them together. Your CSM sees Zendesk. Your SE sees Jira. Your AE sees Salesforce. The full picture only exists if someone manually assembles it.
What this looks like in practice
One team I worked with built a manual version of this: CSMs logging signals from six different sources every Friday. About 90 minutes per account per week. Their renewal rate hit 96%. But the approach doesn’t scale past a 25-account book.
At 80 accounts in a mid-market motion, you need automation. At 150+ in a PLG model, the signals are still there, you’re watching for cohort-level drops in feature adoption or clusters of the same complaint across a segment, but you cannot find them without automation.
The teams doing this manually are logging into six tools every Friday. The teams doing this with automation get a Slack message when something changes. No dashboard to check. No Friday ritual.
Detection without a playbook is just anxiety. The point of catching signals early isn’t to panic. It’s to have time to act. An executive sponsor who hasn’t logged in for 90 days needs a different intervention than an account with a competitor POC in their Salesforce sandbox. The signal tells you what’s happening. The response has to match.
That gap between knowing what to track and actually tracking it consistently is why I built Renewal Fix. Not to replace the manual process, but to remove the ceiling on it. The platform pulls signals from support tickets, call recordings, CRM data, and engineering channels automatically, stitches them into a single account view, and flags them before they become a renewal surprise.
See it for yourself
Enter your work email at renewalfix.com. In 30 seconds, you’ll get a one-page executive brief showing your blind spots: 10 accounts that look like they belong in your CS platform, built from your company’s products, competitive landscape, and integration stack, each with a health score and risk signals sourced from support tickets, call recordings, and org changes that your current dashboard would never surface. No demo, no sales call.
Find the account that looks most like Account One. Health score in the 70s, risk signals hiding underneath. Then click “Executive Brief” for a one-page summary of your portfolio’s total risk exposure, with dollar amounts and prioritized actions. That view is what Renewal Fix delivers weekly in production.
Your green accounts aren’t necessarily at risk. But they might be quieter than you realize.
