See churn coming before it happens
Churn signals hide in plain sight—across support tickets, usage data, payment failures, and CRM notes. Eru surfaces at-risk accounts by analyzing all of them together.
Why churn is hard to predict
Every team sees part of the picture. None sees the whole thing.
Customer Success watches health scores and NPS responses. They notice when engagement drops in their QBRs—but that's quarterly, and churn doesn't wait.
Support sees ticket volume and sentiment. They know when customers are frustrated—but they don't see usage data or payment history.
Product tracks feature adoption and activity metrics. They see usage decline—but they don't know if the customer is unhappy or just on vacation.
Finance catches payment failures and downgrades. By then, the customer has already decided to leave.
The signals that predict churn are distributed across all these systems. No single team has the complete view.
Signals that indicate churn risk
Research on SaaS churn consistently identifies these patterns:
Usage decline
Active users dropping. Login frequency decreasing. Core features unused. This is the most reliable signal—but it lives in product analytics, not CRM.
Support patterns
Increasing ticket volume. Negative sentiment in conversations. Escalations to management. Unresolved issues that linger. This lives in Intercom or Zendesk.
Payment friction
Failed charges. Expired cards not updated. Requests for invoicing changes. Delayed payments. This lives in Stripe.
Engagement decay
Emails unopened. QBRs declined. Champion leaving the company. Contract renewal conversations going silent. This lives in the CRM.
The compound signal
Any one of these might mean nothing. Together, they tell a story. A customer with declining usage AND increasing support tickets AND a failed payment is almost certainly at risk. But seeing that requires data from three different systems.
How Eru identifies at-risk accounts
Unified customer view
Eru maps customer entities across all your systems. The account in your database, the company in HubSpot, the customer in Stripe, the workspace in Intercom—they're linked. When you look at a customer, you see everything.
Cross-system watchlists
Eru creates watchlists that analyze signals from multiple sources. Not a simple filter on one metric—a scored list based on conditions across your entire data stack.
Evidence-backed insights
When an account appears on the watchlist, you see exactly why. Which signals triggered. What the actual data shows. Links to investigate in each source system. Not a black-box score—transparent analysis you can act on.
Continuous monitoring
Watchlists run on a schedule. Daily, hourly, whatever makes sense for your business. When the list changes significantly—new accounts added, accounts moving to higher risk—you get alerted in Slack.
From detection to action
Knowing who's at risk is only useful if you can act on it.
Prioritized outreach
The watchlist is sorted by MRR at risk, not just risk score. A $50/month account at critical risk matters less than a $5,000/month account at moderate risk. Focus where it matters.
Context for conversations
When CSMs reach out, they know why. "I noticed your team's usage dropped after your lead developer left. How can we help with the transition?" is more effective than "Just checking in!"
Pattern recognition
Over time, you see which signals actually predict churn in your business. Maybe support tickets matter more than usage. Maybe payment failures are a lagging indicator. Eru's evidence packs let you analyze what actually correlates with churn for your customers.
Beyond churn: other watchlists
The same cross-system analysis works for other use cases:
- Expansion opportunities: High-usage accounts on starter plans. Heavy API users approaching limits. Teams adding seats rapidly.
- Stale deals: Pipeline deals with no activity in 30+ days. Proposals sent but not viewed. Champions who've gone quiet.
- Onboarding risks: New customers with low initial engagement. Setup steps not completed. First value milestones not reached.
- Champion tracking: Key contacts changing jobs. Decision-makers leaving customer companies. New stakeholders joining accounts.
Related
Stop reacting to churn
Eru surfaces at-risk accounts before they become cancellation emails.