Struggling to pre-empt churn risk prior to obvious signal from the customer. What analysis or tools are people using for clarity. Company’s focus is decreasing churn by 3% this year, I want to get a system or data analysis in place to at least provide earlier signals.
Really depends what systems you are using. Most popular tools for this would be: • All-in-One Customer Success: ChurnZero or Gainsight
Best for: Managing high-touch accounts with automated "Playbooks" that trigger when health scores drop.
• Product Analytics: Mixpanel or Userpilot
Best for: Identifying friction points and feature-drop-off rates using heatmaps and user paths.
• AI Predictive Modeling: Pecan or Akkio
Best for: No-code AI that analyzes historical data to predict exactly which accounts are at risk next month.
• Sentiment Analysis: Unwrap or Chattermill
Best for: Aggregating support tickets and Gong calls to detect "frustration trends" before a ticket is even filed.
• Revenue Recovery: ProfitWell (Paddle)
Best for: Automating the fix for "involuntary churn" (expired cards and billing failures).
ASKELEPHANT is the best for this. I am biased because I was the founding AE. I ca set you up with some unbiased individuals to chat with if you want. Reviews all conversations(meetings, calls, emails) and information in the CRM. You're able to grab sentiment over time, competitor mentions, a rolling score to show engaged customers are etc.
Liked that you’re thinking about pre-empting churn risk before the obvious signal shows up. A few patterns I’ve seen work well:
Track early churn themes in call content (e.g., budget pressure, internal review, disengagement) instead of waiting for renewal talk.
Combine conversation signals with engagement data like meeting gaps, no-shows, stakeholder drop-off.
Add lightweight account health scoring that includes qualitative signals, not just product usage.
Set up real-time alerts when risk patterns spike so intervention happens earlier.
It might be worth connecting with someone from the Avoma team who’s worked with CS orgs on setting up churn-risk tracking and renewal health scoring. They’ve set different models in practice and can share what’s actually moving the needle. Happy to help with an intro if useful.
Evan S., if you have enough historical data, tools like Mixpanel or Amplitude can help you identify the behaviors that most correlate with churn. From there, you can build a health score and refine it over time as you test its predictability. Once you have a baseline, you can layer in more sophisticated signals: support data, email disengagement, decision-maker departures, key role hires, G2 category searches. Depending on your product and engagement cycle, the predictability can get surprisingly good. At LANDR, we could detect 60% just based on segments. I’ve been told PandaDoc can predict 80% of churn ahead of time.
Hi Evan S. ! This comes up a lot. Most teams try to predict churn from CRM or usage data after the signal is already obvious. What we’ve seen work better is identifying leading signals before they show up as churn risk, like:
changes in product usage patterns (not just drop-offs, but how usage shifts)
gaps between expected vs actual behavior across segments
timing of engagement relative to key milestones
The challenge is less about building another model, and more about connecting these signals early enough to act on them. Curious, what data sources are you currently looking at today?
