Detect churn and expansion signal from customer conversations
Catch renewal risk and expansion opportunities from the conversations your CS team is already having. Weekly digest with cited quotes.
The problem
CS teams sit on enormous amounts of customer conversation data. Support tickets, QBR recordings, Slack customer channels, email threads, and executive business reviews all pile up across four or five tools. None of it gets synthesized into a view of which accounts are sliding and which are climbing. By the time the red flags are obvious, the renewal is thirty days out and the CSM is scrambling. Expansion opportunities get missed for the same reason. Nobody caught the comment where the VP said they wanted to roll this out to the whole division. The signal was always there. It was just buried in the wrong tool, on the wrong day, seen by the wrong person.
How Amdahl solves it
Amdahl continuously reads every customer conversation across every source. It clusters signals by account and flags the ones that correlate with churn or expansion. CS leaders get a weekly digest with the ten accounts most at risk, the ten most likely to expand, and the exact quotes that triggered each flag. The CSM sees the signal weeks or months before the renewal date. Risk gets addressed early instead of rescued late. Expansion conversations start when the buyer first hints at interest, not after the budget window has closed. The work the CS team was already doing becomes the input. The synthesis is what Amdahl adds.
What you ship
Weekly churn risk digest with cited evidence per account
Expansion opportunity alerts with source quotes
QBR prep docs populated from the account's recent conversations
Health score rationale tied to specific customer statements
Renewal playbook recommendations based on similar past accounts
Workflow
- Step 01
Connect the customer conversation layer
Link support (Zendesk, Intercom), calls (Gong, Fathom), CRM, and customer Slack channels. Amdahl ingests the back catalog and keeps syncing as new conversations land.
- Step 02
Set thresholds per segment
Configure the churn and expansion signal thresholds per account segment. Strategic accounts get tighter thresholds than self-serve. The CS lead owns the config.
- Step 03
Amdahl monitors and clusters continuously
Every new ticket, call, and Slack message feeds the account-level cluster. Amdahl surfaces the top risks and opportunities in a weekly digest with cited source quotes.
- Step 04
The CS team reviews, acts, and logs the response
The CS lead reviews the digest, prioritizes outreach, and logs the response. Confirmed and dismissed flags feed back into the model so future digests sharpen to your specific business.
Customer example
Caught a top-20 account renewal risk six weeks before the deadline. The CSM saved the account with targeted outreach grounded in the flagged quotes.
We used to rescue accounts at day ninety. Now we catch them at day thirty.
Frequently asked
- How does this compare to Gainsight or Catalyst?
- Gainsight and Catalyst own the health score, the playbook, and the CSM workflow. They are the system of action. Amdahl owns the conversation data layer underneath. We read every call, ticket, and Slack message, cluster the signals by account, and pipe the output into whichever CS platform you already run. If you use Gainsight, the signals land in Gainsight as health score inputs or timeline events. If you use Catalyst, same story. Amdahl does not replace the CS platform. It replaces the manual synthesis step that nobody had time for, and it does the synthesis from the actual conversations your team is already having.
- Does it work with product usage data?
- Yes, and it complements product usage rather than competing with it. Product usage data tells you what the account is doing in your tool. Conversation signal tells you what the account is saying about your tool. The two catch different things. A power user with rising usage can still churn if the executive sponsor stops showing up to QBRs and the ticket sentiment turns negative. A low-usage account can still expand if the VP tells you on a call that the whole division is about to onboard. Amdahl pulls usage signals from your CRM and combines them with the conversation signal so the weekly digest reflects both layers, not just one.
- How accurate are the churn signals?
- Accuracy improves as the CS team confirms or dismisses flags. On day one, Amdahl uses general patterns that correlate with churn across B2B SaaS. Things like negative sentiment drift, executive sponsor disengagement, and specific objection language. As your team marks flags as real or false, the model learns which patterns predict actual churn in your specific business and segment. Most teams see the signal converge within the first four to six weeks. The digest gets noticeably sharper by month two. The CS lead owns the feedback loop. Nobody is asking you to trust a black box. Every flag links back to the exact quote that triggered it.
- Can CSMs add their own notes as a signal source?
- Yes. CSM notes are a first-class source alongside calls and tickets. If the CSM writes a note in the CRM after a customer meeting, that note feeds the account cluster the same way a Gong transcript does. This matters because the CSM is often the first person to notice that something is off, and that observation usually lives in a CRM note that nobody else ever reads. Amdahl surfaces those notes into the weekly digest with the same citation trail as any other source. The CSM's intuition gets amplified across the team instead of trapped in their own account list.
See this use case running on your own customer conversations.