Your CSM team found out about the Q3 cancellation the same way everyone else did — the Stripe webhook. Meanwhile the evidence was sitting in three systems nobody cross-references: a Fireflies transcript where the champion said “we’re evaluating options,” an Intercom thread that went quiet after two unresolved tickets, and a usage curve that flattened 41 days ago. Legacy churn scoring broke in 2026 because it ranked accounts on seat count and login frequency while the actual signal — sentiment, stakeholder turnover, and support decay — lives in unstructured text. By the time a health score turns red, the renewal conversation is already lost.
This is for RevOps leads, CS operations managers, and technical founders running 200–10,000 B2B accounts who already have Attio (or are migrating to it) and want a working churn-risk automation workflow instead of a dashboard nobody opens. You should be comfortable with REST APIs, JSON payloads, webhook auth, and a workflow runner like n8n, Make, or a small Python service. Out of scope: building a data warehouse, ML model training, replacing your CRM, and anything requiring a data science hire. This is an LLM-scored signal pipeline, not a predictive modeling course.
Honest framing: Gemini Flash is excellent at reading a 40-minute transcript and extracting “the economic buyer has changed” — the thing humans skip because nobody has time. It is bad at calibration. Left alone, it will score everything 65–75 and cite evidence that sounds right but overweights one angry ticket. That is why the guide spends real pages on backtesting against accounts you already lost and on weight calibration. Human review is non-negotiable at two points: before any exec-level email fires, and during the first 30 days of scoring, where a human confirms or rejects every high-risk flag to build your tuning dataset. Never let the automation send a save offer unattended.
What This Guide Covers
- Why 2026 broke usage-based health scoring — and the signal categories that replaced it
- A complete stack decision: what each tool does, what it costs, and where cheaper substitutes actually work
- The 24-indicator churn taxonomy, grouped by lead time, so you know which signals buy you 90 days versus 9
- Exact Attio object and attribute architecture for storing risk scores without polluting your CRM
- API authentication and rate-limit handling for every connected system in the pipeline
- How to ingest Intercom conversations, Fireflies transcripts, and product usage webhooks into one normalized signal record
- A scoring approach that returns consistent 0–100 numbers with citable evidence instead of vibes
- Writing scores back to Attio and building a risk timeline your CSMs can read in five seconds
- Threshold playbooks that route by severity: silent log, CSM task, exec email, or Instantly save sequence
- Dedupe and cooldown logic that prevents the alert fatigue that kills these systems by week three
- Slack digest formatting that people actually open, with the anti-patterns that get channels muted
- A backtesting method using last year’s churned accounts to prove the system works before you trust it
- False-positive tuning and weight recalibration procedures for ongoing production use
- Cost and privacy math at 500, 2,000, and 10,000 accounts, plus a 30-day rollout sequence
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