The QBR Transcript Pipeline: Sentiment as a Leading Indicator
A working pipeline that turns recorded QBRs into a sentiment delta — and the four pitfalls that make every off-the-shelf version of this useless inside an enterprise account.
Frameworks, working notebooks, and teardowns for the AI that actually lands inside a CS function — not the keynote version. Below: the five-signal renewal model, as a diagram and as code.
The job of any CS model isn't a number — it's an defensible prediction. Inputs on the left are the signals every CSM already collects. Outputs on the right are the only three the business actually buys.
The diagram above is the entire thing. This piece walks the architecture choice, the training data, the calibration curve, and — most importantly — the conversation you have with the CRO the first time it disagrees with a CSM's gut.
A working pipeline that turns recorded QBRs into a sentiment delta — and the four pitfalls that make every off-the-shelf version of this useless inside an enterprise account.
A methodology-first scoring framework — six dimensions, weighted, scored 1–5 — applied to four representative copilot categories as worked examples. The scorecard is the contribution.
AI tooling decisions in CS should be made one task at a time, not one vendor at a time. The 4×4 matrix that tells you what AI should automate, augment, inform, or leave alone.
Most published prompt libraries for Customer Success are useless because they're written by people who have never run a renewal. Eight working prompts, organized by Decision Matrix quadrant.
Almost every published AI safety conversation is happening at the wrong altitude for CS work. Eight operational rules that produce defensible decisions in under thirty seconds.