When transcript quality causes churn: an NPS-driven QA playbook

Transcript errors can quietly drive churn in conversational AI products because even minor word substitutions, hallucinations, or speaker-labeling mistakes may produce misleading meeting summaries, incorrect action items, and flawed CRM records before users report problems. The playbook recommends treating transcript health as a leading retention indicator by continuously tracking word-level confidence, error distributions, diarization quality, hallucination patterns, language-specific performance, and audio conditions, then connecting these metrics to engagement, NPS, and retention data. It argues that QA should prioritize high-risk recordings through stratified sampling across languages, call lengths, speaker counts, noise levels, overlapping speech, and code-switching, rather than relying on convenient samples of clean audio. Suggested interventions include calibrated soft and hard confidence alerts, human review of flagged segments before delivery, post-processing for speaker attribution in real-time workflows, custom vocabulary updates for recurring entity errors, model routing based on language and audio profile, and improvements to problematic recording environments. The article presents a progression from manual spot checks and reactive complaint handling to automated telemetry and continuous human-in-the-loop review, claiming that targeted exception handling can reduce review effort at high volume while improving accuracy, though it also promotes Gladia’s models, infrastructure, pricing, and compliance features.

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