The contact center QA scorecard: what to measure and how transcription feeds it

Contact center QA programs often manually review only 1–2% of calls because of staffing limits, leaving potential compliance failures and coaching opportunities undiscovered, while automated QA can extend analysis to every interaction if it is supported by accurate, speaker-attributed transcription. Effective scorecards define weighted metrics such as mandatory disclosures, resolution quality, customer sentiment, soft skills, first-contact resolution, customer satisfaction, and average handle time, whereas rubrics provide explicit, LLM-readable criteria and examples for judging each metric. The article emphasizes that transcription errors, particularly missing disclosures, incorrect entities, and speaker misattribution, can undermine compliance checks, CRM records, sentiment analysis, and coaching decisions, making post-call diarization especially important. It recommends compact scorecards, separating binary requirements from scaled behavioral assessments, defining critical-failure conditions, calibrating automated results against independently reviewed samples, and directing human reviewers toward exceptions rather than random samples. The vendor promotes its asynchronous transcription platform, including Solaria models for European business audio and multilingual code-switching, structured outputs for CRM and workforce-management integrations, and compliance features, citing French platform Gravite’s reported reduction in review time from 15 minutes to one minute per call while processing 50,000 annual audio hours.

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