Speech Analytics for Call Centers

Automated transcription and analysis of 100% of voice interactions — surfacing patterns in customer intent, agent performance, script adherence, and compliance across every call, not a 2–5% manual sample.

Every call is transcribed via ASR, analysed by NLP for topic, intent, sentiment, and compliance signals, scored against configurable QA frameworks, and surfaced in dashboards showing trends across agents, teams, and time periods.

Manual call listening reveals what happened on 5% of calls. Speech analytics reveals what is happening across 100% in near-real-time — including the phrases top performers use to close calls that struggling agents do not.

Complete interaction coverage, 60-minute delivery of analytics results, root-cause visibility into performance and compliance trends, top-performer pattern identification for coaching replication, and early detection of product or process issues from customer feedback signals. Speak to a Convin product specialist at convin.ai/demo.

Insurance (IRDAI compliance and renewal pattern analysis), BFSI (collections performance and RBI disclosure verification), EdTech (admissions counsellor effectiveness), healthcare (patient communication review), and telecom (churn signal detection in retention calls).

Traditional contact centre analytics are based on sampled data, require manual compilation, and take 24-72 hours to produce. Speech Analytics for Call Centers processes 100% of interactions automatically and delivers results within 60 minutes — providing complete rather than partial coverage at a fraction of the reporting effort.

100% interaction transcription via ASR, NLP tagging for quality, compliance, intent, and sentiment signals, ML-based pattern detection and trend analysis, BI aggregation layer for dashboard visualisation, and data export APIs for integration with external BI tools (Tableau, Power BI).

Yes. Analytics surface the root causes of poor customer experience — the specific call types, agent behaviours, and process breakpoints that drive repeat contacts, escalations, and low CSAT scores. Operations teams use this to make targeted improvements rather than broad, generic training investments.

Yes. Analytics identify the highest-cost interaction patterns — repeat contacts, escalations, long AHT drivers, compliance deviations — enabling targeted interventions that reduce those patterns specifically rather than applying broad improvements with diluted ROI.

Via API integration with existing telephony (Genesys, Avaya, Cisco, AWS Connect) and CRM (Salesforce, HubSpot, Zoho) — 2-3 week deployment timeline managed by Convin's customer success team. No rip-and-replace of existing infrastructure required. QA scorecards, compliance rules, and coaching frameworks are configured during onboarding. Speak to a Convin product specialist at convin.ai/demo.