Conversational AI Analytics

A capability that automatically processes 100% of conversation data — voice, chat, email — to surface patterns in agent performance, customer intent, compliance adherence, and operational efficiency. Convin customers use it to identify exactly what top-performing agents do differently and replicate it at scale.

Convin processes every interaction through ASR transcription and NLP tagging — extracting quality signals, compliance outcomes, intent patterns, and sentiment data from 100% of calls. These tagged data points aggregate into analytics dashboards that managers can interrogate at the trend level or drill down to individual call evidence.

To move from intuition-based management to evidence-based operations. Instead of guessing why CSAT dropped or which agents are underperforming, analytics show the exact conversation patterns — with call-level evidence — that drive outcomes good or bad.

Complete conversation coverage, root-cause visibility into repeat contacts and escalations, top-performer pattern identification for coaching replication, automated compliance trend monitoring, and early detection of product or process issues from customer feedback signals. Speak to a Convin product specialist at convin.ai/demo.

Insurance (mis-selling pattern detection and compliance trend analysis), BFSI/NBFCs (collections outcome analytics and RBI compliance tracking), EdTech (enrollment conversion analytics and counsellor performance insights), healthcare (patient communication quality analytics), and e-commerce (repeat-contact root-cause analytics and FCR trending).

Traditional reporting is based on sampled call data and manual tagging. Conversational AI Analytics processes 100% of interactions automatically, enabling trend analysis at a volume and speed no manual team can match.

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.