AI Agent Behavior Analysis

Automated analysis of agent behaviour patterns across 100% of interactions — identifying how agents handle objections, deliver required disclosures, use empathy, apply resolution skills, and adhere to scripts across all call types.

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.

Manual behaviour analysis covers a tiny sample of calls and is subject to observer effect (agents behave differently when they know they are being monitored). AI analysis covers every call without the observer effect.

Complete behaviour pattern analysis across all calls, identification of specific behaviours that drive high and low outcomes, compliance behaviour monitoring, and insights that enable targeted behaviour-change coaching. 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 contact centre analytics are based on sampled data, require manual compilation, and take 24-72 hours to produce. AI Agent Behavior Analysis 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.