AXA reduces average handle time by 23% with Verint Open Platform speech and desktop analytics
A documented Customer Service & Chatbots in Property & Casualty deployment at AXA, with source-attributed results and missing evidence labelled explicitly.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: www.verint.com
The Challenge
AXA UK, part of one of the world's largest insurance groups with a global customer base, faced a fragmented analytics environment across its retail business unit covering customer service, claims, and complaints. The team already relied on Verint Speech Analytics and Workforce Management, but desktop analytics ran on a separate third-party platform, forcing expensive data integration and eroding confidence in the resulting insights. In Property & Casualty, where renewal rates and claims efficiency directly drive combined ratios, this blind spot was costly: only a small proportion of voice calls received manual quality review, leaving service defects and non-compliant interactions undetected. All three business divisions struggled with excessive average handle times, with no unified view to diagnose root causes.
The Solution
AXA migrated its retail contact centre to the Verint Open Platform, consolidating speech analytics and desktop & process analytics under a single connected environment powered by the Verint Da Vinci AI engine — a cloud-hosted NLP model that achieves 95% transcription accuracy. During redeployment, call categories were rebuilt as smaller, process-aligned modules, reducing unclassified calls from 20% to 8%. Verint Performance and Compliance Scoring Bots were layered on top for automated quality management across 100% of interactions rather than manual sampling. End-to-end journey analysis was applied across three high-impact workflows: the customer renewal journey (where NLP identified 'bargainer' versus direct-accept paths), home claims supplier contacts, and a new CRM rollout that had inadvertently spiked handle time by 25%. Desktop analytics provided real-time process telemetry to diagnose and remediate each issue.
Results
Within two months of go-live, AXA delivered measurable improvements across all three workflows:
- 23% reduction (182 seconds) in Average Handle Time on the customer renewal journey, achieved by streamlining and selectively bypassing renewal scripts
- 34% reduction in supplier call volume to the home claims team, by redirecting vendors to self-service portals
- First Contact Resolution improved from 87% to 93% in the service business unit
- Home claims NPS up 97%; auto claims NPS up 26% following process redesign
Qualitatively, the platform enabled AXA to rapidly diagnose a CRM-driven AHT spike — a problem that would previously have taken weeks to surface — and implement targeted agent coaching based on live desktop and speech data.
Key Takeaways
- Consolidating speech and desktop analytics on a single platform eliminates integration overhead and produces a trustworthy, end-to-end view of customer journeys that siloed tools cannot provide.
- Granular journey decomposition — breaking renewal, FNOL, and claims flows into discrete sub-steps — reveals specific process levers that aggregate metrics obscure.
- Automated quality scoring across 100% of interactions replaces sampling-based review, surfacing compliance gaps and coaching opportunities that manual audits miss.
- Real-time desktop analytics should be treated as a change-management tool: when new systems (e.g., CRM rollouts) cause unexpected AHT spikes, process telemetry enables rapid diagnosis rather than prolonged investigation.
Explore Related
Details
- Industry
- Property & Casualty
- Use Case
- Customer Service & Chatbots
- AI Technology
- NLP
- Company Size
- Enterprise
- Company
- AXA
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
Cited source
www.verint.comHave a similar implementation?
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