Case Study: Harnessing Generative AI to Transform Customer Support Intelligence

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Executive Summary

An Australian health insurance provider adopted a generative AI-powered insights platform to modernise its contact centre operations. By automating analysis across all customer interactions, the organisation achieved 92% categorisation accuracy, eliminated ambiguous call drivers, and reduced investigation times by 70%, enabling faster and more effective decision-making.

Customer Context

The client is a well-established health insurance organisation supporting a large and diverse member base across Australia. With thousands of daily enquiries across claims, policy updates, and service requests, the contact centre generated valuable customer data. However, without the right tools, this data remained underutilised, limiting both operational efficiency and customer experience improvements.

Challenge

Despite having a high volume of interaction data, the contact centre struggled to extract meaningful, timely insights. Traditional analysis methods were slow, resource-heavy, and often produced inconsistent results. Key challenges included:

  • Manual tagging by agents, leading to inaccuracy and inconsistency.
  • Reliance on sample-based call listening, providing limited visibility.
  • Investigations into performance shifts (e.g., call volumes, resolution time) were time-consuming and inconclusive.
  • Leaders lacked confidence in answering critical questions such as:
    “What’s driving this sudden increase in claim enquiries?”

Solution

To unlock real-time intelligence, we designed and deployed a Generative AI-powered insights platform tailored for the client’s contact centre operations:

  • Multi-Channel Data Capture – Analysed transcripts across voice, chat, and email channels.
  • Automated Interaction Categorisation – Applied a custom disposition model across every enquiry, categorising by claim type, policy changes, and service-related issues.
  • KPI Correlation – Linked conversational data directly with operational metrics such as average handle time, first-call resolution, and contact volumes.
  • Real-Time Alerts – Anomaly detection surfaced unusual trends instantly, enabling proactive intervention.
  • AI-Driven Reporting – Delivered consistent, detailed insights at scale, improving transparency across all service lines.

Outcome

The platform delivered immediate and measurable benefits:

Reduced “unknown call reasons” to 0%, down from 15%.
Achieved 92% tagging accuracy, outperforming manual agent coding.
70% faster investigation times, enabling quicker root cause identification.
Real-time anomaly detection allowed proactive resolution of emerging issues.
Improved CX outcomes through faster decisions and more consistent service delivery.

Strategic Impact

This initiative elevated the client’s contact centre into a data-driven decision hub. Instead of relying on reactive troubleshooting, leaders can now anticipate challenges, allocate resources more effectively, and respond to member needs in near real-time. The solution has laid the foundation for scaling AI-driven service intelligence across other areas of the organisation, reinforcing both operational resilience and customer trust.

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