Business challenge
The business delivers complex services that are often time-sensitive, regulated, or high-emotion — including:
Delivery across national and international networks
Identity verification in line with government requirements
Clarification of government and financial services
Despite existing digital capabilities, the complexity resulted in escalations due to:
Difficulty matching new customer inquiries
Low automation rates for emotionally charged or complex requests
High agent workloads and inconsistent self-service success
Solution
AI-Powered Intent & Sentiment Analysis
To address these challenges, the organisation partnered with fullstop.ai to undertake a universal intent and sentiment analysis across multiple (7) voice and digital channel. Leveraging millions of lines of transcript data, the analysis aimed to map real customer needs to automation opportunities and improve digital resolution outcomes.
Key Capabilities
AI-Driven Clustering: Identified 30 distinct topics across interactions, using semantic grouping and NLU
Sentiment Analysis: Measured customer emotion scale to surface high-frustration and high-effort interactions
Engagement Metrics: Tracked escalation rates, bot disclosure rates, and AHT to prioritise optimisation
Results linked to key operational metrics, such as service to provide richer insights based on operational context
CX Diagnostics: Identified quick wins for automation uplift and Pinpointed improvements in assistant design
Recommendations Engine: Produced a roadmap of new intents, content enhancements, and escalation handling improvements
Prioritisation Matrix: Recommendations were prioitised against impact and effort, providing targeted recommendations of where to start
Key benefits & impacts
Delivers deep insight into customer needs and pain points by mapping real-world conversations to clear, actionable intents
Highlights the highest-volume and lowest-sentiment inquiries to guide automation and service design efforts
Enables a prioritised roadmap of CX and automation initiatives based on volume, complexity, and sentiment
Establishes a repeatable framework for ongoing optimisation of virtual assistant coverage and effectiveness
Operational Efficiency & Cost Savings
Supports smarter self-service investment by aligning automation initiatives with measurable business impact
Enables faster, more focused CX and automation delivery by prioritising initiatives with clear business impact, validated benefits, and measurable value realisation
Conclusion
By applying large-scale intent and sentiment analysis, the organisation transformed unstructured voice data into actionable intelligence. This initiative lays the foundation for more trusted, empathetic, and scalable service delivery — powered by listening at scale.