Harnessing Large Language Models for Enhanced Customer Experience
summary
Claims concrete outcomes: a cloudnative platform running edge and Kubernetes deployments that processes billions of transactions and integrates LLM chatbots, ML analytics and sentiment models to cut support load; a banking assistant handling 1 billion+ interactions and producing 30% call reduction in inbound voice; telecom broadband systems that eliminate thousands of tier1/tier2 seats through AI‑enhanced self‑help and proactive rebooking. Emphasis on measurable metrics: AI deflection rate, first‑call resolution, agent retention, and human escalation paths.
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chapters (11)
- 0:00Introduction and Speaker Background
- 1:07Technological Evolution in CX
- 3:10AI and Machine Learning in CX
- 10:35Personalized Customer Interactions
- 12:56Proactive Issue Resolution
- 14:21Enhanced Self-Help Solutions
- 15:42Efficient Resource Allocation
- 18:40Sentiment Analysis and Case Studies
- 24:52Metrics and Measurement
- 29:19Future of AI in Customer Experience
- 29:54Conclusion and Final Thoughts
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