Contact Center Automation

Business Objective

A leading supplementary health insurance provider was facing low NPS scores due to issues in handling a large volume of inbound calls. The resolution was highly manual and the digital channel like chatbots was not intelligent enough to handle requests which were largely routed to human operators. The customer was looking at improvement in chatbots handling of queries, the solution to drive 24x7 digital customer interactions, decreased live person customer interaction, and seamless solution integration with case management & enterprise systems

Solution Summary

Coforge solution uses advanced AI and ML methods including:

  • Machine Learning (ML) and Natural Language Processing (NLP) techniques to gain insights from chat transcripts to identify handoff triggers from Chatbots to Live Agents
  • Topic modeling using algorithms such as LDA and LSA to understand key discussion topics
  • Identify and understand top topics and scenarios that cause handoff from Chatbots to Live Agents
  • Understand context, scenarios, and topics in chat transcripts that frequently cannot be handled by existing Chatbots
  • Use this understanding as a key input to enhance Chatbots and made it more sophisticated to handle such topics and scenarios
  • Reduce workload on Live Agents enabling them to focus on more value-adding areas to serve the customer
  • Overall improvement of customer experience and operational efficiency

Value Delivered

  • 100K+ Chat interactions through AI Chat in first 3 months
  • 58% (and growing) interactions contained in AI Chat
  • 80% cost savings through call avoidance
  • Average response time to emails reduced from 8 hours to < 1 hour
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