Enhancing Call Center Operations & Customer Experience through Contact Center Text/Voice Analytics

Business Objective

Our client is one of the largest P&C insurance providers in the US receiving tens of millions of calls annually into their call centers. Less than 1% of the calls are manually reviewed by the client’s Quality Assurance team. The existing models predict a limited number of call intents. The client wanted to

  • Assess the quality of interactions with customers across multiple dimensions and automate compliance measurement using ML models
  • Identify customer call intents and opportunities to refine customer interactions for improved call center operations productivity and customer experience
  • Transcription and diarization errors
  • Unavailability of Baseline metrics for several key Business KPIs
  • Unavailability of PII data to verify customers’ information and policy
    details from transcripts
  • Insufficient labeled data for some use cases

Solution Methodology
  • Leveraged call transcript, keyword corpus data, and compliance rules.
  • Leveraged call transcripts, call data, SOPs/FAQs etc. Pre-processed the data using sentence segmentation, lemmatization, etc.
  • Leveraged call transcript data and converted client SOPs into scenario database.
  • Built a Detection engine to extract call intent, state, and product.
  • Generated chunks (combination of conversations) from preprocessed call transcripts.
  • The final model provided sentiment prediction for the call chunks which could be summarized at the call level
Business Impact
  • Automated authentication compliance measurement of interactions with customers

  • Identified customer call intent at the initial stage leading to faster and accurate resolution

  • Identified inappropriate transfer of calls

  • Automated evaluation of customer sentiment throughout the call

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