An energy and utilities leader faced difficulties in evaluating the performance of their extensive call center network. Their manual process, which assessed only 4% of calls through time-consuming listening and scoring using a 25-point rubric, limited their ability to gain valuable insights. This inefficiency impeded efforts to improve customer service quality and increased operational costs.

Leveraging Google Cloud’s AI/ML capabilities, call center agent evaluations were automated using speech-to-text, sentiment analysis, and summarization. Google’s Text-Bison model assessed transcriptions against predefined rubrics and analyzed sentiment to evaluate interactions. All calls were efficiently analyzed, eliminating manual effort. Cloud Functions and GCS automated the process, storing data in BigQuery. Managers could focus on coaching, with insights visualized in a Looker Studio dashboard.

Comprehensive Call Coverage: Automated analysis of all calls ensured complete visibility into agent performance, enhancing overall insights.

Voice of Customer Insights: Delivered in-depth understanding of customer interactions, driving meaningful operational improvements.

Manager Efficiency: Streamlined processes freed managers from manual tasks, enabling them to concentrate on coaching and quality improvements.

Increased Scalability: Provided consistent evaluations and scalable solutions, improving efficiency across all call center teams.