The client, a leading banking and financial services company, operating a large travel and loyalty platform sought to analyze and enhance agent quality scores. The goal was to take corrective measures that would improve customer satisfaction scores (CSAT), net promoter scores (NPS), and service level agreements (SLA). The current process of agent scoring is manual and limited to a couple of calls per agent per month by the supervisors. The process was not scalable, accurate, or consistent and offered minimal coverage to produce helpful insight. The client also wanted to find productivity improvements without compromising on the KPI's.

ElectrifAi's ContactCenterAi suite of Machine Learning products includes call scoring and sentiment analysis, to score agent performance against key call quality parameters and provide fast and accurate feedback. A proprietary, scalable voice-to-text product improves the accuracy of data analysis resulting in more precise insights and feedback. ContactCenterAi generates agent insights to mentor and train agents to improve customer satisfaction and KPI's. The call summarization product generates automated call summaries improving productivity and delivering immediate cost savings.