Improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing
This work addresses customer satisfaction and retention for telecommunications companies, but it is incremental as it applies an existing machine learning approach to a specific domain.
The paper tackled the problem of optimizing customer-agent pairing in call centers to improve customer experience, achieving a 215% performance improvement compared to a rule-based method.
Customer experience plays a critical role for a profitable organisation or company. A satisfied customer for a company corresponds to higher rates of customer retention, and better representation in the market. One way to improve customer experience is to optimize the functionality of its call center. In this work, we have collaborated with the largest provider of telecommunications and Internet access in the country, and we formulate the customer-agent pairing problem as a machine learning problem. The proposed learning-based method causes a significant improvement in performance of about $215\%$ compared to a rule-based method.