A Multi-criteria Decision Making Model Integrated with Speech Analytics for the Performance Evaluation of Agents in Express Delivery Industry

In the cargo transportation sector, the complaint rate and the resolution time of customer complaints significantly impact both customer satisfaction and financial performance. In this regard, an accurate analysis of customer representatives’ performance in handling incoming calls is of critical importance. Traditional performance metrics such as the number of answered calls, average call duration, and average response time to customer complaints do not fully reflect the performance of customer representatives. For a thorough analysis of customer satisfaction after calls, there is a need for speech analytics-based criteria that assess the success of the conversation, in addition to traditional performance metrics. Furthermore, the utilization of performance metrics like the duration of holding on during customer interactions, interruption frequency, rate of using standard expressions, the emotional state of the customer (angry or happy), and the count of customer acknowledgments is highly advantageous for the analysis of customer representative performance and identification of potential areas for improvement. Thus, in this study, we aim to put forward an integrated approach of a multi-criteria decision-making model by using Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Grey Relational Analysis to incorporate both traditional performance metrics and criteria derived from speech analytics to evaluate customer representative/agent performance. A case study of a express delivery company is presented to illustrate the agent performance evaluation by using an integrated approach.