Deep-Learning-Based Customer Complaints Monitoring System Using Online Review

Abstract

Customer complaint monitoring has been considered essential for service quality management among the voice of customers. This study proposes an advanced method of customer complaint monitoring with deep learning and explainable AI (XAI) techniques to manage service quality effectively in a rapidly evolving industry. Online reviews have been analyzed for customer-centric service quality management. Researchers are advancing methods of customer complaints monitoring by integrating these online reviews with SPC to monitor effectively in rapidly evolving industries. We are developing the method and introducing a new framework to manage service quality effectively. We utilize a BERT-based model to extract customer-perspective service features from online reviews and analyze customer sentiment for each feature. The results of this analysis are calculated as an improved customer complaints index and displayed as a staged p-chart. Unlike traditional customer complaints charts, our improved chart enables close monitoring in the seasonal industry. We also analyze negative review texts with an XAI model to identify the causes of poor service quality from the customer's perspective. Our approach alleviates the limitations of NLP techniques in traditional online review analysis and allows us to identify the root causes of poor quality previously considered less critical.

Keywords: online review analysis, Statistical Process Control (SPC), Explainable AI (XAI), deep learning, voice of customer (VoC)