TRANSFORMING CUSTOMER COMPLAINTS INTO OPERATIONAL INSIGHT IN AFTERSALES SERVICE OPERATIONS

Abstract

The rapid growth of digital transformation has encouraged organizations to utilize operational data as a strategic asset for service improvement and decision support. In aftersales service operations, customer complaint data contains valuable information that can support service quality enhancement, operational responsiveness, and customer experience improvement. This study aims to explore the implementation of digital customer complaint intelligence using historical complaint data collected from aftersales service activities over a 29-month period. The dataset consists of various complaint categories, spare part types, product models, and customer segments. Using a data-driven analytical approach, this study identifies complaint patterns, dominant service issues, temporal trends, and operational insights that may support managerial decision-making processes. The findings indicate that customer complaint data can function not only as a reactive service record but also as a strategic digital intelligence resource for preventive action and operational performance improvement. This research contributes to the growing discussion on digital service analytics and provides practical implications for organizations seeking to strengthen aftersales service effectiveness through data-driven approaches.