Data Collection, data mining and transfer of learning based on customer temperament-centered complaint handling system and one-of-a-kind complaint handling dataset

Ching-Hung Lee, Xuejiao Zhao

Abstract: One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled “3D” model for customer complaint data analysis. Three phases are (1) Development and launch of e-Customer Complaint Handling system, (2) Data collection and transfer of learning by e-Customer Complaint Handling system, and (3) Data mining by e-Customer Complaint Handling system. An advanced electronic Customer Complaint Handling System called the e-CCH system was then developed and launched. This system adapts the seasonal associations model based on Hippocrates's customer temperament theory to the whole stages of customer complaint reporting and handling. With this system, we conducted a dataset collection work from restaurant chains of two brands over four years. As a result, we collect thousands of real-world temperament-centred customer complaint cases by four years to form the one-of-a-kind CCH dataset. This one-of-a-kind CCH dataset was open-sourced with detailed customer complaint attributes and heuristic decision-making for valuable industrial handling manner. After further analysis of this dataset, we found that customers with different temperament types tend to have different types of complaints. In addition, adapting the temperament theory to the e-CCH system can classify customer types better and provide personalized solutions. To our best knowledge, this rich and the one-of-a-kind CCH dataset reported in this paper is the first comprehensive study of customer complaint handling in an industrial service management context. Meanwhile, data mining with cross analysis and correspondence analysis and an ChatGPT experiment for transfer of learning based on this yearly and one-of-a-kind industrial customer complaint dataset was analyzed and discussed. In addition, how this dataset may contribute to more realistic complaint-handling theoretic studies for better service failure recovery and interactive marketing is discussed in-depth.