Authors: Hafizah Farhah Saipan Saipol; Sulfeeza Mohd Drus; Marini Othman
Addresses: Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Jalan IKRAM – UNITEN, 43000 Kajang, Selangor, Malaysia; Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia ' College of Computer Science and Information Technology, Universiti Tenaga Nasional, Jalan IKRAM – UNITEN, 43000 Kajang, Selangor, Malaysia ' Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Jalan IKRAM – UNITEN, 43000 Kajang, Selangor, Malaysia
Abstract: Fulfilling customer satisfaction gives significant impact to any business including in the electricity industry. The key to achieving customer satisfaction is by providing them the best quality services at fair and reasonable costs. Customer complaints must be managed professionally and appropriately, and be leveraged to improve the quality of services and operational efficiency. In this regard, identifying the root cause of the problems becomes paramount in improving customer service for future improvement. The accumulated customer complaints generate massive data which can be fully utilised by using big data analytics. The purpose of this paper is to determine frequent customer complaint regarding electricity issue and review various methods of big data analytics that have been used to identify valuable insights within the data and to analyse the pattern that can be useful to find solutions to the problem, thus improving the electricity industry services especially in terms of complaint management. On the basis of a study of the different researches, different techniques of machine learning have been used because of its accuracy and in finding a pattern to solve the relevant electrical problem such as predicting power demand, managing power loads, and enhancing strategic planning.
Keywords: customer complaints; customer satisfaction; electricity; electricity industry; distribution; insight; big data; big data analytics; billing; power quality; electricity safety.
International Journal of Business Continuity and Risk Management, 2021 Vol.11 No.2/3, pp.208 - 223
Received: 25 May 2019
Accepted: 02 May 2020
Published online: 06 Jul 2021 *