This study aims to support electricity distribution companies on measuring and predicting customer satisfaction.
The developed methodology selects and applies machine learning techniques such as decision trees, support vector machines and ensemble learning to predict customer satisfaction from service data, power outage data and reliability indices.
The results on the predicted main indicator diverged only by 1.36 per cent of the results obtained by the survey with company customers.
Social, economic and political conjunctures of the regional and national scenario can influence the indicators beyond the input variables considered in this paper.
Currently, the actions taken to increase customer satisfaction are based on the track record of a yearly survey; therefore, the methodology may assist in identifying disturbances on customer satisfaction, enabling decision-making to deal with it in a timely manner.
Development of an intelligent algorithm that can improve its performance with time. Understanding customer satisfaction may improve companies’ performance.
This research was supported by COPEL (power utility from the state of Paraná, Brazil), under the Brazilian National Electricity Agency (ANEEL) R&D program PD 2866-0370/2013.
Cavalcante Siebert, L., Bianchi Filho, J.F., Silva Júnior, E.J.d., Kazumi Yamakawa, E. and Catapan, A. (2021), "Predicting customer satisfaction for distribution companies using machine learning", International Journal of Energy Sector Management, Vol. 15 No. 4, pp. 743-764. https://doi.org/10.1108/IJESM-10-2018-0007
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