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Predicting customer satisfaction for distribution companies using machine learning

Predicting customer satisfaction for distribution companies using machine learning This study aims to support electricity distribution companies on measuring and predicting customer satisfaction.Design/methodology/approachThe 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.FindingsThe results on the predicted main indicator diverged only by 1.36 per cent of the results obtained by the survey with company customers.Research limitations/implicationsSocial, economic and political conjunctures of the regional and national scenario can influence the indicators beyond the input variables considered in this paper.Practical implicationsCurrently, 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.Originality/valueDevelopment of an intelligent algorithm that can improve its performance with time. Understanding customer satisfaction may improve companies’ performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy Sector Management Emerald Publishing

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References (30)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1750-6220
eISSN
1750-6220
DOI
10.1108/ijesm-10-2018-0007
Publisher site
See Article on Publisher Site

Abstract

This study aims to support electricity distribution companies on measuring and predicting customer satisfaction.Design/methodology/approachThe 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.FindingsThe results on the predicted main indicator diverged only by 1.36 per cent of the results obtained by the survey with company customers.Research limitations/implicationsSocial, economic and political conjunctures of the regional and national scenario can influence the indicators beyond the input variables considered in this paper.Practical implicationsCurrently, 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.Originality/valueDevelopment of an intelligent algorithm that can improve its performance with time. Understanding customer satisfaction may improve companies’ performance.

Journal

International Journal of Energy Sector ManagementEmerald Publishing

Published: Jul 26, 2021

Keywords: Artificial intelligence; Energy sector; Regression; Electricity; Residential; Distribution; Correlation analysis; Operation management; Machine learning; Performance measurement; Performance management; Power distribution; Customer satisfaction

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