Customer Service Improvement based on Electricity Payment Behaviors Analysis using Data Mining Approaches

Thailand Provincial Electricity Authority (PEA) has 19.36 million residential customers and approximately 73% of them have late or unpaid electricity payments. This causes several problems including reduction of PEA's income. Therefore, we aim to classify and find data mining algorithms to predict classes of electricity payment behaviors of residential customers in order to improve customer service and to reduce the number of late or unpaid payment of electrical bills. In this work, the data set consists of payment behaviors and demographic characteristics of customers, which are gathered from 1,079 customers. The prediction process is divided into two main parts: clustering classes of customers by using k-means algorithm, and using data mining algorithms for customer model prediction. We performed experiments using five data mining algorithms: logistic regression, decision tree, random fores, support vector machine (SVM) and XGBoost. Our results show that the random forest algorithm is a good choice for this predictive classes of customers to develop appropriate strategies to increase satisfaction of customers and revenue of PEA.