Abstract
The lack of management control and not aware about their potential customer is becoming a growing concern of the service provider. The problem of the late payment cannot be solved simply by providing more penalty and spending more money on management. There is an urgent need for upgrading, for better understanding of the current and potential electricity customers to meet the needs in modern urban life. Hence, this study proposed an integrated data mining and customer behaviour scoring model to manage existing tenants at Empire Damansara. This segmentation model was developed to identify groups of customers based on their electricity payment transaction background of history. Thus, the developer or provider can develop its intensive actions that can maintain its incomes and keep high customers’ satisfaction.
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Yusoff, F.H.B., Rosman, N.L.A.B. (2019). A Case Study of Customers’ Payment Behaviour Analytics on Paying Electricity with RFM Analysis and K-Means. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_4
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