Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Zongxiao Wu, Cong Zang, Chia-Huei Wu, Zilin Deng, Xuefeng Shao, Wei Liu

Source Title: Journal of Global Information Management (JGIM)30(3)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799897279|DOI: 10.4018/JGIM.20220701.oa1

MLA

Wu, Zongxiao, et al. "Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms." JGIM vol.30, no.3 2022: pp.1-23. http://doi.org/10.4018/JGIM.20220701.oa1

APA

Wu, Z., Zang, C., Wu, C., Deng, Z., Shao, X., & Liu, W. (2022). Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms. Journal of Global Information Management (JGIM), 30(3), 1-23. http://doi.org/10.4018/JGIM.20220701.oa1

Chicago

Wu, Zongxiao, et al. "Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms," Journal of Global Information Management (JGIM) 30, no.3: 1-23. http://doi.org/10.4018/JGIM.20220701.oa1

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Abstract

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.