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

Title: Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms
Creator: Wu, Zongxiao; Zang, Cong; Wu, Chia-Huei; Deng, Zilin; Shao, Xuefeng; Liu, Wei
Relation: Journal of Global Information Management Vol. 30, Issue 3, no. 1
Publisher Link: http://dx.doi.org/10.4018/JGIM.20220701.oa1
Publisher: IGI Global
Resource Type: journal article
Date: 2022
Description: 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.
Subject: computing; consumer; consumption forecast; data mining; K-Means Clustering Anaysis; marketing strategy
Identifier: http://hdl.handle.net/1959.13/1454064
Identifier: uon:44815
Identifier: ISSN:1062-7375
Language: eng
Reviewed