Open AccessImproving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms
Publication year - 2022
Publication title -
journal of global information management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.315
H-Index - 41
eISSN - 1533-7995
pISSN - 1062-7375
DOI - 10.4018/jgim.20220801oa02
Subject(s) - computer science , customer lifetime value , data mining , index (typography) , customer value , entropy (arrow of time) , profiling (computer programming) , algorithm , consumption (sociology) , customer retention , marketing , business , service quality , economics , market economy , social science , physics , hierarchy , quantum mechanics , sociology , world wide web , service (business) , operating system
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.