Title: Categorising customers and predicting their buying patterns taking into account the customer evaluation model RFM

Authors: Feisal Alaswad; B. Muruganantham; Amer Bouchi

Addresses: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India ' Department of Computer Engineering, Aleppo University, Aleppo, Syria

Abstract: Choosing the best marketing strategies and understanding customer behaviour is what drives many companies to resort to analysing these behaviours. There are several approaches and perspectives to reach a deep understanding. In this work customers were categorised based on their buying pattern taking into account the loyalty score which was calculated using the RFM model. Mainly it is represented by three factors: recency, frequency and monetary. After performing the categorisation process, several models are trained and used to predict the cluster of the customers. In this paper, the proposed models and their results will be discussed in order to reach the best model. We also show that after discussing the models, it was found that the best model was the gradient boosted decision tree that achieved a prediction accuracy of 97% in the testing phase. By relying on this work, it was found that it is possible to classify and predict the customers' class according to their buying pattern and the level of their loyalty to the store.

Keywords: customer relationship management; CRM; customer segmentation; data mining; RFM model; machine learning; marketing strategy.

DOI: 10.1504/IJBFMI.2021.120152

International Journal of Business Forecasting and Marketing Intelligence, 2021 Vol.7 No.2, pp.124 - 142

Received: 11 Feb 2021
Accepted: 03 Jun 2021
Published online: 07 Jan 2022 *

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