Abstract
Customer loyalty is a crucial factor for retail business success. This paper illustrates an AI approach, named CENTAURO, to learn customer loyalty prediction models that may help retailers to run powerful loyalty programs and take better decisions. In particular, the proposed approach learns a classification model from the Recency, Frequency and Monetary (RFM) value of historical customer shopping data. For this purpose, the RFM model is extended to monitor Recency, Frequency and Monetary both over time and over the various categories of products purchased. Experiments performed with a benchmark dataset explore the performance of the extended RFM model in combination with several classification algorithms (e.g., Logistic Regression, Multi-Layer Perceptron, Random Forest, Decision Tree and XGBoost). Finally, we use an eXplainable Artificial Intelligence (XAI) technique – SHAP – to explore the effect of RFM values on the customer loyalty profile learned through the classification model.
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Acknowledgment
The work of Giuseppina Andresini and Vincenzo Pasquadibisceglie was supported by the project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI, under the NRRP MUR program funded by the NextGenerationEU. The work of Annalisa Appice, Pasquale Ardimento, Donato Malerba and Giuseppe Ieva was in partial fulfilment of the research objectives of the Research Contract “LUTECH DIGITALE 4.0: Progetto di Tecniche di Machine Learning predittivo per la piattaforma di Loyalty Management” within the project “LUTECH DIGITALE 4.0”.
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Andresini, G. et al. (2023). CENTAURO: An Explainable AI Approach for Customer Loyalty Prediction in Retail Sector. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science(), vol 14318. Springer, Cham. https://doi.org/10.1007/978-3-031-47546-7_14
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DOI: https://doi.org/10.1007/978-3-031-47546-7_14
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