Deciphering modern customer loyalty: a machine learning approach

Kechen Li; Chang Xu; Zheyuan Zhao; Mengyu Zhu; Xiangxuan Cui; Shurui Xu; Jichen Zou

doi:10.1117/12.3013297

29 November 2023 Deciphering modern customer loyalty: a machine learning approach

Kechen Li, Chang Xu, Zheyuan Zhao, Mengyu Zhu, Xiangxuan Cui, Shurui Xu, Jichen Zou

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371O (2023) https://doi.org/10.1117/12.3013297
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In the contemporary retail sector, deciphering customer behavior is crucial for businesses vying for a competitive advantage. While customer loyalty has conventionally been gauged through parameters such as repeat purchases and promotional response, the merging of technology, media, and commerce has necessitated a reevaluation of these benchmarks. This study recognizes the inadequacy of solely relying on traditional transactional data to evaluate loyalty. Utilizing Machine Learning (ML), we crafted a novel model to predict customer loyalty, taking into account a plethora of modern factors, including online behavior and social media interactions. This research leveraged a diverse dataset and sophisticated ML algorithms to identify subtle patterns and relationships. Key findings include the significance of online behavior in loyalty prediction, the emerging role of social media interactions, and the nuanced influence of device preferences on brand loyalty. Notably, this manuscript offers a pioneering ML model that accurately reflects the complexities of the modern retail landscape, providing a valuable tool for businesses seeking to foster enduring customer relationships.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kechen Li, Chang Xu, Zheyuan Zhao, Mengyu Zhu, Xiangxuan Cui, Shurui Xu, and Jichen Zou "Deciphering modern customer loyalty: a machine learning approach", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371O (29 November 2023); https://doi.org/10.1117/12.3013297

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