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January 17, 2026International Journal of Information Technology

Leveraging advanced machine learning with feature selection approach for robust customer segmentation with purchase behavior prediction

R. Somasundara Manikandan S. Santhosh Kumar J. Jegathesh Amalraj

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Key Points

Purchase behavior prediction relies heavily on feature selection to optimize model performance and accuracy.
The predictive modeling shows a significant improvement in customer segmentation compared to traditional methods, increasing targeting efficiency.
Analysis integrates innovative machine learning techniques with rigorous feature selection to refine customer insights and segmentation.
These findings may enable businesses to create more tailored marketing strategies, enhancing customer engagement and revenue generation.

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Implication

Observational analysis leverages advanced machine learning to enhance customer segmentation, suggesting improved targeting strategies.

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Cite This Study

Manikandan et al. (2026) studied this question.

synapsesocial.com/papers/69a75a04c6e9836116a1f7d6 https://doi.org/10.1007/s41870-025-03061-3

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1RFM ranking – An effective approach to customer segmentationOriginal Research · 2018 · 211 citations
2RFM model for customer purchase behavior using K-Means algorithmOriginal Research · 2019 · 207 citations
3A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted MarketingOriginal Research · 2023 · 49 citations
4Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori AlgorithmOriginal Research · 2019 · 48 citations
5LRFS: Online Shoppers’ Behavior-Based Efficient Customer Segmentation ModelOriginal Research · 2024 · 8 citations

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