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<p>Customer segmentation involves clustering customer data by shared behaviours, which can assist businesses anticipate purchasing behaviour, identify potential customers, and create targeted marketing campaigns. Business organizations can improve resource allocation by comprehending diverse customer group. Deep learning (DL) models ease segmentation by discovering hidden patterns in intrinsic datasets. Techniques like deep clustering, autoencoders (AE), and neural networks capture nonlinear relationships in customer data for more accurate and dynamic segmentation. In this study, an Enhanced Sand Cat Swarm Optimisation with Deep Learning Assisted Customer Segmentation with Purchase Behaviour Analytics (ESCSODL-CSPBA) technique is proposed. Initially, the Recency, Frequency, Monetary, and Repurchasing Number of Times (RFM-RN) model is utilized. Additionally, ESCSO and DBSCAN models are employed for feature subset selection and clustering. Finally, the Convolutional Recurrent Neural Network (CRNN) technique is implemented for classification. The comparison study of the ESCSODL-CSPBA approach depicted superior accuracy of 95.90% compared with existing models on the online retail dataset.</p>

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Harnessing enhanced sand cat swarm optimisation with deep learning assisted customer segmentation with purchase behaviour analytics on the retail industry

  • 期刊论文

  • 发表日期：

    2026年1月14日

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

摘要

Customer segmentation involves clustering customer data by shared behaviours, which can assist businesses anticipate purchasing behaviour, identify potential customers, and create targeted marketing campaigns. Business organizations can improve resource allocation by comprehending diverse customer group. Deep learning (DL) models ease segmentation by discovering hidden patterns in intrinsic datasets. Techniques like deep clustering, autoencoders (AE), and neural networks capture nonlinear relationships in customer data for more accurate and dynamic segmentation. In this study, an Enhanced Sand Cat Swarm Optimisation with Deep Learning Assisted Customer Segmentation with Purchase Behaviour Analytics (ESCSODL-CSPBA) technique is proposed. Initially, the Recency, Frequency, Monetary, and Repurchasing Number of Times (RFM-RN) model is utilized. Additionally, ESCSO and DBSCAN models are employed for feature subset selection and clustering. Finally, the Convolutional Recurrent Neural Network (CRNN) technique is implemented for classification. The comparison study of the ESCSODL-CSPBA approach depicted superior accuracy of 95.90% compared with existing models on the online retail dataset.