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<p>Customer segmentation refers to the process of categorising customers into clusters that exhibit identical behaviour, enabling more effective product promotions and marketing strategies. The customer segmentation aims to investigate how to address customers in various forms. It helps companies recognise their valuable customers and fulfil their requirements by enhancing products and services and includes aspects such as demographic, geographic, psychographic, and behavioural. Recently, machine learning (ML) approaches have been used for customer and classification models that identify complex data patterns. This study presents an Advanced Machine Learning with Feature Selection for Robust Customer Segmentation with Purchase Behaviour Prediction (AMLFS-RCSPBP) approach. Initially, data pre-processing is performed to transform customer data into a usable format. Additionally, the information gain (IG) technique is used to select an effective set of features, and fuzzy-c-means (FCM) clustering is employed for segmentation. To estimate the optimum number of clusters, the Silhouette score is applied. Moreover, a multilayer perceptron (MLP) classifier is used to allocate the clusters to the unseen customers. Finally, the enhanced dung beetle optimisation (EDBO) technique is used to determine the optimal choice of MLP parameters. The comparison study of the AMLFS-RCSPBP method demonstrated a superior accuracy value of 94.75% over other models on the benchmark dataset.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月17日

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

摘要

Customer segmentation refers to the process of categorising customers into clusters that exhibit identical behaviour, enabling more effective product promotions and marketing strategies. The customer segmentation aims to investigate how to address customers in various forms. It helps companies recognise their valuable customers and fulfil their requirements by enhancing products and services and includes aspects such as demographic, geographic, psychographic, and behavioural. Recently, machine learning (ML) approaches have been used for customer and classification models that identify complex data patterns. This study presents an Advanced Machine Learning with Feature Selection for Robust Customer Segmentation with Purchase Behaviour Prediction (AMLFS-RCSPBP) approach. Initially, data pre-processing is performed to transform customer data into a usable format. Additionally, the information gain (IG) technique is used to select an effective set of features, and fuzzy-c-means (FCM) clustering is employed for segmentation. To estimate the optimum number of clusters, the Silhouette score is applied. Moreover, a multilayer perceptron (MLP) classifier is used to allocate the clusters to the unseen customers. Finally, the enhanced dung beetle optimisation (EDBO) technique is used to determine the optimal choice of MLP parameters. The comparison study of the AMLFS-RCSPBP method demonstrated a superior accuracy value of 94.75% over other models on the benchmark dataset.