Scholarlink

<p>In recent years, customer segmentation has become a crucial tool in the retail industry. Customer groups are segmented based on traits to create customized marketing campaigns. Customer segmentation approaches overlook complex hidden patterns in consumer datasets. Traditional RFM (Recency, Frequency, and Monetary) analyses segment customers but lack depth in defining customer dynamics. To resolve these issues, research in deep learning (DL) and machine learning (ML) is used to develop a customer segmentation and prediction process for the retail industry. This study develops an endwise predictive model for customer segmentation with purchase behavior Analysis using an Extended RFM with Fine-Tuned Convolutional DL (EXRFM-FTCDL) model. Segmentation and prediction are two stages of the EXRFM-FTCDL model. EXRFM features with behavioral measures (recency, frequency, money, repurchase time, average order value (AOV), and interpurchase time) are derived during segmentation, and a Gaussian Mixture Model (GMM) is applied to cluster. EXRFM-FTCDL model chooses the optimum number of clusters exploiting the Bayesian Information Criterion (BIC) and the Silhouette score. An improved artificial rabbit optimization (IARO) method using a hybrid 1-dimensional convolutional long short-term memory approach (Conv1D-LSTM) network has been used in the prediction stage of the EXRFM-FTCDL method. A retail benchmark dataset from a benchmark repository is employed for performance evaluation of the EXRFM-FTCDL technique. According to the results of the experiments on the public Online Retail dataset, the EXRFM-FTCDL methodology is effective at segmenting and classifying regular, occasional, and high-frequency customers. According to a comprehensive study comparing EXRFM-FTCDL methodology to other state-of-the-art models, it had better evaluation metrics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-driven customer segmentation model for online retail industry based on extended RFM model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

查看全文
  • R. Somasundara Manikandan,
  • Santhosh Kumar Sundar,
  • J. Jegathesh Amalraj,
  • Prabu Selvam,
  • Muhammad Faheem

摘要

In recent years, customer segmentation has become a crucial tool in the retail industry. Customer groups are segmented based on traits to create customized marketing campaigns. Customer segmentation approaches overlook complex hidden patterns in consumer datasets. Traditional RFM (Recency, Frequency, and Monetary) analyses segment customers but lack depth in defining customer dynamics. To resolve these issues, research in deep learning (DL) and machine learning (ML) is used to develop a customer segmentation and prediction process for the retail industry. This study develops an endwise predictive model for customer segmentation with purchase behavior Analysis using an Extended RFM with Fine-Tuned Convolutional DL (EXRFM-FTCDL) model. Segmentation and prediction are two stages of the EXRFM-FTCDL model. EXRFM features with behavioral measures (recency, frequency, money, repurchase time, average order value (AOV), and interpurchase time) are derived during segmentation, and a Gaussian Mixture Model (GMM) is applied to cluster. EXRFM-FTCDL model chooses the optimum number of clusters exploiting the Bayesian Information Criterion (BIC) and the Silhouette score. An improved artificial rabbit optimization (IARO) method using a hybrid 1-dimensional convolutional long short-term memory approach (Conv1D-LSTM) network has been used in the prediction stage of the EXRFM-FTCDL method. A retail benchmark dataset from a benchmark repository is employed for performance evaluation of the EXRFM-FTCDL technique. According to the results of the experiments on the public Online Retail dataset, the EXRFM-FTCDL methodology is effective at segmenting and classifying regular, occasional, and high-frequency customers. According to a comprehensive study comparing EXRFM-FTCDL methodology to other state-of-the-art models, it had better evaluation metrics.