TACS-Net: Temporal-Aware Customer Segmentation Network

Abstract

Customer segmentation is essential for personalized marketing, customer retention, and strategic decision-making. Traditional segmentation methods, such as k-Means and Gaussian Mixture Models, rely on static features and fail to capture the evolving nature of customer behavior. Existing methods also struggle to account for temporal dynamics, limiting their effectiveness in fast-changing markets. This article proposes TACS-Net, a Temporal-Aware Customer Segmentation Network that dynamically models behavioral shifts using Temporal Convolutional Networks (TCN), Transformers, and a Recurrent Clustering Algorithm (RCA). TACS-Net adapts to changes in customer purchasing patterns over time, offering superior segmentation accuracy and stability. It integrates short- and long-term behavioral modeling, providing a robust, real-time framework for continuous customer profiling. Evaluation on two real-world datasets (CSD1 and CSD2) demonstrates that TACS-Net achieves a silhouette score of 0.55 on CSD1 and 0.54 on CSD2, outperforming traditional baselines. The model also shows higher temporal stability, with 84.3% and 83.7% of customers retaining their segment over one month in CSD1 and CSD2, respectively, compared to 72.1% and 74.0% with k-Means. Explainability analysis using SHAP reveals key factors driving segmentation, such as spending score, purchase frequency, and last purchase amount. While TACS-Net outperforms existing methods in clustering quality and stability, its higher computational cost calls for further optimization.