Data clustering is a crucial technique utilized in the study of customer behavior data, facilitating the optimization of business strategies for organizations. This study assesses the efficacy of traditional clustering algorithms (K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Model) in comparison to deep clustering techniques (DNN-Based and DEC) utilizing three datasets of customer transaction behaviors. The algorithms are assessed using cluster quality criteria, including Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, and Execution Time. The findings indicate that deep clustering techniques excel on extensive and intricate datasets, producing more distinct and stable clusters. Silhouette Analysis further reveals uniform positive distributions for DNN and DEC, contrasting with negative values in traditional methods due to noise or misassignments. Nevertheless, they necessitate substantial computational expenses, with execution durations markedly exceeding those of traditional approaches. K-Means and DBSCAN algorithms are appropriate for applications necessitating speed or uncomplicated data, but deep learning methods are advisable for scenarios with ample computational resources. The study emphasizes the balance between quality and computational efficiency, while suggesting future research avenues aimed at optimizing the computational expense of deep clustering and investigating sophisticated deep learning architectures, such as variational autoencoders, to improve performance across various datasets.