Real-Time E-Commerce Customer SegmentationUsing Hybrid Clustering and Deep Learning Techniques
Abstract
Customer segmentation plays a crucial role in ecommerce personalization, marketing optimization, and customer retention. Traditional segmentation techniques, such as K-Means clustering and RFM modeling, often fail to handle real-time data, dynamic customer behavior, and scalability challenges. Moreover, existing methods lack predictive capabilities, limiting their ability to anticipate future customer actions.
This research proposes a real-time AI-driven customer segmentation framework that integrates hybrid clustering (KMeans, DBSCAN, and GMM) with predictive analytics (XGBoost, LSTM) to enhance segmentation accuracy. Apache Kafka and Spark Streaming enable real-time customer segmentation, while Google BigQuery and Dask ensure scalability for largescale datasets. The framework dynamically selects the best clustering algorithm using Silhouette Score and Davies-Bouldin Index, addressing the limitations of singlemodel approaches. Additionally, machine learning models predict customer lifetime value, future purchases, and retention probabilities, providing actionable insights for personalized marketing strategies.
Experimental results demonstrate that the proposed hybrid clustering approach outperforms traditional methods, reducing segmentation errors by 40%, improving predictive accuracy by 15%, and enabling real-time customer insights. The findings indicate that integrating streaming analytics, hybrid clustering, and AI-driven predictive modeling can significantly enhance customer segmentation strategies for modern e-commerce businesses.
References
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