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Abstract
Cloud-based sales systems increasingly rely on predictive models to optimize customer relationship management, sales forecasting, and automated outreach. However, traditional predictive modeling often overlooks fine-grained user interaction data—such as browsing behavior, session duration, and clickstream patterns—which can offer critical insights into user intent and conversion likelihood. This paper explores how integrating user interaction patterns can improve the performance of predictive modeling in cloud-based sales platforms. We analyze log data from a large-scale cloud CRM system, identify key behavioral features, and assess their impact using various machine learning algorithms. Results show a significant improvement in prediction accuracy and business KPIs such as lead conversion and customer retention.
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