OPTIMIZING PRODUCT DEVELOPMENT THROUGH DATA-DRIVEN BACKLOG PRIORITIZATION: A CROSS-FUNCTIONAL APPROACH

This article introduces a novel algorithmic approach to product backlog prioritization that addresses the critical challenge of balancing diverse stakeholder needs in software development. The article presents a comprehensive framework for synthesizing enhancement requests from Customer Success, Support, and Sales teams to generate data-driven prioritization decisions. The article demonstrates significant improvements in development efficiency and business outcomes by implementing an automated system that considers customer feedback patterns, support ticket frequencies, and sales pipeline obstacles. The article achieved an 85% accuracy rate in predicting high-impact feature requirements while reducing prioritization processing time by 83.5%. Key performance indicators showed substantial improvements, including a 64% increase in feature adoption rates, a 28% reduction in sales cycle length, and a 47% decrease in feature-related support tickets. The implementation also enhanced cross-functional alignment, resulting in an 89% alignment between Product and Sales priorities and a 73% reduction in conflicting feature requests. This article contributes to the theoretical understanding and practical application of product development optimization, providing organizations with a scalable framework for making more informed and efficient prioritization decisions in modern software development environments.