The Impact of Machine Learning Models on Product Strategy and Lifecycle Management
Keywords:
machine learning, product strategy, lifecycle management, predictive analytics, innovation, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
In today’s competitive landscape, organizations increasingly rely on machine learning (ML) models to inform product strategy and manage the lifecycle of products. This manuscript investigates how ML models can optimize decision-making processes throughout a product’s lifecycle—from ideation and development to market exit. Through an extensive literature review, quantitative analysis, and a discussion of emerging trends, we highlight the transformative potential of ML in predicting market trends, optimizing resource allocation, and enhancing product performance. Statistical analysis based on performance metrics from various ML algorithms reinforces the role of data-driven insights in product lifecycle management. Our findings suggest that integrating ML into product strategy can lead to more agile, efficient, and successful product outcomes.
