MVP strategy as a tool for business model validation in the early stages of a startup lifecycle

The studyʼs relevance is determined by the need to enhance the effectiveness of business model validation for startups in the early stages of their lifecycle by implementing an MVP as a tool for risk reduction and optimizing project scaling processes. Under economic instability and limited resources, there is a growing demand for a comprehensive approach to market hypothesis testing focusing on cost minimization. This article aims to identify strategic solutions for utilizing MVP as a business model validation tool, facilitating project adaptation to market conditions, and increasing scalability potential. Methods. The study employs system analysis to assess key MVP components in the business model validation process, structural-functional analysis to identify the interrelationships between MVP elements and their role in hypothesis testing, and forecasting methods to formulate recommendations for MVP optimization during scaling. Results. The primary methods of integrating MVPs into startup processes have been explored, emphasizing their role in mitigating risks and confirming the project's investment appeal. A systematic approach to MVP formation was found to promote timely hypothesis testing, collection of feedback, and adjustment of the business model in response to market changes. The scientific novelty lies in the proposed enhancement of the MVP integration approach as a strategic tool for evaluating the viability of a startup's business model by developing a comprehensive system for assessing financial, technological, and marketing indicators, thereby reducing risks at the scaling stage. Conclusions. It has been demonstrated that implementing MVP enables rapid testing of key market hypotheses with minimal financial costs, reduces the risk of resource loss during scaling, and facilitates the optimization of the business model before market entry. An integrative MVP assessment model focusing on a comprehensive evaluation of financial and technological indicators is proposed, along with the implementation of machine learning algorithms for feedback analysis and forecasting target audience behavior changes