The aim of this study was to determine how tools of high-speed experimentation and data analysis can strengthen the innovative development of start-ups and increase the effectiveness of aligning marketing and product decisions. The methodology was based on a theoretical-empirical approach and included the systematisation of academic sources, the analysis of digital platforms, the comparison of technology companies and the study of practical cases that reflected the use of experimentation, recommendation models and optimisation of user experience. The results established that models with an emphasis on the product as the main growth tool ensured higher financial performance: companies that applied a product-led growth approach demonstrated an average annual increase in Annual Recurring Revenue of 35%, whereas companies with traditional approaches achieved only 26%, and customer acquisition costs in product-oriented businesses were 39% lower. The significance of experimentation cycles was confirmed by the high intensity of tests: the company Nebula conducted 612 experiments in 2024, which helped to increase conversions on average by 4-7%. Optimisation of user experience, researched on the example of Duolingo, provided an increase in user retention of 15-25%, while speeding up the process of first use of the product increased user activation by 10-30%. The most powerful viral mechanism proved to be recommendation models: the example of Dropbox demonstrated growth of 3,900%, and the number of users increased from 100 thousand to more than 4 million, which confirmed the effectiveness of viral dissemination cycles. The practical significance of this study lies in the fact that its results can be used by start-ups to build effective growth mechanisms, optimise product-marketing decisions and increase the effectiveness of experimentation
Iryna Smuk (Tue,) studied this question.