Förvärv av nästa genombrott : Hur man identifierar och värderar europeiska YouTube-kanaler för barn före förvärv

Sammanfattning

This study examines how European YouTube channels for children can be identified and valued prior to acquisition through the analysis of expected trend growth. The study is motivated by the increasing importance of digital intangible assets and the need for alternative valuation methods for early-stage digital content assets that lack stable financial data. Traditional valuation models are considered insufficient in this context, which motivates a data-driven approach. The study applies a quantitative method based on machine learning and uses data collected from YouTube API v3 and Social Blade. In total, 3934 YouTube channels were analysed using different XGBoost-based models, including point regression, quantile regression, and binary classification. The models were applied to estimate future growth in views six months ahead, analyse uncertainty, and identify the probability of significant growth or decline. The results indicate that engagement-based parameters and time-based trend metrics can be used to identify channels with high growth potential. Furthermore, the analysis demonstrates that machine learning can function as a complement to traditional valuation methods by capturing complex and non-linear relationships in platform data. The study thereby contributes a practical framework for how investors and media companies can systematically identify and evaluate YouTube channels within the children’s segment prior to acquisition.