Enhancing Influencer Marketing Strategies through Machine Learning : Predictive Analysis of Influencer-Generated Interactions

Sammanfattning: The field of influencer marketing has experienced rapid growth in recent years. However, uncovering the true effectiveness of this marketing approach remains a significant challenge. This thesis addresses the challenge of predicting the effectiveness of influencer marketing campaigns by employing advanced machine learning techniques, specifically the Auto Machine Learning framework Autogluon. With the aim of democratizing machine learning and empowering businesses in the influencer marketing domain, this work leverages Autogluon to predict the interactions generated by influencers when posting affiliate links. By evaluating various settings of AutoGluon and assessing the performance using metrics such as R-squared score, we observed promising results with good predictive accuracy. The findings from our study contribute to critical discussions in the field. This research offers a streamlined and efficient approach to machine learning, reducing the need for extensive manual model tuning and enabling marketers to make informed decisions and optimize their campaign strategies. The outcomes of this study have practical implications for businesses, allowing them to effectively predict campaign interactions and maximize the impact of influencer marketing initiatives. By leveraging the power of automated machine learning, this thesis opens up new opportunities for businesses to harness the potential of influencer marketing in driving successful marketing campaigns.