Predictive Analytics and Algorithmic Framework for Social Media Influencer Engagement and Conversion

Abstract

In the current study, a predictive analytics and algorithm approach is presented, in effort to improve the efficiency of influencer marketing on social media, in the context of the Yamaha Music Waves Montage 8 Synthesizer campaign. The framework utilizes the audience segments that demonstrated the best promise of conversion based on their demographic and behavioral characteristics, leveraging supervision machine learning classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), to help identify valuable audience for promotion. The results indicate that the optimized KNN and SVM models achieve an impressive accuracy (about 93 percent) and high precision and recall metrics in the prediction of purchasing behavior. The analysis also uncovers notable gender differences in purchasing conversion rates, which can help inform marketers marketing and advertising strategy with, and to improve conversion efforts. As the study showed predictive analytics are a practical and concrete method for increasing influencer and their engagement, beyond the application with this focus groups. Through the use of predictive analytics data-driven marketing has a robust tool for decision making, which would be helpful in smart algorithmic practices to optimize influencer marketing decisions.