Social Media Data Analytics – Using Big Data for Big Consumer Reach

Abstract

Since the arrival of ‘Big Data’ and social media’s meteoric rise in popularity, businesses have been forced to review, reinvent, and reallocate their marketing strategies. Having a social media presence is a requirement for most any firm that has goods or services to sell to consumers. In today’s environment of highly charged verbal-volatility, companies are not only scrambling to adjust to the YouTube generation of advertising, but also monitoring and protecting their corporate image. While branding is outside the scope of the paper, we do touch on artificial intelligence and how machine learning is employed to monitor user sentiment on social media platforms. The monitored data segments evaluated in this research paper are frequency, education level, gender, age, geographic location, and personal interests. In addition to data monitoring, the paper also includes a brief discussion of the online marketing environment. The ethos of this document is to demonstrate how small and mid-size businesses can best allocate their advertising budgets to maximize exposure, and ultimately conversions on the most popular social media platforms. By tracking conversions and impressions, we present a scenario of social media marketing optimization that demonstrates how Excel’s Solver add-in can be used for advertising allocations with the goal of highest potential sales.