Automated Derivation of Marketing Analytical Insights from Social Media Information

Abstract

In contemporary digital marketing, effective audience segmentation is crucial at both aggregate and individual user levels. Personas, representing fictional user profiles, serve to humanize data, offering distinct identities to audience segments. Marketers utilize personas to gain deeper insights into their customers and make efficient marketing decisions for each segment. Developed within the realm of big data analytics and artificial intelligence, this article proposes a solution that not only demonstrates the viability of automatic persona generation from social media data but also illustrates the design and implementation of a highly scalable, expandable, and accurate system for this purpose. The proposed solution comprises various components, each assigned specific tasks, including data collection, enrichment, clustering, and persona generation. Multiple classifiers are employed to extract insights from the data, subsequently utilized to create customer segments. The integration of these components into a stream-processing architecture mitigates bottlenecks, ensuring a high level of separation of concerns. The final outcome reveals user segmentation with exceptional accuracy, precision, and recall measures, all exceeding 0.90%.