Influencers ranking and engagement analysis

The proliferation of social media has made influencer marketing a cornerstone of digital advertising strategies. However, identifying the right influencers based on quantitative engagement metrics, rather than just follower count, remains a significant challenge for brands and marketers. This project addresses this gap by developing a comprehensive Influencer Ranking and Engagement Analysis Platform built on the MERN (MongoDB, Express.js, React.js, Node.js) stack. The system is designed to automate the end-to-end process of influencer discovery and evaluation. It begins by aggregating real-time data from Instagram via the Apify API, scraping post information for specific hashtags. This "Fresh Post Data" is then processed to calculate key performance indicators such as likes, comments, and engagement rates. A core functionality of the platform is its algorithm that calculates a proprietary Engagement Score for each content creator, which serves as the primary metric for objectively ranking influencers. The processed and ranked data is presented through an intuitive User Dashboard, which features dynamic data visualizations for at-a-glance insights. Users can apply advanced filters to narrow down influencers based on specific criteria and export the refined data for reporting purposes. The platform effectively bridges the gap between raw social media data and actionable business intelligence, providing marketers with a data-driven, efficient, and scalable solution to optimize their influencer marketing campaigns, maximize Return on Investment (ROI), and streamline their collaboration efforts.

Keywords: MERN Stack, Influencer Marketing.