Social Network Influencers’ Engagement Rate Algorithm Using Instagram Data

The Social Influencer Database System is a project that has been implemented with the purpose of allowing brand owners and influencers to interact with each other. Influencer in this context refers to a social media user who is popular and has a high amount of followers. Through social media, it is now possible for a larger number of people to communicate with one another about products as well as the companies that are involved in their production. The problem that has been existing in social media marketing now is that it is difficult to determine the efficiency of influencers on how well they are able to promote the product. Having large amount of followers does not guarantee the influencer is efficient in promoting the product. Thus, there is a need for metrics to measure the efficiency of influencers. The Social Influencer Database System project consists of three main parts, namely a web scraping algorithm, a database, and a web application. The purpose of the algorithm is to determine the efficiency of the influencers while the web application provides a platform for brand owners and influencers to interact with each other. This study has been done with the aim of gaining a wider range of knowledge of how to determine the efficiency of influencers. Research has been done about related case studies and existing algorithms to get a better understanding of how to use available metrics such as likes, comments and followers, to calculate the engagement rate of influencers in the most accurate way. Brand owners can then select the most efficient influencer to advertise their product. The algorithm formulated in this study has been applied to Instagram data. The use of an influencer can greatly enhance the outcome of digital marketing. Hence, a proper measurement of efficiency must be done in order to produce the best outcome.