A novel influence quantification model on Instagram using data science approach for targeted business advertising and better digital marketing outcomes

Instagram is one of the most popular and widely used social network platforms. It is used as a digital tool to connect with other users and also to share information and influence them for marketing and advertising purposes. The influence of popular users is broadly determined by post’s engagement rate in terms of likes, comments, and shares, and the number of followers as well. An objective and comprehensive measure of popularity is necessary to understand the factors that will help make an influencer marketing campaign more successful and beneficial for business activities. This research work attempts to take various features of an influencer account and Instagram posts dataset and develop a novel model that accurately quantifies and determines the influence of a user on Instagram. The research is based on datasets of top regional Instagram influencers and their posts based on categories signified through hashtags and captions. Our research attempts to develop a model using principal component analysis to quantify influence and using it to rank influencers. In our experiment, the proposed model after experimentation, gave the Instagram username “iqbaal.e” influence score as 874,712.9526, username “1nctdream” as 753,830.5847 and username “weareone. exo” as 668,054.4360. The proposed model ranks were compared with other ranks for Instagram users based on other measures such as follower rank etc. User names “huyitian”, “bintangemon” and “bimopd” are top social media influencers based on the proposed model for better business advertising and digital marketing outcomes with collected data and experiment context. This proposed approach gives an exploration for the stakeholders to quantify the impact of influencer in social media and demonstrate an innovative approach.