Data-driven influencer marketing strategy analysis and prediction based on social media and Google Analytics data
Abstract
Due to various uncertainties on social media, data-driven strategy has become a necessity for influencer marketing. Typically, a promotional post by an influencer aims to direct the viewers to buy a product from a brand's website. The objective of this paper is to analyse the factors that contribute to the popularity of promotional posts in terms of likes and website visits count. This research utilised Facebook (FB), Instagram (IG) and Google Analytics (GA) data collected from the ambassadors (or influencers) of MeCan App, a Malaysian e-commerce company. The factors that contribute to popularity have been successfully identified, such as the optimal posting time, hashtags, image type, interval and ratio of posts. For example, they should post based on the ratio of one regular post for 5.4 promotional posts for the best exposure. Additionally, regression methods were implemented to predict website visit count, with an accuracy of 69.9 per cent using Random Forest Regressor.
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Author's Biography
Kristo Radion Purba is an assistant professor of computer science at the University of Southampton Malaysia. He is a data scientist, focusing on social media, machine learning and artificial intelligence. He has published in many high impact journals, especially on Instagram analytics and machine learning predictions. He was a data scientist at MeCan App, Sdn Bhd, a Malaysian e-commerce company, working on delivering analytical solutions to improve sales and engagement. He is certified in advanced data science by IBM.
Yee Jia Tan received a B.Bus degree in information technology from La Trobe University, Australia, and an MBA in project management from Management & Science University, Malaysia. Currently, she is the data analyst at the Department of Ambassador Development, MeCan App Sdn Bhd. MeCan is a social commerce platform where ambassadors are playing the role of social media posting and promotion. Yee Jia's main role is to improve ambassadors' overall performance.
Citation
Purba, Kristo Radion and Tan, Yee Jia (2023, January 1). Data-driven influencer marketing strategy analysis and prediction based on social media and Google Analytics data. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 8, Issue 3. https://doi.org/10.69554/NLPQ6097.
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