Data-driven influencer marketing strategy analysis and prediction based on social media and Google Analytics data

Kristo Radion Purba and Yee Jia Tan
Additional contact information
Kristo Radion Purba: Assistant Professor, Department of Computer Science, University of Southampton Malaysia, Malaysia
Yee Jia Tan: Data Analyst, Department of Ambassador Development, MeCan App Sdn. Bhd., Malaysia

Applied Marketing Analytics: The Peer-Reviewed Journal, 2023, vol. 8, issue 3, 314-328

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.

Keywords: data analytics; machine learning; social media; e-commerce; marketing strategy (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hstalks.com/article/7494/download/ (application/pdf)
https://hstalks.com/article/7494/ (text/html)
Requires a paid subscription for full access.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2023:v:8:i:3:p:314-328

Access Statistics for this article

More articles in Applied Marketing Analytics: The Peer-Reviewed Journal from Henry Stewart Publications
Bibliographic data for series maintained by Henry Stewart Talks ().