Using Analytics on Social Media Posts to Gauge the Impact on User Engagement: A Case Study Analysis on #FoundItOnAmazon

<p>The purpose of this research is to use analytics on social media content to study consumer engagement. Companies and brands may benefit indirectly when trends on social media take on positive hashtags. The hashtags provide exposure to the brand as they are shared and, in some cases, generate engagement as consumers join the trend and/or respond to posts relating to the trend. Certain factors may improve engagement, which in effect extends the trend and leads to more brand exposure. These may include # of followers, time of posting, and post format. While our data do not speak directly to sponsoring social influencers, it can be informational in terms of guiding companies to work with such influencers or content creators to increase or extend engagement. More specifically, we analyze the effect of creator, content, and context-related factors affecting user engagement. The dataset has a total of 1,000 social media posts and 273,586 likes and 6,306 likes and comments on Instagram, YouTube, and Twitter. Of the 1,000 posts, we ran a regression on 295 Instagram posts. The results reveal that the # of followers dominated the "overall engagement". Hence, a second regression using "user engagement rate" as the dependent variable was run. The rationale behind using user engagement rate in the second regression is to understand the relative impact of other factors and eliminate the dominance of followers from the equation. The results for the final regression revealed that posted format and time of posting have a significant impact on user engagement rate.</p>