An Innovative Business Intelligence Modelling Approach on The Effects of Social Media Features on User Engagement

The intensity of today's social media landscape is unparalleled, with platforms that have evolved beyond basic messaging to include features such as story sharing, video content, shopping, and more. These are all in a bid to enhance user engagement. Although the effect of social media features on user engagement has been studied before, most of these studies have relied on in-person interviews, which can change the natural setting and unintentionally introduce response biases. This research adopted a different approach by utilizing observational data to analyze whether specific features influence user engagement. Data were from SEMrush and covered a time series of 2×60 points of user engagement metrics from two popular social media platforms in Indonesia: Instagram and TikTok. We retrospectively searched for credible references to determine when each feature was first introduced on these platforms. We used monthly fixed-effects regressions with robust standard errors to model the relationship between social media features and two metrics of user engagement: average visit duration and bounce rate. Our findings reveal diverse effects of features on user engagement. For instance, introducing a shopping feature increased the average visit duration of Instagram users, but conversely, a similar feature degraded the user engagement of TikTok users. Such intriguing patterns are discussed further in the paper.