Engagement in Motion: Exploring Short Term Dynamics in Page-Level Social Media Metrics

Using page-level metrics of a randomly selected group of 15,625 among the top 100,000 Face book check-in locations which rank high in terms of customer engagement, we explore if the short-term dynamical information on these metrics could deliver, via a clustering approach, some new insights for marketing decision making. Using a highly-scalable clustering algorithm, statistical methods, and combinatorial optimization met heuristics based on memetic algorithms, we have observed that some pages naturally cluster with others that share the same user-defined category. Our results highlight the need of suggesting other further "meta-categories" that encompass several user-reported categories for pages and that a priori geographical segmentation might be necessary to investigate more relevant patterns that take into account seasonal variability of behaviours and physical proximity.