Optimizing the Timing of Social Media Posts with Historical Data

Modern companies regularly use social media to communicate with their customers. In addition to the content, the popularity of a social media post may depend on the season, the day of the week, and the time of the day. We show how the timing of a post can be optimized based on historical data on previous posts and their popularity. We promote a prescriptive approach based on recent advances in causal inference and consider optimizing the timing of Facebook posts by a large Finnish consumers’ cooperative using historical data. We express the understanding of the causal relations in the form of a directed acyclic graph, use a state-of-the-art identification algorithm to obtain a formula for the causal effect, and finally estimate the required conditional probabilities with Bayesian generalized additive models. As a result, we obtain estimates for the expected popularity of a post for different counterfactual choices of timing