Estimating the causal effect of timing on the reach of social media posts

By Lauri Valkonen, Jouni Helske and Juha Karvanen in Causal Inference Bayesian Inference

October 22, 2022



Modern companies regularly use social media to communicate with their customers. In addition to the content, the reach of a social media post may depend on the season, the day of the week, and the time of the day. We consider optimizing the timing of Facebook posts by a large Finnish consumers’ cooperative using historical data on previous posts and their reach. The content and the timing of the posts reflect the marketing strategy of the cooperative. These choices affect the reach of a post via a dynamic process where the reactions of users make the post more visible to others. We describe the causal relations of the social media publishing in the form of a directed acyclic graph, use an 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 reach of a post for alternative timings.

Posted on:
October 22, 2022
1 minute read, 160 words
Causal Inference Bayesian Inference
See Also:
Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach