Predicting social media performance metrics and evaluation of the impact on brand building: a data mining approach

Abstract

This study presents a research approach using data mining for predicting the performance metrics of posts published in brands' Facebook pages. Twelve posts' performance metrics extracted from a cosmetic company's page including 790 publications were modeled, with the two best results achieving a mean absolute percentage error of around 27%. One of them, the "Lifetime Post Consumers" model, was assessed using sensitivity analysis to understand how each of the seven input features influenced it (category, page total likes, type, month, hour, weekday, paid). The type of content was considered the most relevant feature for the model, with a relevance of 36%. A status post captures around twice the attention of the remaining three types (link, photo, video). We have drawn a decision process flow from the "Lifetime Post Consumers" model, which by complementing the sensitivity analysis information may be used to support manager's decisions on whether to publish a post.