The Effect of Social Media Marketing Content on Consumer Engagement: Evidence from Facebook∗

We investigate the effect of social media content on customer engagement using a large-scale field study on Facebook. We content-code more than 100,000 unique messages across 800 companies engaging with users on Facebook using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing algorithms. We use this large-scale database of content attributes to test the effect of social media marketing content on subsequent user engagement − defined as Likes and comments − with the messages. We develop methods to account for potential selection biases that arise from Facebook’s filtering algorithm, EdgeRank, that assigns messages non-randomly to users. We find that inclusion of persuasive content − like emotional and philanthropic content − increases engagement with a message. We find that informative content − like mentions of prices, availability, and product features − reduce engagement when included in messages in isolation, but increase engagement when provided in combination with persuasive attributes. Persuasive content thus seems to be the key to effective engagement. Our results inform content design strategies in social media, and the methodology we develop to content-code large-scale textual data provides a framework for future studies on unstructured natural language data such as advertising content or product reviews.