Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Social Science Research Network
We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement-defined as Likes, comments, shares, and click-throughs-with the messages. We find that inclusion of widely used content
... widely used content related to brand personality-like humor and emotion-is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content-like mentions of price and deals-is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality-related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers' path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook's EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook's behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality-related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews. Abstract 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 advertising attributes to test the effect of ad 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 posts 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 advertising design 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.