Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Carnegie Mellon University - David A. Tepper School of Business
University of Pennsylvania - Operations & Information Management Department
Stanford University - Graduate School of Business
February 13, 2017
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 dataset 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 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 non-random targeting of Facebook's EdgeRank (News Feed) algorithm, so reflect more closely user reaction to content, rather than Facebook's behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics associated with immediate leads (via improved click-throughs) with brand-personality related content that helps maintain 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.
Number of Pages in PDF File: 58
Keywords: consumer engagement, social media, advertising content, content engineering, marketing communication, large-scale data, natural language processing, machine learning, selection, Facebook, EdgeRank, content engineering.
JEL Classification: M3
Date posted: September 26, 2013 ; Last revised: February 14, 2017