Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Dokyun Lee (), Kartik Hosanagar () and Harikesh Nair
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Dokyun Lee: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Kartik Hosanagar: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Management Science, 2018, vol. 64, issue 11, 5105-5131

Abstract: 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 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.

Keywords: consumer engagement; social media; advertising content; content engineering; marketing communication; machine learning; natural language processing; selection; Facebook; EdgeRank; News Feed algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (136)

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