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

By Dokyun Lee, Kartik Hosanagar, Harikesh S. Nair. Management Sciences, 2018

902 citations recorded by the source corpus.

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 conversio...

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/49ae2abdba289d0823cf3440b4320a76e9a580c0

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Citations in the corpus

Natural language processing (NLP) in management research: A literature review (2020, methodology): “…the accuracy of learning methods, the ensemble learning model—which includes logistic regression, Naive Bayes, and support vector machines—is frequently applied to identify large numbers of text features, such as brand mention, product mention, readability index, and more (D. Lee et al., 2018).”
Engaged to a Robot? The Role of AI in Service (2020, background): “Anthropomorphized consumer robots make consumers feeling warm (Kim, Schmitt, and Thalmann 2019), and natural language–based social robots engage customers (Lee, Hosanagar, and Nair 2018).”
Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities (2019, result): “Third, in line with previous research on online brand communities (e.g., Lee, Hosanagar, and Nair 2018; Relling et al. 2016), the count of likes and comments indicates the degree to which others approve and share a message and provides an objective measure of virality.”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, background): “…applied NLP to marketing problems (Archak, Ghose, and Ipeirotis 2011; Decker and Trusov 2010; Eliashberg, Hui, and Zhang 2007; Lee and Bradlow 2011; Lee, Hosanagar, and Nair 2018; Lee, Manzoor, and Cheng 2019; Liu, Singh, and Srinivasan 2016; Nam, Joshi, and Kannan 2017; Netzer et al.…”
The Role of Social Media Content Format and Platform in Users’ Engagement Behavior (2021, background): “While much of the literature identifies individual-related factors as antecedents of customer engagement, few studies (e.g., Cvijikj & Michahelles, 2013; Dolan, Conduit, et al., 2019; Lee et al., 2018) have studied the impact of social media content strategy on engagement behavior).”
A strategic framework for artificial intelligence in marketing (2020, methodology): “At the feeling level, more real-time and accurate emotion sensing from posted messages can better engage customers and provide a better interaction experience (Hartmann et al. 2019; Lee et al. 2018).”
Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, background): “Chen and Lee (2018) investigated the use of Snapchat for social media marketing while targeting young consumers.”
Certainty in Language Increases Consumer Engagement on Social Media (2021, background): “However, despite the importance of engaging consumers with brand content, research is only just beginning to identify the characteristics of brand messages that drive consumer engagement (Cruz, Leonhardt, & Pezzuti, 2017; Lee et al., 2018; Ordenes et al., 2018).”
How stories generate consumer engagement: An exploratory study (2019, background): “To understand how CE generates benefits for brands, recent focus has been placed on how specific brand contents work to create engagement (Kim et al., 2016; Schultz, 2017; Lee et al., 2018; Hollebeek & Macky, 2019).”
Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages (2019, background): “Every brand message in our data set had been shared for at least seven days, reflecting the period when most consumer engagement with a social media message happens (Lee, Hosanagar, and Nair 2017).”