We investigate the effect of social media advertising content on customer engagement using a large-scale field study on Facebook. We content-code more than , unique messages across companies using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing algorithms. We use this large-scale dataset of content attributes to test the effect of social media marketing content on subsequent user engagement defined as Likes, comments, shares, and click-throughs with the messages. We find that directly 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 brand-personality related attributes. Our results suggest therefore that there may be substantial gains from content engineering by combining informative characteristics associated with immediate leads via improved click-throughs with brand personality related content that help maintain future reach and branding on the social media site via improved 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.
Top Search Marketing and Social Media Research Projects of 2009
Social Media Marketing Research Paper
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Social Media Marketing Research Paper Topics & Free Essay Examples
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