Marketing Papers

Document Type

Technical Report

Date of this Version

1-2018

Publication Source

Management Science

DOI

10.1287/mnsc.2017.2902

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.

Copyright/Permission Statement

Originally published in Management Science © 2018 INFORMS

This is a pre-publication version of an Article in Advance. The final version is available at http://dx.doi.org/10.1287/mnsc.2017.2902

Keywords

consumer engagement, social media, advertising content, content engineering, marketing communication, machine learning, natural language processing, selection, Facebook, EdgeRank, News Feed algorithm

Share

COinS
 

Date Posted: 15 June 2018

This document has been peer reviewed.