Sequencing Advertising Campaigns in Social Media

26 Pages Posted: 5 Aug 2017  

Parshuram Hotkar

University of Texas at Austin, Red McCombs School of Business

Rajiv Garg

University of Texas at Austin - Department of Information, Risk and Operations Management

Date Written: July 1, 2017

Abstract

Social media platforms like Twitter and Facebook have emerged as new channels for product advertising that enable targeting based on demographics, interests, and user behavior. Spillover through word of mouth (WOM) and peer interactions plays a crucial role in social media marketing. Since individuals are part of multiple networks (based on their diversified interests) on social media, strategically sequencing ad campaigns across asymmetric networks can potentially increase sales. In this paper, we present a theoretical model for an individual's information retention and then apply the retention model to study the impact of sequencing social ads. We test the theory with field experiments on two dominant social media platforms (Facebook and Twitter) to investigate the effectiveness of an advertisement with sequential targeting - we target users in a small network followed by users in a large network, and vice versa. We find that advertising on small networks provides better returns on Twitter, whereas ads on large network perform better on Facebook. Interestingly, we find that sequentially advertising initially to a small network followed by large network performs better on both platforms.

Keywords: Social Media Advertising, Social Media Marketing, Sequencing, Information Spillover, Information Retention

JEL Classification: M00, M30, M31, M37

Suggested Citation

Hotkar, Parshuram and Garg, Rajiv, Sequencing Advertising Campaigns in Social Media (July 1, 2017). Available at SSRN: https://ssrn.com/abstract=3007806

Parshuram Hotkar

University of Texas at Austin, Red McCombs School of Business ( email )

CBA 5.202
Austin, TX 78712
United States
512-888-1994 (Phone)

Rajiv Garg (Contact Author)

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

HOME PAGE: http://www.RajivGarg.org

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