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Rajiv Garg
Associate Professor
Research Interests
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Articles (7)
Year:
2024
Social media platforms like Facebook and Twitter have emerged as effective channels for advertising that enable consumer targeting based on demographics, interests, and user behavior. Social media marketers have utilized information spillover within these platforms to reach a larger customer base. This information spillover also exists across groups of users within the platform and enhances returns from social media advertising. Thus, this information spillover can be utilized to strategically sequence targeted advertising to amplify the returns from social media ads. In this paper, we present a theoretical model for information retention and show that the sequential advertising strategy is effective in targeting groups of users on a social media platform. In addition, we provide empirical evidence through two series of randomized field experiments. From experiments for a health services organization, we find that sequential advertising campaigns provide 23% more clicks when compared to campaigns that target groups simultaneously, which translates to a saving of 18.7% in the advertising budget to achieve similar results as simultaneous advertising. Additionally, we found that sequential advertising campaigns targeting a smaller group first followed by a larger group provide 10.7% additional clicks when compared to targeting a larger group first followed by a smaller group. These results were consistent for consumer packaged goods that were advertised on Facebook and Twitter. These results provide implications for social media advertising research and practice.
Year:
2023
Year:
2023
Year:
2022
Year:
2021
Year:
2020
Year:
2020
Collaborators (2)
Patrick Brockett
Gus S. Wortham Chair Professor of Risk Management and Insurance
University of Texas at Austin
Kristen Sussman
Assistant Professor of Advertising
Texas State University
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