What are the systematic methods for improving the efficiency and effectiveness of advertising targeting based on big data?

Jisu Huh

For the past 25 years or so, Professor Jisu Huh’s professional career has been devoted to advertising practice, research, education, and service, across the advertising industry and academia. Her research program has been centered on scientifically examining and better understanding advertising’s potentially positive and negative effects on consumers and its functional and dysfunctional roles in the society. 

More recently, Huh’s research has focused on applying the computational research approach and trying to find systematic methods for improving the efficiency and effectiveness of advertising targeting based on big data. One line of her ongoing research, which consists of multiple research projects, is about Trust and Its Influence on Advertising Processes and Effects: Computational Research Applying the Trust Scores in Social Media (TSM) Algorithm. 

“In today’s interactive communication network environment, advertisers have little control over where their ad messages go and what consumers will do (or not do) with and to the ads,” she said. “My research examines the role of consumer-to-consumer trust in advertising message diffusion and effects in the ever-evolving interactive communication network environment, using a variety of methodological approaches.” 

Huh’s latest project applies a computational research approach to examine the impact of consumer-to-consumer trust in a social network on the extent and speed of viral advertising diffusion, using real-life viral ad examples representing different product types, brands, and content characteristics. Specifically, this project uses the Trust Scores in Social Media (TSM) algorithm, which was developed through collaboration between computer science scholars. 

Another line of research Huh is exploring is the possibility of granular-level targeting of TV and digital video audience based on their mood. To tackle this, Huh, along with her doctoral advisee, is working with computer science researchers to empirically test the relationship between TV content-induced affective states of TV viewers and their attention to and evaluation of ads.