The reality: Online content aggregators have so much video content. Challenge: so little consumer attention. The problem: how to create and recommend samples of video content to entice viewers. Proposed Solution: find the most ‘emotionally interesting clips’ in the content and show them to the viewer in a logical sequence. Twist: do this automatically using Big Data + Bayesian AI.
Access Classroom Lecture Slides
Liu, Xuan, Savannah Wei Shi, Thales Teixeira, and Michel Wedel (2018), “Video Content Marketing: The Making of Clips,” Journal of Marketing, 82 (4), 86-101.
Consumers have an increasingly wide variety of options available to entertain themselves. This poses a challenge for content aggregators who want to effectively promote their video content online through original trailers of movies, sitcoms, and video games. Marketers are now trying to produce much shorter video clips to promote their content on a variety of digital channels. This research is the first to propose an approach to produce such clips and to study their effectiveness, focusing on comedy movies as an application. Web-based facial-expression tracking is used to study viewers’ real-time emotional responses when watching comedy movie trailers online. These data are used to predict both viewers’ intentions to watch the movie and the movie’s box office success. The authors then propose an optimization procedure for cutting scenes from trailers to produce clips and test it in an online experiment and in a field experiment. The results provide evidence that the production of short clips using the proposed methodology can be an effective tool to market movies and other online content.
Special thanks to Kelley Gullo, Ph.D. candidates at Duke University, for their support in working with authors on submissions to this program.
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