DEEPSENSE: A Deep-Learning Predictive Tool for Evaluating Effectiveness of Video Commercials

Joseph Johnson, Gang Ren, Hyunhwan “Aiden” Lee, and Mitsunori Ogihara, “Deepsense: A Deep-Learning Predictive Tool for Evaluating Effectiveness of Video Commercials,”

  • accepted for Theory + Practice in Marketing Conference 2019
  • preparing for initial submission to Journal of Marketing.

Testing and validation of television commercials require sophisticated audience response collection schemes at various production and deployment stages of a commercial’s lifecycle including script writing, actor casting and footage shooting. Audience response at these stages allows interactive adjustment to the production and deployment processes which increases the effectiveness of the television commercials. Current testing advertising approaches include manual annotation, focus group studies and audience/customer opinion surveys. Besides their high operational cost, these approaches for collecting audience response data are usually time-consuming and lack immediacy.

We propose a computational “media cognition” tool that uses deep neural networks on video, audio and natural language content of commercials as training data and the audience response attributes as the training labels to form a predictive analysis framework for video commercials. Our data source is YouTube video commercials. The audience response data include the manual annotations as well as YouTube likes/dislikes and comments.

We find that the direct content modeling system performs better when we combine the video, audio, and text dimensions of the content than employing any of these dimensions alone. For predictive analysis based on a single dimension, the video stream has the highest predictive power. The central implication of our study is that firms can train deep learning algorithms directly on the content of existing commercials and use them to reliably predict audience response of unreleased media content.

Keywords: deep learning, advertising effectiveness, multimedia content analysis, long short-term memory, convolutional neural network

Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences

Hyunhwan “Aiden” Lee, Joseph Johnson, and Gerard J. Tellis. “Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences”

http://geoinfluence.net

  • Preparing for submission at Journal of Marketing Research
  • The 41st Annual ISMS Marketing Science Conference, June 20 – 22, 2019, Rome, Italy
  • The 2019 Haring Symposium, April 19 – 20, 2019, Bloomington, IN.
  • The 1st Interdisciplinary Research Cluster Day, April 1, 2019, Coral Gables, FL.
  • The 3rd Annual Three Minute Thesis Competition, Feb 6, 2019, Coral Gables, FL.
No brand is the market leader uniformly across geographic locations. Brands have strong and weak markets. Therefore, analysts need to track brand preferences by location. However, current methods of tracking geographic brand preferences (e.g., surveys, company sales, scanner panel data, Google Analytics, etc.) have limitations: limited sampling of brands, stores, and categories with time and region aggregation. We address these limitations through a spatial, nonparametric approach to track brand preferences at the micro-temporal and micro-geographic level using Twitter. Our approach inherits the intrinsic strengths of social media: micro-temporal, micro-geographic, faster than news media, higher frequency than sales, low cost, competitive monitoring, sentiment inclusion, and social network inclusion. A key feature of our approach is modeling the temporal geographic influence of social connections based on the followers of each post. We estimate our model for carbonated soft drinks brands. Our validation uses Pepsi sales, an apparel fashion brand, and the 2016 U.S. presidential election (a domain where marketing is critical). We find that geographic influence adds predictive power to micro-temporal and micro-geographic brand measures, leads online sales and predicts elections. Our study implies that the micro-temporal and micro-geographic social media measures provide actionable insights for marketing brands and political candidates.

Keywords: micro-temporal brand preference, micro-geographic brand preference, sentiment analysis, Twitter, geographic influence model, election prediction