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

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

  • accepted for Theory + Practice in Marketing Conference 2019
  • Submitted to Journal of Marketing.
  • Artificial Intelligence in Management Conference, University of Southern California. March 14, 2020

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

Brand Hazard: A Prognostic Complement to Customer-Based Brand Equity

Joseph Johnson, Debanjan Mitra, Sivaramakrishnan Siddarth, Hyunhwan “Aiden” Lee

  • Revision invited, Journal of Marketing

Existing customer-based brand equity metrics are built on customers’ choices and preferences and not on the dynamics inherent in their brand switching and timing, which potentially handicaps the prognostic ability of such metrics.   To remedy this we develop a complementary metric that focuses on the rate of change in customers’ purchase transitions. We use the counting process to model the repeat and switching rates of a brand, parse out baselines for each transition rate, cumulate these baselines over a given duration, and combine them to obtain brand hazard (BH). Using customer panel data, we estimate BH for six leading brands in three product categories over sixteen two-year moving windows.  We find increases in BH foretells future declines in the intercept of logit models of customers’ brand choice (BCI) – a customer based brand equity metric estimated from the same data. More important, we find that changes in BH and BCI are able to predict future changes in a brand’s price premium and revenue premium in the broader market.  Our findings imply that BH complements BCI, and tracking them together provides a comprehensive prognosis of a brand’s health.

Key Words:  Branding, Brand Equity, Brand Management, Hazard Models, Markov Process, Counting process