Driving consumers’ brand perception and engagement: A large-scale meta-analysis of video ads using Google machine learning x human coding

Minh Nguyen, Kosuke Yoshida and Tomoya Naohara

Google, a technology brand, developed a robust research framework to eta-analyse the quality of video ads in Japan by blending Google machine learning and human coding.

Keywords: creative meta-analysis, ads research, computer vision, machine learning, cloud computing

Summary

Bad ads create bad experiences for consumers and negatively impact your brand. It is critical to know what creative element or pattern can help drive brand performance and direct response. However, creative A/B tests or lab tests with facial expression analysis are expensive and not scalable. In this research, we used Google Cloud APIs to compute thousands of features from large creative samples to conduct creative meta-analysis. We added those features from machine learning and computer vision with human encoding elements. These elements cover emotional feelings which...

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