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.
Stop! Is your methodology biased? Ad measurement provides 'Accountability' (to prove that ads work), however, we argue that measurement should produce 'Incrementality' (help businesses grow with ads). To measure true ad effectiveness - incrementality, we have to move away from the long-accepted methodology: pre vs. post-campaign or non-exposed vs. exposed. For this presentation we will redefine ad measurement and demonstrate how we measure it at Google with examples.
Stop! Is your methodology biased? Ad measurement provides 'Accountability' (to prove that ads work), however, we argue that measurement should produce 'Incrementality' (help businesses grow with ads). To measure true ad effectiveness - incrementality, we have to move away from the long-accepted methodology: pre vs. post-campaign or non-exposed vs. exposed. For this presentation we will redefine ad measurement and demonstrate how we measure it at Google with examples.
With the ESOMAR/MRSI Asia Pacific conference rescheduled to 1-3 November, programme committee member Takeshi Oshima from insurance company MetLife talks about what programme features inspire him and invites some of the presenters to join him for a discussion on why Asia Pacific is a hotbed for AI innovation.