Furthermore, given the multidimensional nature of artwork, we might be missing many other possible factors that might explain an artwork’s success, such as figure orientation, the color of the background, facial expressions, etc. The manual nature of these tasks means that we cannot test many titles at a time. However, this approach has a major drawback: it is not scalable because we either have to label images manually or create new asset variants differing only in the feature under investigation. In the past, we have attempted to design A/B tests where we investigate one aspect of artwork at a time, often within one particular genre. Given Netflix’s vast and increasingly diverse catalog, it is a challenge to design experiments that both work within an A/B test framework and are representative of all genres, plots, artists, and more. We will discuss a causal framework that will help us find and summarize the successful components as creative insights, and hypothesize and estimate their impact. We are going to make an assumption that the presence of a specific component will lead to an artwork’s success. What if we could use machine learning and computer vision to support our creative team in this process? Through identifying the components that contribute to a successful artwork - one that leads a member to choose and watch it - we can give our creative team data-driven insights to incorporate into their creative strategy, and help in their selection of which artwork to feature. Our creative team helps make this happen by designing promotional artwork that best represents each title featured on our platform.

A framework to identify the causal impact of successful visual components.īy Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi IyengarĪt Netflix, we want our viewers to easily find TV shows and movies that resonate and engage.

Machine learning vs deep learning