You have seen that A/B testing enables you to take a data-driven approach to improving the product. Here at Netflix we use A/B testing extensively to improve personalized recommendations on the homepage, playback, non-member signup flow, etc. One of the newer areas of A/B testing is around selecting the optimal image asset for every video on the service to best represent titles at a glance.
This session will explore the incremental steps towards building a sequence of A/B tests from a set of hypotheses about image asset selection, the fastest way to learn what improves the product, challenges with foundational data used for such tests, scaling challenges; test analyses, etc. Some of the details can be found in this tech blog here: http://techblog.netflix.com/2016/05/selecting-best-artwork-for-videos.html
Photo credit Richard Foster;
Photo credit https://commons.wikimedia.org/wiki/File:Youth-soccer-indiana.jpg
“Analyze this” movie by Time Warner. https://en.wikipedia.org/wiki/Analyze_This