Leaked Google documents tell us about Attributes and their use in Rankings

Documents describing Google's ranking systems in great detail have been leaked by someone who sent an email to Rand Fishkin. In this video, I share my thoughts on Rand's post and then at around 35 mins or so I play around with analyzing this document with Gemini 1.5 Pro in AI Studio. Hope you enjoy this type of video. I share a lot of my thoughts on how machine learning systems work to use all of the many attributes described in this documentation. This includes links, clicks, return to search results and much more. Take home point: Google uses MANY signals/attributes/features in their calculations that predict what is likely to be helpful to the searcher. Our goal should be to create content that people choose to engage with and ultimately find to be the satisfying answer to their query - or in other words, HELPFUL. This episode may be best seen as a video podast: https://www.youtube.com/watch?v=kVYUvh4Vt30 Links mentioned Marie’s newsletter: mariehaynes.com/newsletter Marie’s notes (paid newsletter - follow along as I take notes on things like this each week.) https://community.mariehaynes.com/spaces/12261861/feed Marie’s Course and book: SEO in the Gemini Era is out soon. The Docs referred to in this video: https://hexdocs.pm/google_api_content_warehouse/0.4.0/api-reference.html Rand Fishkin’s post. He received an email with the contents of the leak. https://sparktoro.com/blog/an-anonymous-source-shared-thousands-of-leaked-google-search-api-documents-with-me-everyone-in-seo-should-see-them/ Mike King’s article on these documents: https://ipullrank.com/google-algo-leak