Youtube: How do you do it?
When we search up content on Youtube, why are some videos at the top whereas the others are in the later pages? We learned in class about how content is displayed on websites and how that affects how likely a user is going to click on the particular video through the clickthrough rate. How often an individual is engaged with a video based on the slot that the video is in also plays a key role in understanding the financial impacts such as how much profit a content creator makes by having their video in slot two in comparison to slot two. This article highlights the Youtube Algorithm and the recent changes made to the algorithm as well as important key metrics that they considered to rank the videos my prevalence for the user. This aspect is key as users of Youtube to understand why certain videos are recommended to us whereas other videos may be recommended to our friends. In addition, for content creators, this plays a major role in their strategy in curating content specific to a target audience and really understanding who their audience is in order to create content that will be consistently recommended to them.
First, let’s go through a brief history of the algorithm and how it has changed throughout the years. Starting in 2005 when Youtube was created, Youtube’s algorithm would have the videos with the most views or clicks at the top slot. As a result, this led to many videos having clickbaits on the thumbnails so more people would click on it. Later in 2012, the algorithm was adjusted for how much time an individual spent on the videos which led to creators creating shorter videos in order for viewers to finish the whole video. Eventually, Youtube realized that although they were spending more time on the product, it doesn’t mean that users are satisfied with the content. As a result, in 2015, Youtube changed its algorithm to measure viewer satisfaction through surveys and one-click response metrics such as likes and “not interested” buttons.
In the present day, what we see on our feed is selected based on the ranking signals which are personalization, performance, and external factors. The Youtube Algorithm plays a key role for the Youtube homepage, the results displayed after search, and the suggested videos on the sidebar. Personalization is measuring what the user previously watched and performance is understanding if a user is interested in the video and if they finished watching. Performance for the homepage is typically measured through metrics like the click-through rate as well as likes and dislikes in order to gauge engagement. External factors include current news in the world but more targeted specifically to the user. For example, if the user is interested in cooking, an external factor playing into their algorithm would be Gordon Ramsey. Performance and personalization for suggested videos on the sidebar are measured through recommendations such as which videos are typically watched together. For example, if we are watching clips of a movie, we might watch “Part I” and then on the recommended side for the suggested videos, we might see “Part II.” For the search algorithm, Youtube is particularly interested in the keywords as well as the performance to understand the viewer’s engagement more such as the survey feedback. For example, if a user searched up a subject like “Microeconomics,” if individuals rated the video highly on the video’s performance with metrics like the number of likes and positive survey feedback, then the search results would have this video in the top slots.
From learning about the Youtube algorithm, we understand as viewers how important it is to have the right keywords when we do search results and how our likes/dislikes measures the performance of the videos in terms of their ranking on the page. For marketers and content creators, it is especially important to understand where to place certain keywords about what their content is about as well as what videos their users would want more engagement. Understanding who their audience is plays a key role in developing their customer base because the Youtube algorithm will recommend videos often watched together on the suggested video component of the product. I thought this article was really interesting because we use content sites like Youtube nearly every day for entertainment purposes and we understand that our feed is different from our friend’s but haven’t truly understood how Youtube does so and what factors does the product consider. This also takes into account how much personalization of our videos is good for us and how personalization can affect our perspectives or not affect our perspectives.