Quality Content Achieves Marketing Goals
You should define content quality based on how much you get out of it, not how much time and money you put into it. Imagine you own a baseball team and need to add a batter to your roster. Are you going to sign a player based on their size or their looks? Or how he speaks? Or maybe how many social media followers he has?
You would look at the things that matter, like stats - hits, homers, on-base percentage, etc. You know, how the player actually behaved on the pitch. Unicorn content can be long or short, have zero images or 10, and have a few spelling mistakes or totally perfect grammarization. Ultimately, it comes down to whether your foundational content is achieving its marketing goal, whether it's driving traffic, rankings, engagement, or conversions.
Quality content ranks well in Google
Google uses machine learning as part of its RankBrain algorithm, which is used on every search. One thing all machine learning systems have in common is that they reward high engagement. How does Google measure engagement? I think it's through a combination of click-through rate (people clicking on your content) and dwell time (people spending time and/or interacting with your content). CTR is important for SEO because for every 3% increase or decrease in the CTR of your experience, your cell phone number list position can increase or decrease by one point.
Meanwhile, the data reveals how Google is slowly weeding out traffic to pages with low dwell times (the time people spend on your website after clicking on your search result listing). We cannot measure dwell time, but time spent on site is proportional to dwell time. Previously, Google SERP positions were mainly determined by who had the most/best links and the most relevant content. While these remain important ranking factors, it is now just as important that people engage with your content if you want to rank well.
Quality content has a remarkable CTR
Before Google used machine learning as an organic search ranking signal, Google used machine learning in Google Ads (they also used it for the Google Display Network, Gmail ads, and YouTube). If your ad has a higher Quality Score, you pay less and your ad appears more prominently. if your ad has a lower Quality Score, you pay more and your ad impression share is much lower.