The search engines constantly strive to improve their performance by providing the best possible results. While "best" is subjective, the engines have a very good idea of the kinds of pages and sites that satisfy their searchers. Generally, these sites have several traits in common:
- Easy to use, navigate, and understand
- Provide direct, actionable information relevant to the query
- Professionally designed and accessible to modern browsers
- Deliver high quality, legitimate, credible content
Despite amazing technological advances, search engines can't yet understand text, view images, or watch video the same way a human can. Thus, in order to understand content they rely on meta information (not necessarily meta tags) about sites and pages in order to rank content. Web pages do not exist in a vacuum - real human beings interact with them. Search engines use data to "observe" how people engage with web pages, and this gives them incredible insight as to the quality of the pages themselves.
On Search Engine Rankings
There are a limited number of variables that search engines can take into account directly, including keywords, links, and site structure. However, through linking patterns, user engagement metrics and machine learning, the engines make a considerable number of intuitions about a given site. Usability and user experience are "second order" influences on search engine ranking success. They provide an indirect, but measurable benefit to a site's external popularity, which the engines can then interpret as a signal of higher quality. This is called the "no one likes to link to a crummy site" phenomenon.
Crafting a thoughtful, empathetic user experience can ensure that your site is perceived positively by those who visit, encouraging sharing, bookmarking, return visits and links - signals that trickle down to the search engines and contribute to high rankings.
Signals of Quality Content
1. Engagement Metrics
When a search engine delivers a page of results to you, they can measure their success by observing how you engage with those results. If you hit the first link, then immediately hit the "back" button to try the second link, this indicates that you were not satisfied with the first result. Since the beginning, search engines have sought the "long click" - where users click a result without immediately returning to the search page to try again. Taken in aggregate over millions and millions of queries a day, the engines build up a good pool of data to judge the quality of their results.
2. Machine Learning
In 2011 Google introduced the Panda Update to its ranking algorithm, significantly changing the way it judged websites for quality. Google started by using human evaluators to manually rate 1000s of sites, searching for "low quality" content. Google then incorporated machine learning to mimic the human evaluators. Once its computers could accurately predict what the humans would judge a low quality site, the algorithm was introduced across millions of sites spanning the Internet. The end result was a seismic shift which rearranged over 20% of all of Google's search results. For more on the Panda update, some good resources can be foundhere and here.
3. Linking Patterns
The engines discovered early on that the link structure of the web could serve as a proxy for votes and popularity - higher quality sites and information earned more links than their less useful, lower quality peers. Today, link analysis algorithms have advanced considerably, but these principles hold true.
All of that positive attention and excitement around the content offered by the new site translates into a machine parse-able (and algorithmically valuable) collection of links. The timing, source, anchor text, and number of links to the new site are all factored into its potential performance (i.e., ranking) for relevant queries at the engines.
Now imagine that site wasn't so great - let's say it's just an ordinary site without anything unique or impressive.