clear that any reasonable search engine would use click data on its own results to drive rankings to improve the quality of search results. Results that are clicked infrequently should fall to the bottom because they are less relevant, and results that are clicked frequently rise to the top. Building a feedback loop is a fairly obvious quality step forward for search and recommendation systems, and a smart search engine would integrate the data.
The actual mechanisms for using click data are often proprietary, but Google makes it clear that it uses click data with its patents on systems such as “ranking-adjusted content items”[1]. WordStream research suggests that for a niche's long-tail search terms, if a page:Beats the expected CTR for a given position by 20%, you are likely to appear in position 1. Beats company employee list the expected CTR for a given position by 12%, then you are likely to appear in position 2. Falls below the expected
CTR for a given position by 6%, then you are likely to appear in position 10. People think that RankBrain specifically looks at the query, pages visited, searcher intent (mostly informational or transactional), and time spent interacting with that page (dwell time). In theory, Rank rain determines whether the web page fulfills the intent of the query. Then, future search results will be biased towards satisfying this imagined intention. Think about this: how do people interact with your page when they click on it? Do they quickly return to search results to seek