Google & Search Behavior Ranking

google_searchGoogle has for some time used behavior as part of their search ranking algorithm. Essentially, Google collects some non-personal user data to enhance and further refine their search results. This is very similar to the data that you collect with your website. Metrics like clicks, exits, IPs, location, search queries and such are all metrics Google has used to improve their search results.

We have known for some time that Google can make adjustments to a page’s rank based on these metrics. For example, a page receives 5K visits for the search term “happy guy”, but 4K of them bounce and return to search again for related terms. This tells Google that this page is probably not a great match for the term “happy guy” and they adjust rank accordingly.

On October 27th, Google was granted a patent for this ranking methodology. In essence, the patent referred to as “Rank-adjusted content items” seeks to use click logs and query logs are processed to identify statistical search patterns.


This disclosure relates to identifying content items.

Content items, e.g., video and/or audio files, web pages for particular subjects, news articles, etc., can be identified by a search engine in response to a query. The query can include one or more search terms, and the search engine can identify and rank the content items based on the search terms in the query. Typically the content items are displayed according to the rank.

The content items, however, are often identified only in response to a particular query, i.e., the search engine may identify and rank content items to independently for each query. For example, for three different queries, the search, engine may return a particular identification and rank of content items for each particular query, regardless of the other queries. In such implementations, a particular content item that may be highly relevant to a user’s current interests may not be identified and/or highly ranked and presented to the user until the user has conducted multiple searches. Additionally, other users may experience similar challenges when searching for content.


Disclosed herein are systems and methods of identifying content items. In one implementation, click logs and query logs are processed to identify statistical search patterns based on the click logs and query logs. A search session is compared to the statistical search patterns. Content items responsive to a query of the search session are identified, and a ranking of the content items is adjusted based on the comparison.

While I am certain that many will have a word about this and how it relates to both our own search habits and information, this is really a positive thing. Think about the last time you searched and the result was not as you had expected or was not a match…. This patent really seeks to use behavior and raw data to improve the entire search experience.

Said it before, say it again… Google is running its business much as we seek to run our own. Happy searchers are happy customers. Let’s not forget that better search metrics mean better, more targeted and convertible traffic for us all.