Searchmetrics

Searchmetrics Ranking Factors: Rethinking Search Results Based on Google’s Deep Learning and Content Relevance

Submitted by RealWire on Tue, 12/13/2016 - 12:25

Searchmetrics' annual study of top Google ranking factors undergoes radical format shift to match industry-specific needs and results

SAN MATEO, Calif., December 13, 2016 - Today's search results are shifting dramatically to match answers to the perceived intent of a search, as Google and other search engines increasingly employ deep learning techniques to understand the motivation behind a query, according to key findings in a new Searchmetrics study.

Findings from the latest Searchmetrics Ranking Factors study, "Rebooting for Relevance," suggest marketers face new challenges as Google deemphasizes traditional ranking factors such as collecting more backlinks and employing enough focus keywords in text. As technical SEO factors become table stakes in online content strategies, marketers in various industries will be forced to adopt new techniques to succeed," said Marcus Tober, Searchmetrics founder and CTO.

"Google revealed last year that it is turning to sophisticated AI and machine-learning techniques such as RankBrain to help it better understand intent behind the words searchers enter, and to make its results more relevant," Tober says. "User signals such as how often certain results are clicked and how long people spend on a page help the search engine get a sense of how well searchers' questions are answered. That allows it to continually refine and improve relevance."