Search Engine Ranking Factors 2015

Here, we present Moz's analysis of 17,600 keyword search results from Google.com (US). The numbers shown are mean Spearman's correlation with higher rankings, meaning that a higher number indicates that websites/pages with the given feature tended to rank higher on average than those without.

Remember: Correlation is not causation! Just because websites/pages with a given feature tend to rank higher doesn't necessarily mean that this particular feature is the cause of that higher ranking. That said, we can learn a lot from correlation studies!

All correlations rated on a scale from -1.00 (negative influence) to 1.00 (strong influence).

Page-Level Keyword Usage Features

These features describe use of the keyword term/phrase in particular parts of the HTML code on the page (title element, H1s, alt attributes, etc.) as well as semantic relevance and language modeling of the given keywords.

We continue to see lower correlations between on-page keyword use and rankings. This could likely be because Google is smarter about what pages mean (through related keyword, synonyms, close variants and entities) without relying on exact keyword phrases. We believe matching user intent is of utmost importance.

Page-Level Keyword Agnostic Features

These elements describe non-keyword-usage, non-link-metrics features of individual pages (such as length of the page, load speed, etc.).

While page length, international targeting, and total number of links all show moderate association with Google rankings, we found that using HTTPS has a very low positive correlation. This could indicate it’s the “tie-breaker” Google claims. Negative associated factors include server response time and the total length of the URL.

Page-Level Link-Based Features

These features describe link metrics to the individual ranking page (such as number of links, PageRank, etc.)

Despite rumors to the contrary, the data continues to show some of the highest correlations between Google rankings and the number of links to a given page.

Domain-Level Keyword Usage Features

These features cover how keywords are used in the root or subdomain name, and how much impact this might have on search engine rankings.

While there exists a decent correlation between exact match domains (domains where the keyword matches the domain exactly, i.e. redwidgets.com) and rankings, this is likely due to the prominence of anchor text, keyword usage, and other signals, instead of an algorithmic bias in favor of these domains.

Domain-Level Keyword-Agnostic Features

These features relate to the entire root domain, but don't directly describe link or keyword-based elements. Instead, they relate to things like the length of the domain name in characters.

Our study showed little relationship with the type of top-level domain (.com, org, etc.) and rankings in Google.

Domain-Level Link-Authority Features

These features describe link metrics about the domain hosting the page.

While not quite as high as page-level link metrics, the overall links to a site’s root and subdomain showed a reasonably strong correlation to rankings. We believe links continue to play a prominent role in Google’s algorithm.

Anchor Text Features

These features describe the anchor textof the links pointing to both the page and domain.

Use of anchor text was another prominent feature of high-ranking results, with the number of unique domains linking with partial-match anchor text leading the way.

Social and Brand Features

These features relate to third-party metrics from social media sources (Facebook, Twitter, Google+, etc.) for the ranking page, and also to brand mentions across the web.

Always controversial, the number of social shares a page accumulates tends to show a positive correlation with rankings. Although there is strong reason to believe Google doesn’t use social share counts directly in its algorithm, there are many secondary SEO benefits to be gained through successful social sharing.

Spam Flags

These features relate to features often associated with banned or penalized domains, as defined by Moz’s Spam Score.

Link Metrics from Ahrefs

These features relate to link metrics from our data partner Ahrefs.

Traffic and Engagement Metrics from SimilarWeb

These features relate to traffic and engagement metrics from our data partner SimilarWeb. Traffic data is from April and May, 2015.

Domain Registration Features from DomainTools

These features relate to traffic and engagement metrics from our data partner DomainTools.