Moz is a Software as a Service (SaaS) company located in Seattle, Washington that sells inbound marketing and marketing analytics software subscriptions. Founded by Gillian Muessig and Rand Fishkin in 2004 as a consulting firm, Moz then switched to software development in 2008.
Every two years, Moz runs a survey that collects the opinions from several of the world’s most effective search marketers and researches the correlation studies to come up with a better understanding of search engine algorithm operation. They gather the information to find out what helps or hurts a website’s visibility in search engines. This survey provides an idea of what characteristics the web pages which tend to rank higher have in common.
MOZ Ranking Factors
Moz has created some metrics that play an integral part in their analyses including, MozRank, MozTrust, Domain Authority and Page Authority. All of these are determined on a logarithmic scale, which basically means that as one moves up the scale, increased rankings become more difficult to achieve.
Overall Ranking Factors
• Place Page Signals 19.6% (Categories, Keyword Business Title, Proximity, etc.)
• On-page Signals 18.8% (Presence of NAP, Keywords in Titles, Domain Authority, etc.)
• External Location Signals 16% (IYP/aggregator NAP consistency, Citation Volume, etc.)
• Link Signals 14.4% (Inbound anchor text, Linking domain authority, Linking domain quantity, etc.)
• Review Signals 10.3% (Review quantity, Review velocity, Review diversity, etc.)
In comparison, the following factors received no votes from any of the survey respondents. These factors may still contribute to local search engine rankings, but are not considered high-priority items.
• Association of Videos with Local Plus Page
• Author proximity to authority Google+ accounts
• Clicks to Call Business
• Diversity of Inbound Links to Places Landing Page URL
• High Numerical Rating of hReview/Schema Testimonials
Negative Ranking Factors
• Listing detected at false business location
• Keyword stuffing in business name
• Mis-match NAP / Tracking Phone Numbers Across Data Ecosystem
• Incorrect business category
• Presence of Multiple Place Pages with Same/Similar Business Title and Address
Domain Level Anchor Text
These features describe anchor text metrics, both partial and exact match, about the root domain hosting the page. For example, for the page www.test.com/A, these features are for anchor text links pointing to *.test.com, not just A.
Domain Level Keyword Agnostic
These features relate to the entire root domain. However, they don’t directly describe link or keyword-based elements. Instead, they relate to things like the length of the domain name in characters.
Domain Link Authority Features
These features describe link metrics about the root domain hosting page. As in the 2011 Ranking Factors, metrics that capture a diversity of link sources (C-blocks, IPs and domains) have high correlations. Within the domain/subdomain level, subdomain correlations are larger than domain correlations.
Page Level Keyword Agnostic
These elements describe the non-keyword usage and non-link metrics features of individual pages such as length of the page and load speed. This year’s survey revealed an interesting negative correlation (-0.10) to page response time.
Page Level Social Metrics
These features relate to third-party metrics from social media sources such as Facebook, Twitter and Google+ for the ranking page.
Social signals were some of the highest correlated factors, with Google+ edging out in front of Facebook and Twitter.
Domain Level Brand Metrics
These features describe elements of the root domain that indicate qualities of branding and brand metrics.
This study was carried out by tracking domain name mentions in Fresh Web Explorer. The correlations for mentions are relatively high, winding up between 0.17 and 0.20 for mentions of the full domain name.
Domain Level Keyword Usage
These features cover how keywords are used in the root of subdomain name and how much impact this might have on search engine rankings.
The ranking ability of exact and partial match domains (EMD/PMD) has been heavily debated by SEOs recently, and it seems Google is still adjusting their ranking ability. It was discovered EMD correlations were relatively high at 0.16 and as high as 0.20 if the EMD is also a dot-com.
Page Level Anchor Text
These features describe anchor text metrics, both partial and exact-match anchor text links.
Even with Google’s crack down on over-optimized anchor text, high correlations with both partial and exact match anchor text to the URL were found, with a 0.29 correlation on a number of root domains linking to the page with partial match anchor text.
Page Level Keyword Usage
These features describe use of the keyword term/phrase in particular parts of the HTML code on the page such as the title element, H1s, alt attributes and more.
The data measures the relationship between the keyword and the document both with the TF-IDF score and the language model score. It was found that the title tag, the body of the HTML, the meta description and the H1 tags all had relatively high correlation.
Page Link Authority Features
These features describe link metrics to the individual ranking page such as number of links and MozRank.
Page Authority is a machine learning model inside the Mozscape index that predicts ranking ability from links and, at 0.39, it is the highest correlated factor in the study.