{"id":1051,"date":"2019-02-16T14:39:08","date_gmt":"2019-02-16T12:39:08","guid":{"rendered":"https:\/\/www.dazzlepop.net\/site\/?p=1051"},"modified":"2019-02-14T20:58:17","modified_gmt":"2019-02-14T18:58:17","slug":"how-reputation-became-a-major-ranking-factor-in-seo","status":"publish","type":"post","link":"https:\/\/www.dazzlepop.net\/site\/how-reputation-became-a-major-ranking-factor-in-seo\/","title":{"rendered":"How reputation became a major ranking factor in SEO"},"content":{"rendered":"
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To stay on top today, you need to be diligent in assessing your site’s usability and the quality of its content.<\/h2>\n

Over 10 years ago, I predicted that Google could use quality scoring for organic rankings, and I also proposed a number of ways they could quantify the quality of websites and specific factors that could be vital for this. The recent core algorithm updates and Medic Update over the past year, and publication of the Quality Rating Guidelines, are largely indicating that a business\u2019s reputation is also key to this as well. If you\u2019re interested in how this all may function, read on.<\/p>\n

I first\u00a0predicted<\/a>\u00a0Google might apply a Quality Score to organic search in 2007, and in subsequent years I highlighted the need for: About Us pages so people can know who is behind a website; good Contact Us pages to instill trust; good usability and user-experience; copyright statements; and good spelling and grammar. I have predicted enough of these factors to still see the direction of where the algorithm evolution has been heading.<\/p>\n

The most recent edition of Google\u2019s \u201cQuality Evaluators Guidelines\u201d, also called \u201cQuality Rating Guidelines\u201d (\u201cQRG\u201d) is nearly a script of my past recommendations involving quality factors.<\/p>\n

Google Quality Rating guidelines seem relevant to recent updates<\/h2>\n

It seems clear that quality factors, such as those listed in the QRG, have become more influential in Google search rankings.<\/p>\n

You may already be saying, \u201coh, no \u2013 he is barking up the wrong tree about the Quality Guidelines\u201d, since others have written about them, too, since they were released back in July (as well as with earlier \u201cleaked\u201d releases), and Googler\u2019s have stated that the raters\u2019 scores are not used directly in websites\u2019 rankings. Notably, Jennifer Slegg\u00a0reported<\/a>\u00a0that Danny Sullivan confirmed that the ratings of the human evaluators are not used in machine learning for the algorithms,\u00a0replying<\/a>\u00a0on Twitter, \u201cWe don\u2019t use it that way.\u201d He also\u00a0noted<\/a>\u00a0that rater data is used like restaurants using feedback cards so that they know if their search \u201crecipes\u201d are working:<\/p>\n

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Others have pointed out specific things mentioned in the Guidelines as likely signals, particularly involving Expertise, Authority and Trust (Google cites those as \u201cE-A-T\u201d). For instance, Marie Haynes\u00a0called-out<\/a>\u00a0how she felt that elements mentioned in the Quality Raters Guidelines might be influential ranking factors, such as a business\u2019s BBB ranking and authors\u2019 reputations. Others criticized her for citing the BBB rating and author reputations as a direct ranking factor, and subsequently, John Mueller at Google essentially states that Google was\u00a0not researching author\u2019s reputations<\/a>\u00a0nor\u00a0using proprietary rating scores<\/a>\u00a0like the BBB rating grade.<\/p>\n

At the same time, Googlers have increasingly been advising webmasters to \u201cjust focus on quality,\u201d and even recommending that webmasters read the QRG in offering the best content possible \u2014 this is what Danny Sullivan did when he officially commented upon last year\u2019s core algorithm updates in October:<\/p>\n

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And, Ben Gomes, Google\u2019s VP of Search, Assistant and News, in an interview last year\u00a0stated<\/a>:<\/p>\n

\u201cYou can view the rater guidelines as where we want the search algorithm to go. They don\u2019t tell you how the algorithm is ranking results, but they fundamentally show what the algorithm should do.\u201d<\/p><\/blockquote>\n

So, it rather begs the question \u2013 how exactly is Google determining Quality algorithmically, involving somewhat subjective-seeming concepts of expertise, authoritativeness, trustworthiness and reputation? Google\u2019s algorithms must translate these concepts into quantifiable criteria that can be measured and compared between competing sites\/pages.<\/p>\n

I think that some of Google\u2019s past algorithmic development likely points towards the process, and I think that there has been some degree of disconnect between what people have asked, how various Googlers have answered and how people have interpreted those answers. Much of the conjecture has seemed far too reductionist, focusing on relatively naive, theoretical factors. Factors that Googlers have directly denied, in some instances.<\/p>\n

If Google instructs its human raters to assess E-A-T of a site but does not incorporate the resulting ratings, what is the algorithm using? Simply stating that the algo uses a collection of things like a BBB rating, user reviews, or link-trust analysis seems far too limited. Likewise, stating that Google comes up with a quality assessment based purely upon\u00a0link trust analysis<\/a>\u00a0and\u00a0query analysis<\/a>\u00a0seems too limited \u2013 Google is obviously taking into account some factors beyond just more advanced link\/query analysis, although that is also some part of the mix.<\/p>\n

Google\u2019s website quality patent<\/h2>\n

One of Google\u2019s past patents seems to me to describe what they could be using, or something quite similar, and it relies upon machine learning. It\u2019s \u201cWebsite Quality Signal Generation<\/a>,\u201d and it was\u00a0noted<\/a>\u00a0by Bill Slawski in a brief synopsis and commentary in 2013 when the patent was granted. It describes how humans could be used to rate the quality of websites, and then an analysis algorithm could associate those ratings with website signals \u2013 likely automatically identifying relationships between quantified signals and the human rating values \u2013 and thus generate models from the characteristic signals. These signal models could then be used to compare against other unrated websites to apply a quality score to them. The wording is quite fascinating:<\/p>\n

\u201cRaters (e.g., people) connect to the Internet to view websites and rate the quality of each of the websites. The raters can submit website quality ratings to the quality analysis server through the rating input device. The quality analysis server receives website quality ratings from the rating input devices and stores the website quality ratings in the signal store. The website quality ratings are associated with a uniform resource locator and other website signals corresponding to the rated website. The quality analysis server identifies relationships between the website quality ratings and website signals and creates a model representing the relationships, as described below.<\/i><\/p>\n

Further, the quality analysis server searches the signal store for unrated websites (e.g., sites lacking a signal indicating a quality rating). The quality analysis server determines whether the unrated websites have website signals that are related to quality ratings and applies the model to the unrated websites. Application of the model results in a calculated quality rating. The quality analysis server assigns the calculated quality rating to the corresponding website. The calculated quality rating is an additional website signal that is stored in the signal store for use by other applications.\u201d<\/i><\/p>\n

\u2026<\/i><\/p>\n

\u201cThe search engine can use the website signals stored in the signal store to filter and\/or order the search results based on the stored quality ratings of the websites. For example, a threshold can be used to filter websites that have a stored quality rating below the threshold. Additionally, the websites returned by the search device can be ordered according to their stored quality rating such that the websites having higher stored quality ratings are listed prior to websites having lower stored quality ratings.\u201d<\/i><\/p><\/blockquote>\n

The patent also provides some examples of the sorts of things that could be used as quality factors:<\/p>\n

\u201c\u2026quality factors can be used such as the correctness of the grammar and spelling of the text on the web pages, whether obscene or otherwise inappropriate material is presented, whether the websites have blank or incomplete pages, as well as other factors that would affect the quality of the website.\u201d<\/i><\/p><\/blockquote>\n

You will note that these are some of the very things mentioned in the QRG. But, that is not the reason why the patent is so compelling \u2013 it is because it provides a very logical framework in which to develop methods for assessing websites\u2019 and webpages\u2019 quality, and for generating a Quality Score that can be used in ranking determinations. The methods involved indicate that relatively small sample sets of test pages could be used to create models that could work well across all other pages of similar types.<\/p>\n

Imagine that you have identified a type of page that you would like to create a Quality Score for \u2014 such as an information article about a health topic. (This is a \u201cYour Money or Your Life\u201d type of page, or \u201cYMYL\u201d page as explained in the QRG.) Google could take signals they have mentioned before that they can measure, including: amount of content, page layout, number of ads and ad placement on the page (are they above the fold or interstitial \u2014 do they obscure or interfere with accessing the page\u2019s main content), reviews about the site, reviews and links about the content creator if different from the site, links out from the page (perhaps indicating identity of content creator and\/or citing info reference sources), links to the site and page (PageRank), clickthrough rates to the page from its prime keyword searches, correspondence of the visible headline\/title at top of the page with the page\u2019s content and search snippet title and meta description, and factors that indicate the site is on the up-and-up.<\/p>\n

Through testing of numbers of similar types of pages, Google could have developed a model \u2013 a pattern \u2013 of the combination of quality signals and developed a score value associated with it. Essentially, when a health topic article page has a certain range of the criteria signals like PageRank, plus content layout and quantity of similar type, plus certain inlink PageRank, plus CTR, plus user reviews \u2013 then Google could apply the computed Quality Score to the page without having any human manually review it. It seems likely that Google has developed some such models for many different types of pages.<\/p>\n

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The reason to think that Google could be incorporating machine learning in this sequence is that it could come up with quality rating relationships in the data that would not be simple toggles that they manually set with different weightings. Instead of clumsily setting a weight for a particular signal (like, saying that a health topic article of a certain quality rating level should have minimum PageRank of X, and the minimum number of links, etc), with machine learning the system could identify more complex relationships (like, assign a certain quality score if PageRank is of N to NN, CTR is X to XX, paired with a specific page layout type).<\/p>\n

The patent describes this possibility:<\/p>\n

\u201cIn some implementations, the model can be derived from the website signals and the website quality ratings using a machine learning subsystem that implements support vector regression.\u201d<\/i><\/p><\/blockquote>\n

Essentially, this produces a machine learning classifier. The model can then be used to identify all pages of a common class, calculate a quality score, and then apply the same or similar rating to other members of the same class of pages.<\/p>\n

Whether Google inputs the data directly from the pages and their quality evaluators\u2019 ratings of them into a machine learning system or not does not really matter so much \u2013 they could still use a number of \u201cmodels\u201d of types of pages and then check them against how the quality raters assess them and then manually tweak weightings. But, with the sort of massive firepower Google has at their disposal, I tend to think this does not make sense anymore. Indeed, quite some other SEO analysts are also concluding that Google is incorporating machine learning into content rankings (such as\u00a0Eric Enge and Mark Traphagan<\/a>), and not just in query interpretation which Google has\u00a0publicly disclosed<\/a>.<\/p>\n

This really explains some of the vagueness that has come from Google when asked what to do to address ranking drops from the core algorithm changes. Support vector machines, or neural networks, will come up with scores very holistically, so pointing to any one signal or even a handful of signals as influencing the outcome most in any given instance probably would not really be possible \u2014 it would be buried in the complex interior of the models, which might be fairly abstracted.<\/p>\n

Once the computed models are applied to search rankings, Google then has their evaluators rate the search results again. Doing this iteratively helps tweak the results to better and better over time.<\/p>\n

The various possible signals they are likely incorporating into a Quality Score also have complexity:<\/p>\n