{"id":2301,"date":"2016-12-21T06:24:40","date_gmt":"2016-12-21T14:24:40","guid":{"rendered":"http:\/\/www.canirank.com\/blog\/?p=2301"},"modified":"2020-11-25T10:50:11","modified_gmt":"2020-11-25T18:50:11","slug":"search-ranking-factors-are-bs","status":"publish","type":"post","link":"http:\/\/www.canirank.com\/blog\/search-ranking-factors-are-bs\/","title":{"rendered":"Data-Driven SEO Part 1: Can You Trust Correlation Studies on Search Ranking Factors?"},"content":{"rendered":"

Search ranking factor correlation studies are bullshit<\/em>.<\/p>\n

In the past year, nearly every major SEO media outlet has published its<\/a> own<\/a> variation<\/a> of a search<\/a> ranking factor<\/a> correlation study<\/a>. They scrape huge numbers of search engine <\/span>ranking<\/span> results and calculate the correlation between Google rank and various SEO metrics such as the number of links, keyword density, or CTR.<\/span><\/p>\n

These correlations, the study authors claim, indicate the\u00a0relative importance of various search ranking factors in Google’s algorithm. A factor with a high correlation is more important\u00a0than a factor with a low correlation, and we should adjust our SEO accordingly.<\/p>\n

Unfortunately, it’s total bullshit. Such conclusions are unsupported speculation at best, and dangerously misleading at worst.<\/p>\n

In this post we’ll look at some of the reasons ranking factor correlation studies are not a useful way to measure the relative importance of search ranking factors<\/strong>, and show you some alternatives you might consider\u00a0instead.<\/p>\n

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Correlation studies on search ranking factors are BS. Here are some alternatives. #SEO <\/a><\/span>Click To Tweet<\/a><\/span><\/span><\/p>\n

Is the Data Science Revolution Leaving SEO Behind?<\/h2>\n

Today’s professional SEOs are largely intelligent and analytical marketers who understand the need to ground decisions in a thorough analysis of the data around what works and why. Yet the limited availability of accurate SEO data <\/span>for search engine rankings<\/span> prevents them from becoming more data-driven.\u201d<\/span>\u00a0Faulty studies and misinterpreted results just make things worse.<\/p>\n

As marketers in other areas embrace a new era\u00a0of data-driven performance<\/a> built on sophisticated machine learning algorithms and statistical analysis techniques, SEO risks being left behind.<\/p>\n

CanIRank\u2019s new series on data-driven SEO<\/strong> is our attempt to counteract this situation. We want marketers to gain a deeper and more rigorous understanding of the data underlying <\/span>search engine rankings and<\/span> SEO decisions<\/span>.<\/p>\n

This isn\u2019t for SEOs looking for \u201csimple,\u201d \u201ceasy,\u201d or dumbed down explanations.<\/p>\n

Likewise, it\u2019s not for marketers scared of\u00a0numbers or\u00a0statistics.<\/p>\n

Rather, these posts are for people who have always wanted to peek \u201cbehind the curtain\u201d at Google\u2019s algorithms <\/span>and how the algorithms influence various search ranking factors<\/span>.\u00a0<\/span> Founders who want to become five percent more efficient in their SEO work every month \u2014 and understand just how powerful that would be.<\/p>\n

We\u2019re putting modern SEO best practices under the microscope. You’ll discover which expert recommendations are justified, and which are not.<\/p>\n

I’ll call on my background as a data scientist<\/a>, as well as data from the most accurate ranking models<\/a> outside of Mountain View to help.<\/p>\n

Think of it as a bit like \u201cMythbusters for SEO.\u201d<\/em><\/p>\n

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Our Data-Driven SEO series has three principal objectives:<\/span><\/p>\n

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  1. Make you a better consumer of SEO studies (cultivate your BS detector)<\/span><\/li>\n
  2. Teach you analysis techniques you can apply in your own work (develop your inner data scientist)<\/span><\/li>\n
  3. Show you which SEO \u201cbest practices\u201d the data supports\u00a0so you can become a more productive SEO.<\/span><\/li>\n<\/ol>\n
    \n

     <\/p>\n

    In this first post of the series, we discuss some limitations of current SEO studies. Plus, we show how and why they\u2019re often misinterpreted.<\/p>\n

     <\/p>\n

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    In future posts we\u2019ll look at the data behind SEO questions such as:<\/span><\/p>\n