{"id":7618,"date":"2017-07-25T12:19:26","date_gmt":"2017-07-25T11:19:26","guid":{"rendered":"https:\/\/www.brainlabsdigital.com\/?p=7618"},"modified":"2025-06-20T18:54:28","modified_gmt":"2025-06-20T18:54:28","slug":"information-architecture-for-seo","status":"publish","type":"post","link":"https:\/\/www.brainlabsdigital.com\/information-architecture-for-seo\/","title":{"rendered":"Information Architecture for SEO"},"content":{"rendered":"\n

The SEO community really doesn\u2019t take much persuading when it comes to the importance of links<\/a>. Sometimes it feels like they\u2019re all we want to talk about.<\/p>\n\n\n\n

And yet, we spend hardly any time talking about the most important and easiest links of all – the ones on our own sites. The links in our templates, our footers, our faceted navs<\/a>, and our hideous drop down nested mega menus.<\/p>\n\n\n\n

Those links might affect your site\u2019s performance, at least as much as those you get from other sites. If you\u2019ve ever done log analysis<\/a> for large sites, you\u2019ll have seen how Google\u2019s understanding of a site is shaped by internal linking, and how unimportant pages can end up hogging the limelight. And this kind of issue isn\u2019t limited to large sites \u2013 in fact, smaller sites, which have less strength to play with, need to be even more careful about how they use it.<\/p>\n\n\n\n

When did you last review your information architecture?<\/h2>\n\n\n\n

Information architecture is a broad topic, which arguably includes almost everything that we traditionally call technical SEO, and a lot of UX. In this post, I\u2019m going to focus more narrowly on quickly identifying simple changes to a site\u2019s internal navigation that can boost the performance of your key landing pages. At its most basic, this is a process you could execute in half an hour.<\/p>\n\n\n\n

Here\u2019s what you can do.<\/p>\n\n\n\n

Step 1: You will need<\/h2>\n\n\n\n

The main ingredients of today\u2019s dish are all metrics at a URL level. Specifically, for any given URL, we need to know:<\/p>\n\n\n\n

  1. Level\/Depth: The number of clicks from the site\u2019s homepage that any given URL is.<\/li>
  2. Inlinks: The number of links from elsewhere on the site that a page has.<\/li>
  3. Linking metrics: Measures of how strongly linked to the page is in general, including external links.<\/li>
  4. Organic landings: The number of visitors who have landed on this page via organic search in a representative sample time period.<\/li>
  5. Pageviews minus Entrances: The number of times this page has been clicked through to from some other page on your site.<\/li><\/ol>\n\n\n\n

    Bonus\/Advanced:<\/p>\n\n\n\n

    1. Outlinks: The number of other pages on the site that a page links to.<\/li>
    2. Organic conversions\/revenue.<\/li>
    3. An estimate of internal PageRank flow (pinch your salts).<\/li><\/ol>\n\n\n\n

      Tools<\/h3>\n\n\n\n

      For items 1, 2, and 6 in the above list, I recommend Deepcrawl<\/a> (paid) or Screaming Frog<\/a> (you\u2019ll need a paid license and a good chunk of RAM<\/a> for sites of >500 pages).<\/p>\n\n\n\n

      For item 3, I recommend Majestic Historic Index Citation Flow for this sort of activity, not least because it can be easily sourced at a URL in bulk via their \u201cBulk Backlinks\u201d feature. Moz and Ahrefs equivalent metrics are perfectly viable. All of these tools have free versions, but a paid license makes things much easier at scale.<\/p>\n\n\n\n

      For items 4, 5, and 7, I recommend Google Analytics. To improve your data quality, you could try using the Model Comparison Tool (under Conversions > Attribution) or Google Search Console data to avoid having Google Analytics\u2019 default \u201cLast Non Direct\u201d model pollute your supposedly organic data.<\/p>\n\n\n\n

      For item 8, you could play around with something like igraph<\/a> (requires R) or NetworkX<\/a> (requires Python), which uses a Screaming Frog \u201call inlinks\u201d report, converted into a graph object, to build a map of the site and estimate the weight of internal pages. This gives a more sophisticated internal strength metric than just raw count of internal links, albeit with a significant extra investment of effort.Want more advice like this in your inbox? Join the monthly newsletter.<\/p>\n\n\n\n

      Step 2: Get it together<\/h2>\n\n\n\n

      For sites of up to around 100,000 URLs, it ought to be practical to pull this data together in Excel using Vlookups. If you\u2019re not sure how, check out this handy guide<\/a>.<\/p>\n\n\n\n

      \"gathering<\/figure>\n\n\n\n

      For larger, enterprise class sites, you have two options.<\/p>\n\n\n\n

      Option 1: Quick and easy<\/h3>\n\n\n\n

      If you\u2019re trying to turn this around in a 30-minute window, or even a few hours, your best bet will be trying to put together a sample of pages that includes most templates on the site, and is around 100,000 URLs in size. This shouldn\u2019t be too difficult, as most large sites generate the bulk of their size from a small number of \u201clistings\u201d templates, for locations, facets, or combinations of the above.<\/p>\n\n\n\n

      Option 2: Slow, advanced<\/h3>\n\n\n\n

      If you want to be thorough on a site with hundreds of thousands to hundreds of millions of URLs, you could scale up the process:<\/p>\n\n\n\n