Internal link strategy for SEO and AI search: orphan pages, anchors, structure, hubs and link suggestions · from Screaming Frog, Search Console or Ahrefs exports
Drop the exports here. The tool recognises each file by its columns and merges them. Everything is analysed in this browser: the files are not uploaded anywhere.
Menus and footers repeat the same links on every page; content links (descriptions, blog posts) say more about relevance. Needs the "Link Position" column of Screaming Frog; without it every link counts.
The health of the internal links in one view. Each check has a target from the SEO docs; the score is the weighted share of checks passed (A+ ≥ 90 … F < 50).
A page no internal link points to is orphan: bots only find it through the sitemap, so it gets little authority and is rarely cited by AI. 1–2 links = near-orphan. Only pages known from a page list (Screaming Frog Internal: All, sitemap, Index Tool or GSC) can show up here. The suggested sources are pages on the same topic with the most authority (PageRank) that do not link there yet.
Every link's anchor sorted into 8 types and compared with the target range (why). Image links use the alt text as anchor; a link with neither text nor alt is empty.
Diversity = different anchors ÷ links received. One exact-match anchor repeated many times looks over-optimised; many generic anchors tell bots nothing.
Links whose text says nothing about the target ("read more", "shop now", image without alt). Rewrite them with a describing anchor: the suggestion uses the target page's title.
How the site is wired (models): hub-spoke (each topic has a central page the others point to) is the clearest for AI; silo keeps links inside sections; flat has no centre; chain pushes pages deep.
PageRank computed from the internal links only: the share of the site's link authority each page receives. The home page should be #1, then the main collections, then products and blog posts (why).
Clicks from the home page following the links (Screaming Frog's crawl depth when available). Important pages should be within 3 clicks.
Pages grouped by topic (product line × room, from the URL and title, as in Keyword Research). For each topic the hub is the page with the most links from its own topic. A healthy cluster: every spoke links to the hub and the hub links to its spokes (topic clusters).
Concrete links to add: on the source page, link to the target with the suggested anchor (edit it to read naturally in the sentence). Strong pages lift weak ones; many small pages lift a key page (techniques). Priority: high = orphan or target page; medium = structure; low = anchor wording.
The suggestions grouped in four phases, the order that pays off fastest: quick fixes first, then structure, then anchor balance, then content and competitors.
The Visitor Log (last 30 days) joined with the link data: pages AI assistants read for people or for their search index, with the internal links each page gets. Pages AI reads but you barely link are easy wins (link them from your strong pages); pages you link a lot but AI never reads may be blocked or too deep.
Reads link exports, builds the link graph of the site and checks it against the internal link strategy of the SEO docs.
Each file is recognised by its columns: links (source, target, anchor, alt, position, follow) or pages (URL, title, H1, words, depth, inlinks). URLs are normalised (http/https, www, query, trailing slash).
Links between pages of the website only. Incoming links per page, click depth from the home page, PageRank (30 rounds, damping 0.85), topics from the words of URL and title.
Orphans, anchor types, link model, authority order, hub coverage, trust pages, then link suggestions and a phased plan.
Export the suggestions as CSV for the content team; run again after the changes (new crawl) to compare.