Insights · Field note · Growth and AI visibility

How to structure a comparison page engines will quote

Structured comparisons are the format answer engines lift most often in commercial queries. The structure is the point.

A comparison page that engines quote opens with a criteria table in the first screen, names the options as column headings and the criteria as rows, explains each criterion in its own section, and ends with a recommendation that states who each option suits. It is signed by an expert able to defend the recommendation, verified where it names third parties, and marked up as an Article. The table is what gets extracted; the recommendation is what makes it useful to a buyer.

In detail

What the evidence shows.

Table first

Retrieval systems extract structure readily: a table with clear headings maps directly to the comparison the user asked for. Placing it in the first screen, before the narrative, means the extractable unit is the first thing the engine encounters and the first thing a buyer sees. Our comparison pages put the table immediately under the answer block.

Criteria that decide

Choose the six or so criteria a buyer would actually use to decide, not the features a vendor would list. Each criterion gets a short cell per option and then its own section explaining what it means and why it matters. The sections are where the expertise shows and where sub-questions get answered.

A recommendation, not a shrug

A comparison that ends with “it depends” is not useful to a buyer or an engine. State who each option suits and why. Where the honest answer is both, say so and say in what order. Sign it with the expert who will stand behind it.

Verification and markup

Comparisons that name third parties carry legal exposure; verify every claim and route to counsel where needed. Approach comparisons are often safer and more useful than vendor comparisons. Mark the page up as an Article with the options as the subject. See the comparison hub for thirty pages built this way, and comparison and decision content.

Sources

Primary references.

  1. XFunnel, April 2025. Analysis of 768,000 AI citations by content type.
  2. Performance Data editorial standards

By David Selakovic · Published 2026-10-16 · Revised 2026-10-16 · Reviewed every 180 days

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