Our comparison pages get tons of Google traffic but zero AI citations. What are we doing wrong?

I’ve analyzed 200+ comparison pages for AI citations. Your problem is common:

What gets Google rankings:

• Comprehensive narrative content
• Good keyword usage
• Strong backlinks
• Decent user experience

What gets AI citations:

• Extractable structured data
• Tables with clear headers
• Direct answers in first sentences
• Quick recommendation up front

Your comparison pages are probably structured like articles. They need to be structured like data sources.

Here’s the framework that works:

1. Quick Answer at Top First 50 words should answer: “Which is better and for whom?” Example: “HubSpot is better for marketing-focused teams with $45/month starting price. Salesforce is better for enterprise sales teams needing customization at $165/month.”

2. Comparison Table First Not buried in the middle. RIGHT after your quick answer: | Feature | Product A | Product B | | Price | $45/mo | $165/mo | | Best For | Marketing | Sales |

3. Clear Section Headings as Questions Not: “Pricing Analysis” But: “Which is more affordable: Product A or Product B?”

4. Direct Answers First Each section starts: “Product A costs $45/month vs Product B’s $165/month, making it 73% cheaper for entry-level plans.”

Your existing content is probably good. It just needs restructuring.

Technical angle: Schema markup is critical for comparison content.

AI systems parse schema to understand content structure. For comparison pages, implement:

ComparisonChart Schema:

{
  "@type": "ComparisonChart",
  "name": "Product A vs Product B",
  "itemCompared": [
    {"@type": "Product", "name": "Product A", "price": "$45"},
    {"@type": "Product", "name": "Product B", "price": "$165"}
  ]
}

Or at minimum, Table Schema: Mark up your comparison tables so AI systems know they’re structured data, not just HTML.

FAQ Schema: Add questions like “Which is better, A or B?” with direct answers.

In our testing, comparison pages with proper schema get cited 2.4x more than those without, even when the content is similar.

If you’re doing comparison content without schema, you’re leaving citations on the table.

Real example from a client:

Before restructuring: “Slack vs Teams: The Ultimate Comparison Guide”

• Ranking: #2 on Google
• AI citations: 0

Content was 3,200 words of narrative with buried comparison points.

After restructuring: Same content, reorganized:

• Added 75-word TL;DR with recommendation
• Created comparison table with 12 features
• Changed H2s to questions
• Put verdict first in each section
• Added FAQ schema

Results 45 days later:

• Ranking: Still #2 (no change)
• AI citations: 7 unique prompts citing it
• Time on page: Down 6%
• Conversions: Up 15%

What made the difference:

The table. AI systems LOVE pulling from tables. When someone asks Perplexity “Slack vs Teams pricing,” it can pull directly from a well-structured table. It can’t easily parse “Slack costs between $0-15 per user depending on the plan you choose, while Teams has different pricing structures…”

Since tables are so important, here’s what makes a comparison table AI-extractable:

Good table structure:

What makes this work:

• Clear headers
• Consistent data format per column
• Short cells (under 15 words)
• “Winner” column for each row
• “Best For” summary row

What breaks AI extraction:

• Merged cells
• Nested tables
• Icons/images instead of text
• Long paragraphs in cells
• Inconsistent data types

I’ve seen tables with checkmarks and X marks work great. Tables with custom icons or embedded images - AI ignores them entirely.

One thing that increased our citations: Being opinionated.

Our old comparison content tried to be neutral: “It depends on your needs.” AI doesn’t cite wishy-washy content.

Our new approach:

• “For small teams under 10 people, Product A wins.”
• “Product B is better if you need X feature.”
• “If budget is your priority, Product A saves 60%.”

We went from “balanced analysis” to “helpful recommendations.”

The key is being specific about use cases. “It depends” doesn’t get cited. “Product A is better for startups, Product B is better for enterprise” gets cited.

AI systems are trying to help users make decisions. Cite sources that make clear recommendations. Don’t be afraid to pick winners.

Data from analyzing 150 comparison pages:

Correlation with AI citations:

The insight:

Structure matters 2-3x more than traditional SEO signals for AI citations. A well-structured 1,500-word comparison with tables beats a 3,000-word narrative without structure.

My recommendation:

Prioritize: Table → Quick answer → Schema → Question headings Then worry about: Length, backlinks, traditional SEO

Pro tip: Look at what AI is already citing for your comparisons.

Before restructuring, I ask ChatGPT and Perplexity the exact comparisons we cover. I note:

• Which sources get cited
• What specific text gets pulled
• What format the cited content has

Usually you’ll see patterns:

• Tables get pulled
• “TL;DR” or “Quick Answer” sections get quoted
• FAQ responses get used verbatim

Then structure your content to match what AI is already extracting from others. You’re not guessing - you’re reverse-engineering what works.

I found that Perplexity especially loves citing from articles with clear “Our Verdict” sections. Started adding those to all comparisons. Citations increased.

This thread solved my problem. Here’s my action plan for our 15 comparison pages:

Immediate Restructuring (Each Page):

1. Add 50-75 word “Quick Answer” with specific recommendation at TOP
2. Create structured comparison table with clear headers and short cells
3. Add “Winner” column to tables
4. Add “Best For” row explaining ideal use cases
5. Change H2 headings to questions
6. Put verdict first in each section

Technical Implementation: 7. Add ComparisonChart or Table schema 8. Add FAQ schema for common questions 9. Keep total length similar, just reorganized

Testing Plan:

• Baseline current AI citations (Am I Cited)
• Restructure 5 pages first as test
• Measure after 30 days
• Roll out to remaining 10 pages

Key insight: Our content is good. The structure is wrong. AI needs tables and direct answers it can extract, not narratives it has to parse.

Thanks everyone for the specific, actionable advice!