ChatGPT Comparison Table Extraction -Your Secret Weapon for "vs" Queries

Here's what ChatGPT actually sees when a user asks "Compare LiveChat vs Tidio vs Crisp" -it goes hunting for HTML tables. ChatGPT actively seeks comparison tables, extracts their structured data, and reformats it into a direct answer. This behavior is distinctive and consistent.

But the extraction depends entirely on table formatting. ChatGPT can parse tables that use proper HTML semantics: <table> with <thead> containing <th> column headers, <tbody> containing <tr> data rows with <td> cells. Tables built with CSS grid layouts, div-based faux tables, or image-based comparison charts? Invisible. They lack machine-readable structure.

ChatGPT evaluates three things: structural correctness (proper HTML table elements), data clarity (specific values, not vague statements), and completeness (does the table cover the comparison dimensions users care about?). A well-structured table comparing pricing, features, and integrations across three products is ChatGPT extraction gold.

Columns matter as much as rows. ChatGPT uses column headers to understand what each data point represents. "Price" is clear. "Plan Details" is ambiguous. Specific, descriptive column headers improve extraction accuracy. Row headers should clearly identify what's being compared.

Google AI Overviews also extract table data, but Google has richer context -surrounding text, schema markup, page authority. Google can sometimes make sense of poorly structured tables. ChatGPT relies more on the table's intrinsic HTML structure because it's working with raw page content retrieved through Bing.

Claude takes a different approach entirely. When asked to compare products, Claude writes an original comparison from information gathered across pages. It doesn't need a pre-built comparison table -it's more dependent on having comprehensive product info scattered across your site.

Perplexity extracts tables too, but its approach is closer to Google's -pulling from multiple sources and constructing its own comparison. Perplexity also shows inline citations, so even partial table data gets attributed. ChatGPT's table extraction is more binary: it either extracts your table successfully or ignores it entirely.

The culprit for ChatGPT: the browse-and-extract model. ChatGPT rewards sites that invest in well-structured comparison tables more directly than any other engine. Other engines can work around poor formatting by synthesizing from text. ChatGPT's approach gives a clear, measurable advantage to proper HTML tables.

Tidio (63) -uses comparison tables effectively. Their "Tidio vs Competitors" pages contain well-structured HTML tables: clear column headers (Feature, Tidio, Competitor A, Competitor B), specific data values (pricing per seat, Yes/No availability, integration counts), proper <thead>/<tbody> markup. These pages are among Tidio's most-retrieved content for comparison queries.

LiveChat (59) -had comparison tables on several pages, but we found structural issues. Some tables used CSS styling to create visual layouts without proper HTML <table> elements. They looked like tables in a browser. They were invisible to ChatGPT's extraction -just styled divs underneath. Correcting to proper HTML tables would immediately improve ChatGPT extraction.

HelpSquad (47) -almost no comparison tables anywhere on their site. In a crowded live chat support market, the absence of structured comparison content means ChatGPT has no on-site data to extract for "vs" queries. Instead, ChatGPT cites third-party review sites that have the tables HelpSquad doesn't.

Crisp (34) -pricing tables structured as div-based CSS grids. Looked fine visually. ChatGPT couldn't extract a single pricing data point. When users asked ChatGPT about Crisp's pricing tiers, ChatGPT either pulled from third-party sources (sometimes outdated) or couldn't provide specifics at all. Converting those CSS grids to semantic HTML tables would make Crisp's pricing instantly extractable.

Start here: view source (not Inspect Element) on every table on your site. Verify comparison tables use <table>, <thead>, <tbody>, <tr>, <th>, and <td> elements. If your tables are built with div grids, CSS flexbox, or custom components -convert them to semantic HTML. Visual appearance stays identical. Only the underlying HTML changes.

Write specific column headers using <th> elements. Replace "Details" and "Info" with "Monthly Price per Seat", "Free Plan Available", "Number of Integrations", "Customer Support Hours." ChatGPT uses these headers to understand what each cell means. Specific headers produce accurate extraction.

Use concrete values in cells. "Starting at $19/month" is extractable. "Affordable pricing" isn't. "200+ integrations" is specific. "Many integrations" is useless. "24/7 live support" is clear. "Always available" is ambiguous. ChatGPT extracts specific values. It discards vague ones.

Create dedicated comparison pages with tables for your top competitive matchups. If users ask ChatGPT to compare your product with specific competitors, build a page for each comparison. Cover pricing, features, integrations, support, and domain-specific criteria. Each comparison page becomes a high-value retrieval target.

Add a descriptive <caption> or H2/H3 heading above each table. State what it compares: "LiveChat vs Tidio: Feature and Pricing Comparison (2026)." That context helps ChatGPT understand the table's purpose and improves extraction accuracy. The caption also serves as a direct answer hook -ChatGPT may cite it alongside the extracted data.