How Can I Check Whether ChatGPT Attributes My Original Statistics to the Right Business?

Why does attribution go wrong?

• Roundup pages copy statistics. "100+ [topic] statistics" pages collect figures from many studies, rank well and are cited in their place, often with the original credited loosely or not at all.
• News coverage without links. An article that reports "a recent survey found 68%..." becomes the cited source, and your name drops out.
• The number is hard to read on your site. Statistics shown only in chart images or infographics are hard for AI systems to read and quote accurately: most assistants work from page text, and Google relies on alt text, computer vision and the surrounding page. Figures behind a form are usually out of crawlers' reach altogether, and figures buried deep in a PDF or slide deck are harder to find and quote than a roundup page's plain sentence.
• Your sentence does not carry your name. "68% of respondents said..." on your page, with the brand only in the header, is easy to lift without credit.
• Similar figures from others. If a competitor published 65% on the same question, assistants may merge the two.
• Links without names. A Semrush and Growth Memo study of AI answers found that in 61.7% of domain appearances, a site was cited as a source without its brand being named in the answer. Being linked is not the same as being credited.

Which questions should you ask?

For each statistic you want credit for, write four kinds of question:

Run each several times in ChatGPT with and without web search, and in other assistants your audience uses.

How do you score each answer?

Use a simple outcome scale:

Then calculate an attribution rate: answers that use your statistic and credit you, divided by all answers that use your statistic. Track it per statistic and overall. Checking whether ChatGPT recommends you more broadly is covered in how to check whether ChatGPT recommends your SaaS or business.

How do you find where credit is being lost?

For each answer that credits someone else or no one, open the cited sources. Look for:

• A roundup page listing your statistic. Check whether it credits and links you.
• An article that reported your finding. Check whether it names you and links your page.
• A page with a similar but different figure, which the assistant merged with yours.
• No source at all, which points to model memory, where the figure may have been learned from a page that did not credit you.

Keep a list of the pages that most often take your credit. They are your outreach list.

How do you make statistics harder to misattribute?

• Put every key figure in a plain sentence with your name and the year: "[Brand]'s 2026 survey of 1,200 US small retailers found that 68% use AI for inventory planning." That sentence can be lifted whole without losing its origin.
• Publish the findings as an HTML page, not only as a PDF or a gated report. Keep the download for the full report, but make the headline findings open.
• Add a methodology section: who was surveyed, how many, when and how. Assistants and journalists treat statistics with a stated method as more credible.
• Give each key finding its own heading or anchor, so it can be linked directly.
• Add a short "how to cite this research" note with the exact credit line and URL.
• Keep the page at a stable URL, and update it with new editions instead of creating a new page each year.
• Contact the pages that take your credit. Ask roundup sites and publishers to name and link the original. Most will, because accurate sourcing makes their pages more trustworthy.

Statistics are worth the effort. In the original research on generative engine optimisation, adding statistics to a page was among the methods that most improved how visible its content was in AI-generated answers.

How often should you check?

Monthly for your most important statistics, and after each new edition of your research. Citations move quickly: Scrunch's analysis of 3.5 million AI citation events (September 2025 to March 2026) found that half of the pages cited in answers had dropped out of those answers within about four and a half weeks. A statistic credited correctly this month can lose its credit next month if a new roundup page outranks your source. How related brand mentions build up over time is covered in how co-citation and co-occurrence affect AI recommendations.

What might an illustrative case look like?

Consider an illustrative logistics software company that published a survey finding that 41% of mid-sized shippers had missed a delivery deadline because of address errors. Three months later, ChatGPT used the 41% figure in most answers about address errors but credited a trade magazine in half of them and no one in most of the rest. The company's own page showed the figure only in a chart image, and the full report sat behind a form. The company added an open findings page, with each statistic in a sentence carrying its name and the year, and a methodology section. It also asked the magazine and two roundup sites to link the original, which they did. Its attribution rate rose from 12% to 55% over two months.

What do people ask about AI and statistics attribution?

Should we gate our research reports?

Gate the full report if you need leads, but publish the headline findings openly. Gated findings are the ones most often credited to someone else.

Does it matter if the assistant links to us without naming us?

Yes. A link may bring some clicks, but readers remember the name in the answer. Aim for both.

What if a competitor publishes a similar statistic?

Make yours distinct: exact wording, population, sample size and year. Specific statistics are harder to merge with someone else's.