Publish fake stats, get cited in LLMs

A search-grounded piece of LLM-written content has a tight budget for latency and cost. The system fetches a few pages, pulls the relevant passages, and starts to write. Fact-checking doesn’t happen anywhere in the process.

Verifying a single statistic would mean finding independent sources, comparing them, and resolving conflicts. Across billions of prompts by millions of users, the costs in both time and money would add up, making verification too slow and too expensive for practical purposes.

To get around this, LLMs use rerankers that score relevance, but not truth. That means if a passage matches the query and looks authoritative, it gets used.

Statistics have a built-in freshness that models like

I've written before about ChatGPT's recency bias in how it scores sources. Statistics pages contain the year in the title, the headings, and the URL. Three built-in signals to the model that what’s on the page is fresh.

The two tests I ran show that statistics pages are a very powerful way to get your content cited by LLMs. While I absolutely do not recommend coming up with bogus data, you can use the learnings from our small test to create your own pages with real data. Here's what worked for us at Peec and what I would do if I were making a statistics page again.

Make one claim per sentence. Each stat should make sense on its own, out of context. Retrieval doesn’t work on pages, but rather on passages. If a number needs the paragraph above it to make sense, it probably won't get pulled.

Put the source and year next to every number. "According to [Source] (2026), X%..." This helps the model, the human editor, and your credibility. It’s one small piece of information hygiene that benefits the broader information environment.

Match headings to fanout queries. The headings on your page should match the way LLMs search the internet: "[Topic] statistics," "[Topic] market size," "[Topic] adoption by country." Think in the queries models run, not just the keywords people type.

Show the update date (and actually update it). Freshness is a real signal that models take into account. A stale stats page will lose to a newer one.

Make the stats easy to fetch. Use server-rendered HTML instead of hiding data behind JavaScript, tabs, or images. Also, be sure to check your robots.txt and your CDN or WAF rules. Your site may be blocking AI retrieval bots without your knowing it.

Keep it simple. Our Peec AI GEO statistics page is basically a list. Yours doesn’t need to be anything more than that, either. Save design resources for sites and assets that can actually benefit from them.

Add original data when you can. Aggregated stats may get you a few citations, but when you say something no one else does, you can become the primary source.

Google Search Console won’t show you what’s going on with AI bots on your site. Here is what I track instead:

AI bot activity in your server logs or Cloudflare. Look for retrieval bots like ChatGPT-User, OAI-SearchBot, and PerplexityBot, not only training crawlers. We have this feature!

AI referral sessions in GA4 from chatgpt.com, perplexity.ai, and similar sources.

Citation and visibility tracking across a set of prompts. At Peec, we use our own tool for this, obviously.

New referring domains in Ahrefs or Semrush.

Unlinked mentions. Search your exact numbers in quotes. You will find people using your data without linking to you.

It should go without saying that you should never publish fake content. While my experiment relied on tens of thousands of unverified stats, I ran it in order to share the results with the public, and the website, while still live, now carries a disclaimer. When putting out content we hope gets picked up by LLMs, our job should always be to put out accurate, accessible information.

To add another caveat, there’s no guarantee this tactic will continue to work. We tested it, and these are the results, but now that they’re published, LLMs might close this gap. For now, it works, but in the future it may not, and of course it should never be abused.

And one final point: this is a tactic, and neither can nor should be the basis for your organization’s entire GEO strategy. The results I got with StateGlobe were extreme, but the brands most likely to benefit from using the technique are those people already trust.

At Peec, our research team is running close to 40 experiments like this one. Some are still in progress, and others are already showing results. Building a long-term strategy for your brand means testing, measuring, and sharing what we learn along the way.