SourceCited / Publish original data
Strategy · Original data
The surest way to get cited is to own the number. Originate a statistic — cheapest via Google, Bing and DuckDuckGo autocomplete harvesting — and publish it as a real, downloadable, schema-marked dataset.
AI cites whoever originated a figure, so the strongest GEO play is to publish a statistic only you have. The cheapest way to generate one is autocomplete harvesting — counting the questions Google, Bing and DuckDuckGo suggest for your niche — then packaging it as a real HTML table with Dataset schema, a CSV under CC BY, and an honest method. Refresh quarterly to compound it.
Every AI answer that states a number needs a source for it — and the model cites whoever originated the figure, not the tenth blog to repeat it. So the highest-leverage move in GEO is to stop competing for consensus claims everyone else also makes and instead publish a statistic only you have. The founding Princeton GEO study measured this directly: adding statistics, quotations, and citations lifted visibility up to 40% — and when the stat is yours, every downstream mention of it is a citation you seeded. It is information gain in its most durable form: a fact the corpus can only get from you.
The AI-search question dataset is this play, run live: 3,307 questions we harvested and released so that any article about AI-search demand now has our number to cite. This guide is how to build your own.
You do not need a research team. Four repeatable formats produce citable numbers on a small-site budget:
| Format | What it produces | Cost |
|---|---|---|
| Demand harvest | What people actually ask, counted — from autocomplete (see below) | An afternoon and a script |
| First-hand test | You ran the tool / prompt / product and measured the result | Your time, honestly logged |
| Aggregate of your own logs | Anonymized patterns only you can see — crawler hits, query mixes, conversion by source | Data you already have |
| Small survey or teardown | You polled 50 practitioners, or audited 100 sites for one property | A few days, once |
What they share: the number does not exist until you publish it, so there is no incumbent to out-rank — only you to cite.
The cheapest of the four, and the one behind our dataset. Search autocomplete is a free, public read on real demand: the suggestions Google, Bing and DuckDuckGo serve are drawn from what people actually type. Harvest them at scale and you have a defensible demand dataset for your niche.
The same harvest does double duty: it is a citable dataset (this guide), and it is your content roadmap — the ranked questions tell you exactly which pages to write next. That is how the guides on this site were prioritized.
I want to build a citable demand dataset for my niche: [topic]. Help me: 1. Generate 60 seed phrases covering the head terms, question stems (how/what/why/best/vs/near me), and synonyms a real audience would type. 2. Give me a simple, polite plan to pull autocomplete suggestions for each seed from Google, Bing and DuckDuckGo (endpoints, rate-limiting, how to also expand seed + a-z). 3. Specify the output schema: question, appearance_count (seed x engine hits), engines_seen, cluster. 4. Propose 8-12 topic clusters my results will likely fall into. Then tell me the honest limitations I must state when I publish it.
A number in prose gets lifted; a dataset gets cited and re-shared. Package for both:
| Do | Why it earns citations |
|---|---|
| Lead with the headline figure, in text | The liftable, quotable claim engines extract |
| Show a real table, not an image of one | Engines and agents read HTML tables; they cannot read a chart JPEG |
Emit Dataset JSON-LD + a DataDownload | Makes it a machine-recognizable dataset with a fetchable file |
| State the method and limits plainly | Honesty is what makes a number safe to cite — and what survives scrutiny |
| Name the dataset after your brand | Turns every lift into a branded, ghost-citation-proof mention |
| Offer CSV/JSON under CC BY | Legal reuse requires attribution — you get linked when reused |
The question dataset does every row of this table — view its source if you want a template to copy.
A dataset you re-run each quarter is a citation engine, not a one-off. Each refresh is genuinely new data (so it earns a dated update and drops you inside ChatGPT's ~30-day freshness window), builds a time series competitors cannot retro-fit, and compounds: last quarter's figure is still cited while this quarter's is fresh. Version and date every release, keep the method identical so the numbers stay comparable, and never quietly overwrite — date-stamp instead.
Never fabricate or pad a figure to make it look bigger. A dataset is only an asset while it is trustworthy; one caught invention burns the credibility that made it citable, and the consensus engine is built to filter exactly that.
Give it something only you have. AI answers cite the originator of a figure, so publish original data — a demand harvest, a first-hand test, an aggregate of your own logs — as a real table with Dataset schema and a downloadable CSV under CC BY. Every article that later uses your number cites you.
Collecting the suggestions search engines serve as you type, at scale, to measure real demand. You expand a list of seed phrases through the public autocomplete of Google, Bing and DuckDuckGo, deduplicate while counting how often each suggestion appears, filter to your topic, and cluster — producing a citable demand dataset and a content roadmap at once.
It reads public suggestion endpoints, but be considerate: rate-limit your requests, don't hammer the services, and check each provider's current terms. Treat appearance counts as a directional demand signal, not official search volume, and say so when you publish.
Adding a sourced statistic helps, but you're still citing someone else — they get the durable credit. Originating the statistic makes you the source everyone else has to cite, which is a stronger and compounding position, especially if you refresh it on a schedule.