How to create original research and statistics that AI can cite

Original research becomes a durable citation asset when the question matters, the data is lawful and fit for purpose, the method is reproducible and each statistic carries enough context to interpret correctly. Publish the methodology, definitions, sample boundaries, field dates and limitations beside the findings. Give the report a stable canonical URL and make key results available in accessible HTML, not only in charts or a gated download. The objective is useful evidence, not a dramatic number engineered for attention.

Choose a question your data can answer honestly

Begin with recurring questions from customers, practitioners and observed AI answers. ModelSaid can highlight prompt and topic gaps where assistants rely on stale statistics, cite weak summaries or lack evidence for an important decision. Turn one gap into a precise research question with a defined population, unit of analysis and period. Inventory the data you already collect and the permission under which it was gathered. Do not stretch product telemetry into claims about an entire industry, and do not commission a survey when the sampling frame cannot represent the conclusion in the headline.

• Define the population, sample, exclusions, geography and collection dates before analysis.
• Document whether records are people, companies, sessions, prompts or another unit.
• Separate descriptive observations from causal or predictive claims.
• Predefine important cuts so interesting-looking subgroups are not selected after the fact.
• Apply privacy, consent, security and retention requirements throughout the workflow.
• Ask a qualified reviewer to challenge definitions, calculations and interpretation.

Design every statistic as a self-contained claim

A number should travel with its denominator, population, timeframe, unit and relevant uncertainty. "Forty percent" is unusable without knowing forty percent of what and under which conditions. Keep a calculation notebook or query, a data dictionary and an immutable release snapshot so corrections are traceable. Use weighted results only when the weighting method and purpose are explained. Label survey responses as reported views, not behavior. If a sample is small or self-selected, state that prominently. Never create a precise figure from an anecdote, round in a way that changes meaning or imply external validation that did not occur.

Publish a source others can inspect and reuse

Create an answer-first findings page with a descriptive title, publication date, author and update policy. Pair each chart with a text summary, labels, source note and direct link to the methodology. Offer a downloadable table or suitably anonymized dataset when rights and privacy allow, and provide a citation format that names the original publisher rather than a republisher. Use the meta tag generator to describe the actual finding and the schema validator to check any Dataset, Article or organization markup against visible content. A PDF can supplement the page, but should not contain the only readable evidence.

1. Record baseline prompts that request the statistic, benchmark or decision evidence.
2. Note which sources assistants cite and whether their interpretation is accurate.
3. Use ModelSaid to draft brand-safe summaries constrained to approved findings and limitations.
4. Publish the report, methodology, source notes and correction channel together.
5. Rerun the stable prompts and monitor citation, wording and recency across assistants.
6. Version corrections transparently and preserve the definition used in every release.

Measure adoption without inflating impact

ModelSaid can compare before-and-after answers and monitor whether your research appears, is attributed correctly or loses a critical qualification. Track citations from publishers, links, qualified downloads and sales use separately; none alone proves business impact. A new AI citation after publication is not proof that a particular outreach action caused it. Start with the AI visibility scan, then keep the original prompt panel stable while placing emerging questions in an exploratory set. Refresh recurring studies on a declared schedule, archive superseded editions without breaking their URLs and correct errors visibly. Research earns lasting trust through transparent maintenance, not through a single headline statistic. Maintain a correction policy that distinguishes calculation errors, revised source data and later market change. Give each release its own date and version, keep prior methodology accessible, and notify known users when a correction materially changes the conclusion they may have cited. That transparency protects downstream readers.