Research is useful when the method is visible
Original research is information your organization collected, tested, analyzed, or observed and can explain honestly. It can make a page more useful because it contributes evidence beyond a summary of existing pages. It cannot guarantee an AI citation, a search result, or a conclusion broader than the sample supports.
Begin with a question that a reader can act on. Define the population, period, collection method, variables, exclusions, and analysis before looking for a headline. Ask an independent reviewer to identify likely bias or a missing comparison. The NIST AI Risk Management Framework is a helpful official reference for thinking about valid, reliable, and documented AI-related measurement, though it does not certify marketing studies.
Publish the evidence trail
State what was measured and what was not. Publish sample size, dates, recruitment or selection method, definitions, calculation steps, and material limitations. Link or provide a safe version of the underlying aggregate data where permissions allow. Do not expose personal data, customer URLs, credentials, or confidential logs merely to appear transparent.
For example, a fictional accessibility agency may test 40 volunteer ecommerce homepages in March using a named browser and a documented keyboard-flow checklist. Its article can report how many had an inaccessible menu in that sample, show the checklist, and say that this is not a prevalence estimate for all ecommerce sites. It should not claim that the result proves a ranking effect or that any particular platform is universally inaccessible.
Separate reported results from interpretation. “Twenty-two of 40 pages failed the menu check” is a result. “Teams should test navigation before launch” is a recommendation. “AI engines prefer keyboard-ready menus” is an unsupported leap unless a primary provider has documented it. Google asks whether content offers original information, research, or analysis and whether it makes readers trust the source; its people-first criteria support a method-first approach.
Make it maintainable
Give the study a permanent URL, author names, a published date, a review date, and a correction route. Preserve older methodology when a new edition changes it. Avoid quietly changing a chart after publication; add a correction note with the impact. Keep a source table that distinguishes primary measurements, external references, and quoted opinions.
Write an answer-first summary for busy readers, then include methodology before conclusions become too strong. Link to source citations and editorial trust for citation practice. Test that the research page is publicly reachable with the AI Search Readiness Checker, while remembering technical access does not establish citation selection.
Common mistakes
- Starting with a desired conclusion and selecting measurements afterward.
- Hiding sample selection, dates, or failed tests.
- Generalizing a small convenience sample to an entire industry.
- Presenting vendor marketing data as independent research.
FAQ
Must research include a large sample?
No. A small study can be useful when its limits are clear and its question is appropriately narrow.
Can AI help analyze results?
It can assist, but a responsible author must verify calculations, labels, and conclusions.
Continue with editorial trust, answer-first writing, and GEO.