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You do not need a large research department to publish something useful. A focused benchmark, a small survey or a carefully documented comparison can answer a question your customers keep asking.
The value comes from new information that a reader can understand and inspect. If the work is useful, other writers have a reason to reference it. Whether an AI answer eventually cites it remains something to observe, not a result you can guarantee.
Choose a question you can answer directly
Start with a question that falls within your experience and access. A booking company might examine which booking steps its users find confusing. An agency might compare public content delivery across a defined collection of websites. A software team might benchmark a repeatable task using several documented configurations.
Choose something narrow enough to complete well. “The state of AI visibility” requires a much larger research design than “Which public pages were cited for these 20 booking questions during this collection period?”
Google's content guidance asks creators to contribute original information and analysis. It is a useful editorial standard, not a promise that a research label improves rankings or citations. Google Search Central
Write the question before collecting data. Otherwise, it is easy to keep searching until you find a dramatic result and present it as the original purpose of the study.
Pick a method that matches the claim
Different methods support different statements:
| Method | A claim it can support |
|---|---|
| Customer survey | What the responding customers reported |
| Public website audit | What the tested pages returned under recorded conditions |
| Product benchmark | How selected configurations performed in a defined test |
| Support-ticket review | Which topics appeared in the tickets you classified |
| AI answer collection | What appeared in the specific completed observations |
A survey of your customers does not represent every company in the market. A test of five handpicked tools does not establish the best product for all users. Put the scope in the title or opening paragraph so readers can judge the result properly.
For customer material, use data you are authorized to analyze and publish. Aggregate results and remove identifying details where appropriate. A research idea should not depend on exposing a customer's private question or business information.
Define the counting rules in advance
Write down what counts as one observation, which records are eligible and how missing data will be treated.
For an illustrative citation study, one unit might be one completed answer to a specified question on a specified provider. A cited domain could count once per answer even if several of its URLs appear. Failed requests would be reported separately.
If a question produces an answer with no citations, decide whether that remains in the denominator. Usually it should remain when your metric concerns all completed answers, but the key is to state and consistently apply the rule.
Have someone review a small sample of classifications. If two people interpret “recommendation” differently, clarify the definition before processing the rest. Record any changes to the rules instead of silently rewriting the method after seeing the outcome.
Keep enough evidence to reproduce the result
Preserve the collection dates, tools, settings and source records needed to understand the calculation. Save raw observations privately when they cannot be shared publicly.
Publish an appropriately anonymized or reduced dataset when feasible. Explain exclusions. If a page failed to load, show that as a failed observation rather than assuming the desired content was missing.
Suppose a demonstration dataset contains 60 completed answers and 12 cite the same domain. The share is 20% of those completed answers. It is not 20% of the assistant's users or 20% of all searches about the subject. This is an illustrative calculation, not a Prerender Buddy study result.
A reader should be able to trace each headline number back to its numerator, denominator and scope.
Publish a page that stands on its own
Open with the question and the main finding. Explain the method near the result, then show the supporting table or figure and discuss what the result means for the reader's decision.
Put essential numbers and labels in page text or an accessible table. A chart can help someone notice a pattern, but it should not be the only place the result exists. Link directly to the method, source material and downloadable data when available.
Include the author's real identity or the site's established editorial attribution, the collection period and the publication date. Explain any relationship to the products being compared. If the study concerns your own tool, make that relationship explicit.
End with practical implications that follow from the evidence. A finding about missing price explanations can justify reviewing pricing pages. It cannot support a sweeping claim about the future of every search engine.
Share the specific finding and maintain it
Approach relevant writers with the question, the result and the method. Make the contribution easy to inspect without requiring them to create an account.
When the data changes, preserve the original collection period. Add a dated update or publish the next edition with a consistent method. If you discover an error, explain the correction and its effect on the conclusion.
You can use questions and sources observed in Prerender Buddy AI Visibility to choose a research topic. The research itself should add evidence that did not exist before.