Original Research and Proprietary Data: Become a Cited Source

Original research is any data that only you can publish: a customer survey, aggregated metrics from your product, a replicable test or a price benchmark. It tends to attract citations because it does not exist anywhere else, but no AI citation is guaranteed and no independent study proves causation. What works is a clear method, a useful number and a page that is easy to cite.

Quick summary

• In SEO, AEO and GEO, proprietary data is one of the few assets a competitor cannot copy with a prompt. Google talks about "non-commodity" content.
• You do not need a budget: surveys of your own audience, aggregated product data, tests and analyses of public data are enough.
• Credibility comes from the methodology: who asked, whom, when, how many responded and what the limits are.
• A small convenience sample counts as an indication, not as a "market study". Say so on the page.
• Publish the headline number right at the top, with a table, the methodology and a downloadable file, and offer a way to cite it.
• Citations come from distribution: press, communities and partners. The page alone is rarely enough.

How to read this: if you are a manager, read the data types, the step-by-step and the example. If you work in SEO or public relations, go to methodology, privacy, page format and distribution.

Why is proprietary data more likely to be cited?

Because it is new information. A text that reorganizes what already exists can be replaced by any other, and a number only you have cannot. The evidence on the effect in AI is indirect and has limits, so it is worth separating the pieces.

• Google. The guide to optimizing for generative AI says that unique, useful content with its own point of view tends to influence presence in generative search more than the guide's other suggestions, and contrasts "commodity" content, based on common knowledge, with "non-commodity" content, which brings its own perspective or experience.
• Academic research. In the study on Generative Engine Optimization (KDD 2024), adding statistics, expert quotations and sources to the content did the most to increase visibility in answers, on a benchmark created by the authors. The study's limits are described in how to structure citable content.
• Sources AI cites. Muck Rack's May 2026 report "What Is AI Reading?", released in a press release, analyzed more than 25 million links from ChatGPT, Claude and Gemini answers across 17 industries. According to the report, 84% of citations come from earned (unpaid) media, journalism accounts for 27% of sources and paid or advertising content for 0.3%. Muck Rack sells public relations and AI monitoring software, so a commercial interest exists, and we did not review the full methodology.

What this data suggests, taken together, is a two-step path: publish your own numbers and get reliable outlets and sites to pick them up. What it does not prove is that your research will be cited by a specific engine.

In plain English: think of a rumor and a certificate. A rumor can be repeated by anyone. A certificate exists only at a records office, and whoever needs it has to go there. Proprietary data makes you the records office for that number.

What types of proprietary data can you create on no budget?

Five types cover most businesses, and all of them use resources you already have: your customer base, your product, your time and public data. The table compares effort and the main risk of each.

Type	Example source	Effort	Main risk
Survey of customers or audience	Email list, community, in-product form	Low to medium	Biased sample and leading questions
Aggregated product data	Anonymized usage, times, volumes, patterns	Medium	Privacy and representativeness
Replicable test or experiment	Comparing methods, tools or timelines	Medium to high	Lack of control and of repetition
Analysis of public data	Open data, official reports, market prices	Medium	Collection and interpretation errors
Data from your own site	Search Console, analytics, content test results	Low	Generalizing from one site to the market

If you use Contentor, the Results page gathers Google Search Console data per project, which can serve as the basis for an analysis of your own site. It shows your case, not the market, so label it that way.

How do you create a study in seven steps?

Follow a short sequence and write down the decisions before collecting. The most common mistake is collecting first and discovering the question afterward, which leads to hunting for a number that "makes a headline".

1. Choose the question. It should be something that journalists, customers and AI need answered and that still has no reliable number. "How long does a clinic take to reply to a patient's appointment request?" is better than "What do people think about marketing?".
2. Define the population. Whom do you want to describe: customers, professionals in an industry, product users? Write down the inclusion rules.
3. Plan the collection. A form, a short interview or data extraction. Word the questions neutrally, test with five people and fix the dates.
4. Collect with consent. Explain how the data will be used and do not ask for personal data you do not need.
5. Clean and analyze. Remove duplicates and invalid answers, record what was removed and calculate percentages and medians before choosing the headline sentence.
6. Choose the headline number and the limits. The number must be true, clear and accompanied by what it does not let you conclude.
7. Publish with the methodology and prepare the distribution (see the two sections below).

For SEO professionals: use Search Console and analytics to choose the question. Queries with many impressions and no page with a proprietary number are candidates. Pick questions where a real number answers what the SERP today answers with opinion.

What methodology protects credibility?

Disclose enough for someone else to assess and, if they want, repeat the study. The American Association for Public Opinion Research (AAPOR), a reference in opinion research, lists in its disclosure standards what must be reported when publishing results.

• Who sponsored and who conducted the research.
• The exact wording of the questions.
• The population studied and how the sample was generated and recruited (probability or not).
• The mode and dates of collection.
• The sample size.
• The weighting procedures, if any.
• The quality controls and the limitations of the design.

You do not need a research institute to follow this. You need an honest "Methodology" section.

In plain English: if a cook says the dish is the best in town because three friends liked it, nobody takes it seriously. If the cook says "three friends liked it", that is true and useful information. The methodology is the difference between the two sentences.

For SEO professionals: a margin of error only applies to probability samples. With a random draw of 200 respondents and 50% answering a given way, the approximate margin is 1.96 × √(0.5 × 0.5 / 200), about 6.9 percentage points, and with 1,000 respondents it falls to about 3.1. In a convenience sample (your email list), that calculation does not apply, and publishing a "3% margin of error" would be misleading. Say "convenience sample, not representative" and show the raw counts. If a question gets fewer than 30 valid answers, avoid percentages and show the absolute number.

How do you handle privacy when using product data?

Use aggregated, anonymized data, and confirm the legal basis. Brazil's General Data Protection Law (Law 13,709/2018, the LGPD) provides, in article 12, that anonymized data is not considered personal data, unless the anonymization process can be reversed with its own means or with reasonable effort. Your data may fall under a different law depending on where your customers are, so check the one that applies to you. In practice, avoid small groups that could identify a person or company, do not publish a specific customer's data without permission and review your terms of use. When in doubt, consult a lawyer.

Hypothetical example: a fictional scheduling platform for clinics decides to study the interval between an appointment request and the reply. It uses only aggregated data from customers who accepted the analysis, excludes clinics with fewer than 30 requests in the period, and publishes the median by type of specialty, with no customer names. The page carries a headline such as "In [period], clinics reply to appointment requests in [X] hours, at the median", followed by the number of clinics and requests included, the exclusions, a table by specialty and a notice that the results describe the platform's customers, not every clinic in the country. The [X] values are placeholders, and the example shows the format, not a result.

How do you publish so the research is citable?

Put the headline number in the first paragraph and organize the rest for people who want to verify it. People and machines cite what they find quickly and what they can check.

1. Title and opening with the data. The first sentence carries the number, the period and the population.
2. A summary of 4 to 6 findings in a list, each with a number and its context.
3. HTML tables with the raw numbers and counts (not just charts as images). Charts need a text caption.
4. A full methodology section, with the questions, dates, sample and limits.
5. A downloadable file (CSV) of the aggregated data, when possible.
6. Authorship and date visible, with the responsible person identified. See E-E-A-T and trust signals.
7. A ready-made citation line, for example: "Source: [study name], [company], [month/year], [URL]".
8. A stable URL and periodic updates. Repeating the study every year builds a historical series that only you have.

These elements follow the same principle as citable content structure: passages that stand on their own. Other sites may summarize your numbers. To keep your original page as the most complete reference, include what a summary does not carry: the method, the tables and the file.

How do you turn data into links, mentions and citations?

Distribute it to people who have a reason to use the number: journalists, industry associations, niche blogs and communities where the subject is discussed. Outside pickup is what makes the data a reference, and, according to the Muck Rack report, what brings you closer to the sources AI cites most.

• A short press pitch. One email with the headline number, the method in one line, a quote from a spokesperson and the page link. See the full method in Digital PR for link building.
• A linkable asset. Research is one of the formats that attract links on merit. Compare others in linkable assets and linkbait.
• Communities. Share findings and answer questions where the audience already is, without disguised promotion. Understand the role of these platforms in Reddit, YouTube and Quora in AI citations.

Google warns that chasing inauthentic "mentions" is not as useful as it seems, and that ranking systems prioritize quality content. The pickup that counts is the kind that happens because the data is useful, not the kind that is bought or manufactured.

What pitfalls should you avoid?

The most common pitfalls destroy trust all at once, and research with an inflated number is worse than no research.

• A "study" of 30 respondents presented as a portrait of the market. Call it a survey and state the size.
• Leading questions ("do you agree that service X is the best?").
• Choosing only the cut that favors the brand. Also publish what goes against your expectation.
• Confusing correlation with cause. "Customers who use X sell more" does not prove that X causes sales.
• AI-generated or "estimated" numbers with no label. An invented figure is a legal and reputational risk. If it is a projection, label it as one.

Frequently asked questions

Do I need a lot of people to run valid research?

There is no magic number. It depends on what you claim. A survey of 80 customers is fine for describing your customers, as long as you say so. To generalize to a market, you need a random, representative sample, which costs more. State the size and the limitation instead of hiding them.

Can I publish aggregated data from my product?

Generally yes, as long as it is aggregated, anonymized and allowed by your terms of use and the legal basis. Brazil's LGPD provides that anonymized data is not personal data, unless the process can be reversed with reasonable effort. Avoid small groups and consult a lawyer if in doubt.

Does original research guarantee an AI citation?

No. There is no guarantee and no independent study that proves causation. What exists is Google's advice to produce unique content, academic evidence that statistics and sources help visibility, and data showing that earned media dominates citations, according to a report from a company in the industry. Treat it as a well-founded bet.

How long does research take to generate links and mentions?

Nobody knows, and it varies a lot with the topic, the distribution channel and how relevant the number is. Some studies get picked up in days, others never do. Plan the distribution from the start and track links, mentions and visits for at least a few months before judging the result.

Can I use third-party public data and call it original research?

You can, if the analysis is yours and adds something the original data does not show, such as cross-tabulations, time series or comparisons. Cite the source and describe what you did. Republishing the same numbers without analysis is not original research, it is curation.

Next step

With the data in hand, the next step is getting it to the people who can cite it: read Digital PR for link building. If your audience reads Portuguese and you also want to strengthen the authority of the pages that receive this traffic, consider Contentor's backlinks on Brazilian, Portuguese-language sites, and keep in mind that Google considers links bought to manipulate rankings a violation of its policies.

Read also:

• Linkable assets and linkbait
• E-E-A-T in the AI era
• Reddit, YouTube and Quora in AI citations