Original research
Data you generated yourself, through surveys, product data or testing, published as content nobody else can copy.
In depth
What it really means
Original research is data you produced: a survey of your market, an analysis of anonymized product data, a benchmark study, or a documented experiment. The output is a statistic that did not exist until you published it.
It is the most reliable link-earning asset in content marketing and the most durable form of information gain, for a simple reason: a competitor can rewrite your guide in a week and cannot rewrite your data at all.
Why it works twice
The GEO research from KDD 2024 found that adding statistics and citations to content lifted visibility in AI-generated answers by up to 40%. Original research means you are the citation. Every competitor who quotes your number reinforces your association with the topic in exactly the way models pick up on.
Four types, by cost
| Type | Cost | Link potential |
|---|---|---|
| Product data analysis | Low, if you have the data | High |
| Documented experiment or teardown | Low to medium | Medium |
| Industry survey | Medium to high | High |
| Annual benchmark report | High, and recurring | Highest, compounds yearly |
Product data analysis is the underused one. Most SaaS companies are sitting on a defensible dataset and have never looked at it as content.
Pros & cons
Pros
- Earns links passively for years, since people cite statistics.
- Cannot be copied, which makes it real moat material.
- Directly drives AI citations.
- Opens PR and podcast opportunities that content alone does not.
- Annual editions compound, since each year adds trend data.
Cons
- Expensive and slow relative to writing.
- Requires either a dataset or a reachable audience, and small companies often have neither.
- Methodology has to hold up, since a flawed sample gets picked apart publicly.
- Ages. Last year’s benchmark stops getting cited once a newer one exists.
The mistake people make
Burying the number. Teams commission research, then publish a 4,000-word report where the headline statistic appears on page three. Journalists, bloggers and AI models all need the number stated plainly, near the top, in a sentence that can be quoted whole. Write the citable sentence first and build the report around it.
Best practices
- Start with your own product data, which is the cheapest defensible dataset available.
- State the headline statistic in the first hundred words, in a quotable sentence.
- Publish the methodology and sample size openly.
- Make the data easy to cite: clear charts, a downloadable dataset, an embed code.
- Pitch it to journalists and newsletters before it goes live.
- Repeat annually, so the trend line becomes the asset.
FAQs
What counts as original research?
Any data you generated: surveys, product data analysis, benchmark studies, documented experiments.
Do I need a large sample?
A few hundred respondents is enough to be quotable in most B2B categories, provided the methodology is stated honestly.
What is the cheapest version?
Analysing your own anonymized product data. The dataset already exists and nobody else has it.
How does it help AI visibility?
You become the citation. Statistics and citations were among the strongest measured drivers of generative visibility in the GEO study.
How often should I publish research?
Annually for a flagship benchmark, plus smaller data pieces through the year.
Keep reading
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