Original Research Examples: What Great Original Research Looks Like
Great original research content takes a handful of recognizable formats — annual benchmark reports, proprietary surveys, analysis of data a company already owns, crowdsourced studies, expert panels, hyper-local market research, and interactive calculators — and executes each one with a genuinely surprising finding, transparent methodology, and design that makes the data easy to grasp at a glance. The format matters less than the execution; a mediocre survey and a brilliant one follow the same shape but produce wildly different results.
- The Annual Benchmark or "State of the Industry" Report
- The Proprietary Survey
- Analysis of Data the Company Already Owns
- Crowdsourced and Community-Contributed Studies
- Expert Panel and Prediction-Aggregation Research
- Hyper-Local and Market-Specific Original Research
- Interactive Tools and Calculators as Research
- The Anatomy of Great Original Research, Across Every Format
What follows is a tour of the formats that consistently work, illustrated with the kind of asset each typically produces. None are real case studies attributed to a specific company — they’re archetypes, patterns you’ll recognize once you start looking, because these shapes repeat across nearly every industry that does original research well.
The Annual Benchmark or "State of the Industry" Report
This is the format most people picture first: a recurring, dated report that establishes a company as the annual pulse-check for its industry. The “State of Marketing,” “State of Remote Work,” and similar franchises follow this pattern — survey or platform data aggregated into a single flagship report, released on a predictable schedule so the industry starts expecting it.
The mechanics that make this format work are less about any single year’s findings and more about the cadence. Published once, a benchmark report is a nice piece of content; published every year on the same data structure, it becomes a longitudinal asset — “search interest in X grew for the third consecutive year” is a headline only a repeating report can generate, and that year-over-year comparison is what press keeps coming back to cite.
The pitfall we see most often is treating this as a one-time push rather than a program. A benchmark report needs the same methodology preserved release to release — change the sample size, questions, or data source and you lose the ability to say “up from last year,” the single most citable sentence the format can produce.
The Proprietary Survey
Where a benchmark report blends multiple data sources, a proprietary survey is usually a single, focused study: a defined audience answering a defined set of questions, built to answer a narrower question a benchmark wouldn’t get specific enough to address. “How do small business owners actually choose a bookkeeper” is a proprietary-survey question; “the state of small business finance” is a benchmark-report question.
The strongest proprietary surveys we’ve helped clients run share a structural trait: they ask at least one question nobody else is asking. A survey that re-asks industry-standard questions produces industry-standard, forgettable findings. A survey that asks something slightly sideways — not “do you use SEO tools” but “how much of your marketing budget do you regret spending” — produces an unexpected number that gets quoted.
- Sample size transparency: The best proprietary surveys state respondent count and methodology up front — what separates a citable study from a marketing claim dressed up as research.
- A defined, named audience: “500 owners of businesses with 1-50 employees” is credible; “hundreds of business owners” invites skepticism.
- One headline finding, clearly stated: The best surveys lead with a single sentence a journalist could lift directly, not a data dump the reader has to mine for the story.
Analysis of Data the Company Already Owns
This is, in our view, the most underrated format — and often the cheapest, since the data collection cost is already sunk. Any company with a platform, product, or large customer base is sitting on aggregated, anonymized behavioral data nobody outside can see. Turning that into a research asset doesn’t require a survey; it requires asking the data a good question and packaging the answer clearly.
The archetypal version is the “we looked at X million of our own transactions/listings/searches and found Y” report — a payments company analyzing anonymized transaction volume to reveal seasonal spending patterns, or a job board analyzing its listing data to reveal which skills are rising fastest. The company isn’t asking anyone anything; it’s noticing a pattern already sitting in its own logs.
The pitfall is aggregation done badly. Anonymization has to be genuine and stated clearly, and the sample described accurately — “based on data from our platform” reads as vague, while “based on an anonymized sample of 40,000 listings” reads as research. This format ages well as an evergreen asset when the pattern is structural rather than a one-time blip, since the analysis can be refreshed on a schedule.
Crowdsourced and Community-Contributed Studies
Rather than surveying an audience once, this format invites an audience to contribute ongoing data points — a salary-transparency spreadsheet crowdsourced by workers, or a community-maintained pricing database for a service category, are the classic shapes. The research asset is the aggregate of many small, voluntary contributions rather than a single fielded survey.
What makes this format distinct is that the content and the community-building effort are the same activity. A crowdsourced study succeeds when contributing gives the contributor something back immediately — seeing how their number compares, or unlocking the aggregate data — which drives the participation that makes the dataset credible.
The risk is data quality: self-reported numbers are noisier than a controlled survey, and a credible version of this format has to be honest about that. Naming the collection method plainly — “self-reported by site visitors, unverified” — keeps it trustworthy instead of misleading.
Expert Panel and Prediction-Aggregation Research
Instead of surveying a large general audience, this format surveys a small panel of recognized experts — a Delphi-style approach, where a handful of named, credentialed practitioners are asked for predictions on where an industry is heading, and their aggregated, often individually attributed, answers become the research asset.
The credibility of this format comes almost entirely from who’s on the panel. A dozen genuinely respected, named practitioners produce a report worth citing; an anonymous or padded panel produces one nobody trusts. It’s also one of the few formats where contributors have a direct incentive to promote the finished piece themselves, since they’re quoted by name — a built-in distribution advantage.
Hyper-Local and Market-Specific Original Research
Most original research competes at a national or industry-wide level, which is exactly why local and regional research is underused and often disproportionately effective. A local brokerage analyzing its market’s price-per-square-foot trends by neighborhood, or a regional staffing agency publishing local wage-trend data nobody else compiled, faces essentially no competition — national research operations rarely go granular enough to cover it.
This is a format we push local and multi-location clients toward often, because the barrier to entry is genuinely low: a business with even modest customer data in a specific geography can produce a locally-specific study that local news outlets, and increasingly AI answer engines fielding location-specific queries, have almost nowhere else to source from.
Interactive Tools and Calculators as Research
The formats above all produce a static piece of content — a report someone reads. A calculator or interactive tool is a different shape entirely: instead of publishing one dataset, it lets each visitor generate their own data point, so the “research” is really a research engine rather than a report. A mortgage affordability calculator, a salary benchmarking tool, or an ROI calculator built on a company’s own data all fall here.
This format earns links and repeat visits differently than a report — a calculator gets bookmarked, embedded, and linked to as a utility rather than cited as a data point, and the aggregate data from thousands of users can itself seed a future benchmark report, closing the loop between formats.
The Anatomy of Great Original Research, Across Every Format
Whichever format is chosen, the assets that actually earn coverage, links, and AI citations share the same traits. Missing any one is usually why an otherwise well-intentioned research project underperforms.
- A genuinely surprising headline finding: Not “SEO is important” but a specific, counterintuitive number a reader wouldn’t have guessed — the single sentence that makes the whole project worth doing.
- Transparent, stated methodology: Sample size, collection method, and dates of collection stated plainly — what separates research from a marketing claim wearing a research costume.
- Strong data visualization: A chart that makes the finding obvious at a glance travels farther on social media and gets embedded in other publications’ coverage far more than a table of numbers ever will.
- Multi-format distribution: The best research assets aren’t published once as a single page — they’re broken into a press release, a series of social posts, and a full methodology page, so the finding reaches audiences who will never read the full report.
At Salterra, the research projects we’ve been proudest of since 2011 weren’t the ones with the biggest sample sizes — they were the ones where the client let the data say something they hadn’t planned on saying. That willingness to publish an inconvenient finding, rather than only the flattering ones, is consistently what separates research that earns citations from research that reads like a disguised sales pitch.
Frequently Asked Questions
Which original research format is easiest for a small business to start with?
Analysis of data the business already owns is usually the easiest entry point, since it requires no new data collection — just a clear question asked of records already being kept, packaged with honest methodology.
Do original research reports need to be updated every year to stay valuable?
Only benchmark-style reports depend on a recurring cadence for their value; a one-time proprietary survey or local market study can remain a useful evergreen citation without an annual refresh, though refreshing the data periodically extends its shelf life.
How large does a survey sample need to be to count as credible original research?
There's no universal minimum — credibility depends far more on stating the sample size and method honestly than on hitting a specific number, and a clearly described sample of a few hundred is often more trustworthy than a vague, larger one with no stated methodology.
Can a calculator or interactive tool really count as original research content?
Yes — a well-built calculator generates a genuinely useful, personalized data point for each visitor, and the aggregate usage data it collects can itself become source material for a future report.
What's the most common reason a research project ends up looking mediocre instead of great?
Almost always a missing or buried methodology combined with a headline finding that isn't actually surprising — without a clear reason to trust the number and a clear reason to care about it, even solid data reads as forgettable.
Is it better to run a new original research project or analyze existing data first?
Analyzing existing data first is usually the better sequence, since it's faster, cheaper, and tests whether the organization has a genuine research-worthy question before committing budget to a new survey or crowdsourced study.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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