Original Research & Proprietary Data: Your AEO Moat (5 Formats + Lightweight Methods)
An actionable guide to “original research SEO” and “proprietary data content.” Learn five research formats, quick-start methods, publishing checklists, and JSON-LD to earn citations in AI Overviews, Perplexity, and Copilot.
In the generative search era, numbers win quotes. Original research and proprietary data give answer engines something concrete to cite—metrics, rankings, deltas, and definitions only you can provide. This guide shows five research formats that reliably earn citations, plus lightweight ways to ship your first study in weeks.
New to AEO? Start with How AI Overviews Work, compare AI Search Optimization vs. Traditional SEO, structure topics with Topic Clusters, prioritize with ICE Scoring, brief writers using the AEO Content Brief Template, and add schema that moves the needle. Keep pages fast and crawlable with AEO Site Architecture, and tie clusters together with Internal Linking. For definitions, see the AEO Glossary.
Why Original Research Is an AEO Moat
- Unique facts → unique citations: Engines prefer verifiable, attributable data points. Your numbers become the quotable canon in your niche.
- E-E-A-T booster: Methods, authorship, and transparent limitations demonstrate expertise and trustworthiness.
- Compounding link equity: Studies attract organic links, which reinforce your pillar/cluster rankings over time.
| Format | What It Produces | Effort | Time to Ship | AEO Impact |
|---|---|---|---|---|
| Industry Pulse Survey | Stats, rankings, trend deltas | Medium | 3–5 weeks | High |
| Benchmark & Teardown | Feature matrices, scores, winners | Medium | 2–4 weeks | High |
| Telemetry Rollup | Aggregated usage trends | Low–Medium | 1–3 weeks | High |
| Pricing/Market Scan | Price indices, availability, volatility | Low–Medium | 1–2 weeks | Medium–High |
| Field Experiment / A/B | Causal uplift metrics | Medium–High | 4–8 weeks | High |
The 5 Research Formats (How to Ship Each)
1) Industry Pulse Survey
Run a short, 10–15 question survey to a targeted cohort (your list + partner communities). Publish topline stats, segment cuts, and a simple ranking. Share the instrument and sample size; disclose methodology and limitations. Pair with a downloadable CSV.
2) Benchmark & Teardown
Define a feature matrix and scoring rubric. Test 8–15 products or approaches, then publish scores, screenshots, and criteria. Keep the rubric reproducible (versioned) and note conflicts/disclosures.
3) Telemetry Rollup (Aggregated/Anonymized)
Aggregate usage metrics (e.g., adoption by feature, time-to-value) over a defined window. Only publish de-identified, rollup-level trends. Be explicit about privacy and sampling rules.
4) Pricing/Market Scan
Capture prices/availability across vendors geos/tiers on a single date or a short time window. Publish an index, min/median/max, and volatility notes. Avoid scraping that violates terms; prefer official catalogs, APIs, or manual sampling with citations.
5) Field Experiment / A/B
Test one clear intervention (e.g., reminder cadence) with a control group. Report uplift, confidence intervals when possible, and practical implications. Include a replication checklist so others can validate.
Lightweight Methods: Ship a Study in 2–3 Weeks
- Micro-survey: 100–300 responses via your newsletter + 1 partner list. 10 questions max; mix multiple choice and 1–2 open-ended for quotes.
- Mini-benchmark: Test 6 products on 10 criteria. Publish a transparent scoring sheet (weights + notes).
- Telemetry snapshot: Pick 2–3 KPIs, compare last 30 days vs prior 30 days; publish percent change with context.
- Price check: Sample 5 vendors × 3 tiers; publish medians and spread with date-stamped tables.
The Publishing Package (Maximize Citability)
- Answer-first summary: 60–80 word lead with 1–2 key stats. See Self-Contained Paragraphs.
- Downloadables: CSV of topline tables + methodology PDF. Host in
/public/downloads/. - Charts: Simple bar/line charts with labeled axes and source notes. Alt text + figure captions.
- Schema: Article/TechArticle + Dataset JSON-LD (see below). Use consistent names and dates.
- Link map: From pillar to study; from study to relevant cluster pages (e.g., glossary terms, how-tos).
Copy-Ready JSON-LD: Article + Dataset
Article
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "2025 Industry Pulse: AI Intake Adoption Benchmarks",
"author": { "@type": "Organization", "name": "Agenxus" },
"datePublished": "2025-09-08",
"dateModified": "2025-09-08",
"mainEntityOfPage": { "@type": "WebPage", "@id": "https://example.com/blog/original-research-proprietary-data-aeo-moat" }
}
</script>Dataset
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Dataset",
"name": "AI Intake Adoption Survey — Topline 2025",
"creator": { "@type": "Organization", "name": "Agenxus" },
"description": "Topline results, methodology, and codebook for the 2025 AI Intake Adoption survey.",
"license": "https://creativecommons.org/licenses/by/4.0/",
"distribution": [{
"@type": "DataDownload",
"encodingFormat": "text/csv",
"contentUrl": "https://example.com/downloads/ai-intake-adoption-2025-topline.csv"
}]
}
</script>Ethics, Privacy & QA (Don’t Skip)
- Aggregate/anonymize; no personal data in downloads. Respect platform terms for any public data collection.
- Disclose timeframes, sample size, response rate, exclusions, and any conflicts of interest.
- Version your dataset and figures; keep a changelog.
Measurement: Did It Work?
- Count third-party citations/links; archive with screenshots.
- Track referral traffic from answer engines and key publications.
- Monitor brand mentions and assisted conversions tied to the study.
Want a zero-to-study launch? Agenxus’s AI Search Optimization service runs research sprints, builds the dataset package (CSV + charts + JSON-LD), and integrates it into your pillar/cluster strategy.