Research
Original SEO reporting research
This research section publishes the evidence behind practical SEO reporting decisions. Each study includes its sample, coding rules, limitations, source URLs, and a downloadable dataset so an agency or independent researcher can inspect the result instead of trusting an unsupported summary.
The first benchmark reviews public SEO report-template pages for access friction and client-workflow fields. It asks a narrow, reproducible question: what does each publisher explicitly promise on the public page before a user signs up, starts a trial, or opens the resource?
Everything in research
Using research in client work
A useful first-party data asset is different from a conventional blog post. The article explains why a result matters, but the dataset carries the durable value: named rows, explicit columns, a date, and a method another person can challenge. For this benchmark, a feature receives a yes only when the publisher's public page says it is present. We do not infer that a private dashboard has an approval workflow, owner field, confidence note, or AI-search view simply because the broader product might support one. That conservative rule makes the table less flattering but more reproducible.
The sample is designed around one real client-reporting decision: whether a resource only displays performance or also helps a team explain work, assign a next action, and surface uncertainty. KPI coverage is common, so it is not enough to distinguish a useful report. The more revealing columns are work completed, owner or due date, approval workflow, source and reporting-period labels, and confidence or caveat notes. These fields turn a visual summary into a decision record that can survive a client call and the next reporting cycle.
Access friction is recorded separately from report quality. A direct download is easier to inspect than a trial, publisher account, or Google-account copy flow, but that does not prove the underlying resource is better. The access field only describes what a visitor must do before reaching the reusable artifact. Likewise, a no in the feature matrix means not publicly documented, not definitely absent inside the product. Read the evidence note and original source before using one row to make a purchasing or vendor-selection decision.
You can reuse the CSV for analysis, charts, or a reporting-methods discussion. Keep the publication date and sample size attached to any aggregate, link to the original publisher when discussing one resource, and link back to this benchmark when citing the compiled findings. If a source page changes, preserve this dated edition as a historical snapshot and code the changed page into a new edition rather than silently overwriting the old observation.
FAQ
Research FAQ
What counts as original research here?
A study must define a sample, publish a repeatable coding rule, link every source, disclose limitations, and provide the underlying rows in a downloadable format. A list of opinions without inspectable evidence does not qualify.
Does a no mean the product lacks that feature?
No. It means the reviewed public page did not explicitly describe the feature under the benchmark's conservative coding rule. A feature may exist after login or elsewhere in the product, which is why every row includes its source and evidence note.
Why measure access friction separately?
Access affects whether a researcher or consultant can inspect and reuse the artifact, but it is not a quality score. Separating access from structure prevents a free download from automatically outranking a stronger paid workflow.
Can I publish charts made from the CSV?
Yes. Preserve the study date and sample size, describe the public-page scope accurately, and link to this research page for the method and complete dataset. Link to the original publisher when discussing a specific row.
How are corrections handled?
Clear factual errors can be corrected with a dated note. Normal product or landing-page changes belong in a new benchmark edition so the original observation remains auditable instead of being silently rewritten.