Choose a question your data can answer
Begin with an operational question supported by information the business already collects lawfully. A small audit dataset can describe common technical failures in that sample. It cannot prove how every website behaves.
- Write the population, period, unit of analysis, and inclusion rules.
- Decide whether the output is descriptive or intended for inference.
- Exclude fields that are not needed for the stated question.
- Obtain permission before using client-derived information.
Clean and anonymize before analysis
Remove direct identifiers, separate secrets and account data, normalize categories, and inspect rare combinations that could identify a participant indirectly. Aggregation is not automatically anonymous when a segment contains one unusual client.
- Use stable internal IDs that are never published.
- Suppress small groups and unusual combinations.
- Keep the identity key outside the analysis dataset.
- Document exclusions and missing data.
Publish the method beside the result
Readers should be able to understand how observations were collected, which tools and definitions were used, and what changed between reports. A chart without a method may look authoritative while remaining impossible to evaluate.
- Publish definitions for every reported metric.
- Show raw sample sizes beside percentages.
- Separate client sites, pages, scans, and prompt runs.
- State limitations and likely sources of bias.
Turn findings into useful examples
Use the dataset to explain patterns, not merely to create a press release. Pair aggregate observations with anonymized worked examples, corrective steps, and questions for future research.
- Explain what a practitioner should inspect next.
- Distinguish correlation, sequence, and causation.
- Retain results that do not support the original expectation.
- Provide a versioned download when publication rights permit it.
Official references
- Google: Optimizing for generative AI features: Google explains RAG, query fan-out, measurement, agentic experiences, and why ordinary SEO fundamentals still apply.
- Google: Creating helpful, reliable, people-first content: Google provides questions for evaluating experience, expertise, sourcing, authorship, and audience value.
FAQ
How large must a dataset be for SEO content?
There is no universal minimum. Report the sample honestly, avoid unsupported population claims, and suppress segments too small for privacy or meaningful interpretation.
Is removing domain names enough to anonymize client data?
No. Industry, location, timing, traffic, errors, and quoted text can combine to identify a client. Anonymization requires a re-identification review.
Should raw client data be published?
Usually not. Publish aggregate or carefully reviewed de-identified data only when contracts, consent, security, and privacy requirements allow it.
Keep the Next Step Small
Use the related guides to confirm what the page needs. Ask for support only when the change reaches code, templates, or server settings you do not want to guess at.
Contact Your SEO Wizard