A methodology page that says “powered by advanced AI and trusted sources” has mostly described a mood.
A useful one helps a reader decide whether the evidence is suitable for their question.
Explain the collection boundary
Name the source categories, stores, geographies and refresh approach. Distinguish direct observations, seller-reported figures, licensed estimates and your own calculations. Describe what is not covered, including payment channels or media sources that remain unavailable.
If collection is selective, explain the selection. A tracked-app cohort and a complete store census are different datasets.
Explain the transformations
Describe identity matching, deduplication, currency handling, time windows and missing-value treatment at a level a researcher can understand. You do not need to expose credentials or proprietary implementation details to explain the meaning of the output.
For a hypothetical estimate, identify the inputs and important limitations. Do not label a model validated merely because it produces plausible-looking values.
Explain how things go wrong
Sources can become stale, records can be mismatched and vendors can revise historical data. Tell readers how those situations are represented and how corrections are handled.
Provide a way to report a specific problem with an app identifier and source context. Preserve the original observation when a correction changes its interpretation.
Then make the relevant parts visible beside the data. A methodology page supports contextual labels; it should not be the only place where a user can discover that “revenue” means an estimate for one storefront.
Review the page when collection behavior changes. A transparent document that describes last year’s pipeline can become misleading despite its careful wording.
The embarrassing questions are usually the useful ones: how much is missing, how fresh is it, what is inferred and how would you know if the result were wrong? Answer those, and the page earns its place.