This procedure defines separate countries, languages and question groups for AI monitoring. It is the English counterpart of the existing Polish market-configuration guide. It does not cover WordPress hreflang setup or rank countries by private report findings.
1. Separate country from language
A language does not identify a country. Record both, together with the business offer available in that market. State which settings your tool actually applies; do not imply precise user-location control when it is unavailable.
| Field | Complete |
|---|---|
| Market and question language | [separate values] |
| Offer and local competitors | [verified scope] |
| AI system and environment | [system; available settings] |
| Question-set version | [ID; intent groups] |
| Dates and observation cadence | [period; time zone; cadence] |
| Recognition and error rules | [classification; failed observations] |
Check the current service and plan coverage before adding a market. This guide does not promise all systems in every country.
2. Pair buyer intentions
Start with a common need and write a natural local question for each market. Preserve relevant constraints and the same stage of the buying decision. A literal translation can be a starting draft, but it requires local review.
Use separate groups for branded questions, unbranded category questions and needs specific to one market. Do not force a local requirement into a supposedly identical international sample.
3. Fictional configuration example
Create a Polish-language group for Poland and an English-language group for Great Britain. A shared intention is choosing an accounting service for a small business. Local legal terms and service eligibility may differ.
The example specifies a configuration, not actual demand or visibility. Its questions must be checked against the real offer. Compare equivalent intent groups where justified, and report local-only questions separately.
4. Keep the measurement record
Record question wording, settings, dates, valid answers and collection failures. Repeated observations can help assess variation, but no universal run count guarantees a stable result.
If a system, language, market or recognition rule changes, identify the boundary. Separate unavailable measurements from valid answers in which the brand is absent. Do not silently turn missing data into zeros.
5. Compare like with like
First examine change over time within each market under consistent conditions. For cross-market comparisons, explain sample differences, systems, branded-question proportions and metric definitions. A percentage does not automatically have the same meaning across unlike samples.
There is no universal normalisation that makes arbitrary market results equivalent. Keep mentions, recommendations and citations separate. Assign each confirmed content issue to an existing local URL before commissioning a new page.
