Scenario 1 — National cosmetics brand (e-commerce)
- SCAN: a 120-question set; competitor and source map across 6 engines. Most-cited sources: list articles and comparison sites.
- BUILD: move product information into HTML, Product/Organization schema, bot access checks, align entity data with marketplaces.
- WRITE: Q&A guides by skin type, ingredient comparison tables, accurate product details in independent reviews.
- MEASURE: citation share and recommendation position in category questions, AI-referred sessions and their conversion rate.
Scenario 2 — B2B HR software (SaaS)
- SCAN: a 60-question set; identify which sources (review platforms, list articles) comparison answers rely on.
- BUILD: SoftwareApplication/Organization schema, open pages explaining integrations and pricing logic, Bing Webmaster and IndexNow.
- WRITE: original-data content (e.g. an industry survey), honest comparison pages, up-to-date review platform profiles.
- MEASURE: citation share in comparison questions, Copilot citations (Bing AI Performance), “how did you hear about us?” on demo requests.
Scenario 3 — Three-city clinic chain (local)
- SCAN: 30 questions per city; local recommendation map in Gemini/AI Overviews and ChatGPT, list of wrong information.
- BUILD: LocalBusiness/Dentist schema per branch, name-address-phone aligned across Google Business Profile and directories.
- WRITE: Q&A pages along the patient journey (duration, stages, cost drivers), dentist profiles — in language compliant with health advertising rules.
- MEASURE: recommendation position in local questions, change in the number of wrong facts, AI-referred appointment requests.