Incentivized Review Markup: Google and FTC Audit
Audit experience, incentives, disclosures, visible-page parity and aggregate math with a 22-field review markup ledger.
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Follow structured, evidence-led guides for substantial SEO and AI-search tasks. Each guide defines the job, orders the work, shows the evidence, and ends with a completion point.
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Step-by-step help for substantial SEO and AI-search tasks.
Audit experience, incentives, disclosures, visible-page parity and aggregate math with a 22-field review markup ledger.
Use matched weekdays, one primary action, live-post checks, UTM discipline, and explicit confounders to compare Business Profile post and control windows without treating interaction counts as ranking proof.
French news publishers can use AI to accelerate document review, transcription, accessibility, quality control and format adaptation—while keeping human accountability and original reporting at the center. This guide maps the technical, editorial and measurement work required across Google News, Discover, AI answers and direct readership.
Audit testimonial provenance, disclosures, crawlability, structured-data limits, claim portability, and observed AI citations without promising influence.
Answer Engine Optimization (AEO) is the work of making content eligible, clear, verifiable, and measurable across systems that generate direct answers. This guide separates durable practices from citation and schema myths.
Separate business mentions, visible citations, click destinations, and profile cards; then classify source ownership and claim support without turning a sampled source mix into a ranking factor.
Verify employer, role identity, freshness, location eligibility, and the application destination before sharing personal data or treating a listing as active.
Compare Perplexity Router and Agent API by retrieval ownership, tool evidence, citations and fallback using a 24-field run ledger.
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