How do you evaluate whether a GEO program is working (beyond inquiry volume)? What 3 key dimensions should you track?

1) AI Mention / Recommendation Rate (AI Answer Share)

Definition: The percentage of target, procurement-intent questions where your company is mentioned or recommended by major AI answer engines.

• Engines to include: ChatGPT, Gemini, Deepseek, Perplexity (and any region/industry-specific AI tools you sell into).
• Query set: Build a fixed list of buyer questions aligned with the B2B decision path (spec/selection, compliance, delivery capability, risk control).
• What to record (minimum): date, engine, exact prompt, whether the brand/entity appears, position in the list (if applicable), and the cited sources/links.

Why it matters: GEO’s job is to increase the probability that AI systems choose your company as a recommended entity at the moment of intent.

2) Knowledge Sovereignty: Structured + Verifiable Enterprise Knowledge

Definition: How completely your core enterprise information has been converted into structured, machine-readable knowledge with verifiable evidence.

GEO is not only content publishing. It is knowledge infrastructure. AI systems reward information that is consistent, specific, and cross-verifiable.

• Structure: brand, products, applications, delivery capability, quality controls, transaction process, and industry viewpoints organized into clear entities/attributes.
• Atomic “knowledge slices”: FAQs, specs, process steps, constraints, definitions, and decision criteria broken down into quotable units.
• Verifiability: each critical claim should be attachable to evidence (e.g., certification ID, test method reference, documented process, or traceable records). If you cannot verify it, AI may down-rank it or avoid recommending it.

What improves: AI comprehension and confidence. This is the foundation for stable long-term recommendation, not a short-term traffic spike.

3) Authority & Trust Signal Accumulation: Entity Linking + Evidence Chain

Definition: The extent to which the broader web contains consistent, source-linked references that connect your company to the right topics, industries, and capabilities—forming an evidence chain AI systems can cite.

• Entity association: your brand/entity consistently linked with the same products, industries, and problem categories across channels (official site, social platforms, technical communities, and reputable media where applicable).
• Evidence chain: repeatable references that support key decision claims (capabilities, processes, delivery terms, quality system), reducing AI uncertainty.
• Semantic distribution outcome: after publishing and syndication, AI engines can retrieve multiple independent signals pointing to the same facts.

Why it matters: When multiple sources corroborate the same entity facts, AI systems are more likely to treat the company as trustworthy and “safe to recommend.”