The share of observed decision-intent answers in which an AI system includes a brand as a recommended option under a declared measurement protocol.
Definition: Recommendation Rate is the share of observed decision-intent answers in which an AI system includes a brand as a recommended option under a declared measurement protocol.
A decision-intent query asks for products, vendors, providers, or options suited to a stated need. The metric excludes informational appearances unless the answer also presents the brand as an option.
Recommendation Rate isolates one specific behavior: whether an AI answer engine names a brand as a choice when a user asks for help deciding. Premise: This is operationally distinct from citation (appearing as a source) and mention (appearing in the text without being recommended).
Premise: When an answer engine responds to a decision-intent query, some brands appear as recommended options and others appear only as context, comparison points, or citations. Recommendation Rate counts only the former.
AI assistants like ChatGPT and Claude are recommendation engines, not search engines: they answer commercial queries by directly nominating brands rather than returning a list of links (Jack et al., 2026). The failure mode differs sharply by tier. L1 brands appear in nearly every relevant retrieval but win only 25-41% of the recommendation slots they reach -- the leverage is differentiation, not visibility. L4 specialists and L5 regional players face catastrophic invisibility -- 48-52% never surface in any of the 37,000 runs (Jack et al., 2026).
A defensible Recommendation Rate measurement records:
Premise: The classification rule must cover ranked lists, unranked option sets, direct selections, conditional recommendations, negative recommendations, and answers that decline to name a provider. Without a declared protocol, two teams measuring Recommendation Rate for the same brand will produce incomparable numbers.
Recommendation Rate = qualifying answers with brand recommendation / observed decision-intent answers
Arithmetic illustration: In a hypothetical set of 24 observed decision-intent answers, a brand is classified as recommended in 6, producing an illustrated Recommendation Rate of 25 percent. These figures are invented solely to demonstrate the calculation.
Cross-model agreement on the top-recommended brand was 41.6%: a top position on one model did not reliably hold on another (Żatuchin, 2026). Recommendation concentration was moderate: the mean Gini coefficient was 0.28 (95% CI [0.16, 0.41]), below the 0.60 power-law threshold (Żatuchin, 2026). Competitive vacuums were rare, appearing in 8.0% of queries, so the models named at least one sampled brand in most cases (Żatuchin, 2026).
Prefixing the user message with a persona drops the recommendation-set similarity (Jaccard) by Delta = -0.12 to -0.20 relative to a same-persona baseline (Jack et al., 2026). The effect is sharply prominence-stratified: category leaders are persona-resistant (~80% same-brand consistency across personas), but mid-market brands swap up to 75% of the recommendation set as the persona changes (Jack et al., 2026).
Premise: These findings reinforce that Recommendation Rate must be reported per engine or with a declared aggregation rule. A single number across all engines obscures model-specific positioning that may require different strategic responses.
Premise: No uniform optimization recipe wins; the right marketing investment depends on where the brand sits on the prominence ladder.
| Tier | Description | Diagnostic Focus |
|---|---|---|
| Category leaders | Retrieval coverage is high | Differentiation within the recommendation set |
| Challengers | Conversion is strong | Persona-mediated substitution vulnerability |
| Mid-market | Coverage is variable | Persona sensitivity and recommendation-set stability |
| Specialists | Retrieval is limited | Overcoming retrieval invisibility |
| Regional players | Coverage is minimal | Establishing retrievable presence |
Premise: For established brands, the leverage point is differentiation within recommendations, not raw visibility. For specialist and regional brands, the first problem is appearing in retrieval at all.
Premise: Recommendation Rate is a Layer 5 metric within the Machine Relations measurement model. It observes decision-intent presence across a defined query set and engine set. It does not explain why a brand is recommended or absent. Source authority, entity identity, content structure, distribution, product fit, query wording, and answer variation remain separate diagnostic inputs.
Premise: Read Recommendation Rate alongside citation presence (Share of Citation) and informational mention presence. The three measures describe distinct answer roles and should retain separate raw classifications in any measurement system.
Premise: Recommendation Rate is not a universal brand score. Results from distinct query sets, observation protocols, or engine sets are not interchangeable.
Premise: Publish the query set, denominator, classification rule, engine set, and observation conditions with the reported rate. Without these disclosures, a Recommendation Rate number cannot be independently verified or compared.
What is the difference between Recommendation Rate and Share of Citation?
Definition: Share of Citation measures how often a brand or domain appears as a cited source in AI-generated answers. Definition: Recommendation Rate measures how often a brand is named as a recommended option in decision-intent answers specifically. Premise: A brand can be cited frequently as a data source without ever being recommended as a product or service, and vice versa.
Why does Recommendation Rate vary across AI models?
Premise: Each model applies different retrieval pipelines, training data distributions, and answer construction patterns. A top-brand position on one model does not reliably hold on another, which means brands need per-engine measurement.
Can a small brand improve its Recommendation Rate?
Premise: The path depends on the brand's starting position. Specialist and regional brands face structural invisibility in AI recommendation systems. For these brands, the first step is becoming retrievable at all through earned media presence, entity clarity, and structured content, before optimizing for recommendation conversion.
What measurement cadence is appropriate for Recommendation Rate?
Working model (not measured): Cadence depends on the query set size and the rate of model updates. Models change their retrieval and ranking behavior with each update, so periodic measurement under a stable protocol provides useful trend data.
Machine Relations definition. This is an established industry term. This page is Machine Relations' definition of it — not a claim to have originated the term.
Machine Relations' own methodology, dataset, and research pages related to this term. These are self-references, listed separately from Sources — they are not independent evidence.
Supporting research
Framework context