Glossary · RecoProof metrics

Recommendation Gap

Definition

How often AI mentions a brand without recommending it. Recommendation Gap is the share of eligible answers that name the brand but do not recommend it, so it equals Mention Rate minus Shortlist Share.

RecoProof metric Canonical definition in the methodology

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What it means

The gap counts answers in which the engine knows the brand but does not choose it. Every mention is either a recommendation or a gap, so Recommendation Gap = Mention Rate − Shortlist Share, expressed in percentage points.

It does not require another brand to be recommended instead; Displacement Share, which the AI Recommendation Index publishes beside the gap, measures that. The index, the paid audit and the free check each measure a variant, and the free check shows it as a count out of five answers.

Why it matters

A large gap points to a selection problem rather than an awareness problem: the engine has heard of the brand but finds better-supported reasons to recommend others. That calls for clearer positioning and evidence, not more awareness.

Example

Illustrative: on one engine, a brand is mentioned in 25 of 50 eligible answers and recommended in 10. Mention Rate is 50%, Shortlist Share is 20% and Recommendation Gap is 30 percentage points.

Common misunderstandings

  • It does not mean a competitor was recommended instead; Displacement Share measures that.
  • It does not mean the answer was negative about the brand.
  • It does not explain why the brand was not recommended.
  • It describes measured answers at a point in time and does not guarantee future recommendations.