AI Visibility

WORKING PAPER

Recommendation Share: A Measurement Standard for AI Visibility

Recommendation Share: A Measurement Standard for AI Visibility

Recommendation Share: A Measurement Standard for AI Visibility

A repeatable method for measuring how often an AI system recommends a brand first across a controlled portfolio of buyer prompts.

A repeatable method for measuring how often an AI system recommends a brand first across a controlled portfolio of buyer prompts.

A repeatable method for measuring how often an AI system recommends a brand first across a controlled portfolio of buyer prompts.

First Brand Research

10 min read

Recommendation share chart

KEY FINDING

Recommendation Share is meaningful only when the prompt set, model, location, run conditions, and scoring rules remain controlled and visible.

Recommendation share chart

KEY FINDING

Recommendation Share is meaningful only when the prompt set, model, location, run conditions, and scoring rules remain controlled and visible.

PAPER

01 Abstract

02 Method

03 Findings

Abstract

AI visibility is frequently reduced to screenshots or isolated prompts. Those examples are persuasive, but they are not a measurement system. This paper defines Recommendation Share, specifies its denominator, and proposes a reporting protocol designed to make changes comparable over time.

AI Visibility working model: Fix the prompt set, Record valid answers, Score first recommendations.

Definition

First Brand defines Recommendation Share as the percentage of valid tracked answers in which a brand is the first explicit recommendation for a defined prompt portfolio. The numerator is the number of first recommendations. The denominator is the number of valid answers eligible for scoring. Failed responses, safety refusals, and answers that contain no recommendation are reported separately rather than silently mixed into the score.

Method

Create a fixed prompt portfolio, assign every prompt an intent and audience, and record the model, mode, market, language, and date of each run. Run the same prompts on a consistent schedule. Store the full answer, the first recommended brand, all mentioned brands, cited sources, and whether the response was eligible for scoring. Keep raw records so reviewers can reproduce the calculation.

Findings

A single Recommendation Share score can hide important movement. Break the result down by category, audience, funnel stage, and platform. A brand may lead informational prompts but disappear from high-intent comparisons. It may be mentioned often but rarely recommended first. The useful insight is not only the total score. It is the pattern of wins, losses, and unanswered prompts beneath it.

AI Visibility operational scorecard: Prompt coverage, Valid response rate, First recommendation, Source support.

How to apply the framework

Use a stable core portfolio for trend reporting and a rotating discovery portfolio for emerging questions. Report both, but never combine them without labeling the change. Pair Recommendation Share with citation presence and entity accuracy. A recommendation that misstates the offer or lacks supporting evidence is not a clean win. Review movement at the prompt level before assigning a content action.

Limitations

Generated answers can vary between runs because models, indexes, retrieval systems, and answer policies change. Recommendation Share is a sampled observation, not an audit of every possible answer. It should be reported with the exact portfolio and run conditions. Comparisons are strongest when the same protocol is maintained across periods.

Frequently asked questions

What is a valid answer?

A valid answer directly responds to the prompt and provides a recommendation that can be scored. Refusals, errors, and answers without a recommendation are tracked outside the denominator.

How many prompts are enough?

There is no universal number. Use enough prompts to cover the important intents, audiences, categories, and markets without creating a portfolio that cannot be reviewed consistently.

Primary sources

How to cite this paper

First Brand Research. “Recommendation Share: A Measurement Standard for AI Visibility.” Published August 15, 2026. firstbrand.ai/research/recommendation-share-ai-visibility-standard.