Competitive Mind Share
How AI models rank and frame a brand against a defined competitor set on identical prompts, order, leader vs alternative labels, and recommendation strength.
Definition
Competitive Mind Share measures a brand's position inside AI answers relative to a defined competitor set. It captures order of mention, framing label (leader, challenger, alternative, niche), and recommendation strength.
Why it matters
Being mentioned is binary. Being mentioned first, with the leader framing, while a competitor is listed as 'a smaller alternative' is not. Mind share is the AI-era equivalent of share-of-voice for the surface that is replacing search.
How to track it
Define a 3–7 brand competitor set. Run shared prompts across the leading AI models weekly. Score each response on order, framing, and recommendation strength. Aggregate into a per-model and cross-model mind share index.
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The discipline of measuring how AI models describe, categorize, and contextualize a brand, and which sources shape those descriptions. One layer beneath visibility tracking.
Tracking which competitors AI models recommend in your category and how their visibility evolves over time.
The percentage of AI answers in a defined category in which a brand is mentioned, relative to its competitors.
Whether AI-generated answers describe a brand in positive, neutral, or negative terms.
Designing and refining the set of prompts used to test AI visibility, so that the prompts reflect real buyer questions and produce stable, comparable results.
Measuring how a brand performs in ChatGPT vs. Gemini vs. Claude vs. Perplexity side by side, so you can spot the exact models where you're invisible.