AI Recommendation Openness
AI recommendation openness is a Routeless 0-100 summary of how widely business names were distributed across 18 sampled AI answers. Higher means names were more dispersed; lower means a smaller set repeated more often. It does not measure market saturation, demand, or quality.
The score describes the recommendation pattern in one dated sample. It does not tell you how many businesses operate in the city, how much customers spend, or whether entering the market will be easy. “Open” means the assistants spread their recommendations across more names in the sampled answers, not that the real-world market has spare capacity.
The score combines three equally weighted observations. First, how often did the most-named business appear? Second, how many repeat names did ChatGPT, Gemini, and Perplexity share? Third, what share of businesses appeared only once? A market scores higher when there is no dominant repeat name, the assistants share fewer names, and one-off mentions make up more of the list.
Averaged across five metros, dental practices scored 84.6, law firms 68.4, HVAC companies 66.4, med spas 63.2, roofing companies 62.0, and plumbing companies 60.2. In this sample, dental recommendations were spread across more names; plumbing recommendations repeated a smaller set more often. The gap between trades was wider than the city range inside any one trade.
Each market has only 18 answers, AI responses change, and the equal weights are a stated design choice rather than a scientifically optimized formula. The score can compare recommendation patterns within these samples. It cannot predict revenue or identify a market winner.
Compare scores only when the prompt set, assistants, run conditions, and sampling period are consistent. A methodology change can move the score without any underlying market change.
The methodology page publishes the formula, thresholds, limits, and raw inputs so readers can recompute each score.
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