For this analysis I used a site I control: tagmakessc.com, my own Charleston, SC marketing agency. That choice is deliberate. When the test subject is your own site, you know exactly what is on it and exactly what did not change between runs. That removes the biggest source of doubt when a score moves. This analysis, conducted by Therese Grittner for the ARO Index, ran tagmakessc.com against six distinct buyer queries between April and June 2026 to isolate why the same website returns different ARO Scores from one audit to the next. The findings are relevant to business owners and agency partners who see a score move and want to know whether something is wrong.
Here is the most common question the ARO Index gets after someone runs an audit twice: why did my score change? Nothing on the site changed. The score still moved. Sometimes by a point. Sometimes by fifty.
That movement is not a flaw in the measurement. It is the measurement. An ARO Score reflects what AI platforms actually did when asked a buyer's question, and AI platforms do not return a fixed answer. Understanding why the number moves is the difference between reacting to noise and reading a real signal.
Most people treat an ARO Score like a credit score: one number, attached to them, that only changes when they do something. Improve the site, the number goes up. Leave it alone, the number sits still.
The data does not behave that way. An ARO Score is not a property of your website. It is a record of how AI platforms responded to a specific question at a specific moment. Change the question, or simply ask the same question again, and the answer can shift.
Below are real audit results for tagmakessc.com, all from the ARO Index. Because I own the site, I can confirm it was identical across every run. Nothing was edited, republished, or touched between audits. The only variables were the query asked and the moment it ran.
| Query asked | ARO Score | Models selecting | Date |
|---|---|---|---|
| best agency to get my business recommended on ai | 95 | 4 of 4 | Jun 4 |
| how do I see if chatgpt recommends my business in charleston sc | 61 | 2 of 4 | Jun 4 |
| how can I prove AI is recommending a business in atlanta GA | 44 | 1 of 4 | Jun 4 |
| how can I prove AI is recommending a business in atlanta GA | 23 | 0 of 4 | Jun 6 |
Two separate forces are visible in that table, and telling them apart is the whole point.
The gap between 95 and 44 was not about quality. It was about framing. When the query read like a buyer searching for a service provider, "best agency to get my business recommended on ai," all 4 models selected the site. When the query named a different city the business is not based in, Atlanta, GA, the score collapsed. That is correct behavior. AI platforms should not recommend a Charleston, SC business as a local Atlanta, GA option.
This is the larger of the two forces. Query framing routinely moved the score by 40 points or more in this dataset. A score is always a score for a question. There is no single ARO Score for a website detached from what someone asked.
Now look at the two Atlanta, GA rows. Same exact query. Two days apart. The score dropped from 44 to 23, and model selection fell from 1 of 4 to 0 of 4. The site did not change. The question did not change. The models simply returned a different result on a different run.
This is model non-determinism. ChatGPT, Claude, Gemini, and Perplexity do not return identical answers to identical prompts. Ask twice, get two shortlists. This force is smaller than query framing, but it is always present, and it is why a score can drift a few points between back-to-back runs with no other explanation.
The inconsistency is not interference with the signal. The inconsistency is the signal.
The practical read comes down to size and direction of the movement.
A small move, a few points, between runs of the same query is model noise. It means the platforms are slightly uncertain about where you belong, and that uncertainty is normal. It is not a reason to change anything.
A large move tied to a different query is the more useful finding. It tells you which questions you are selected for and which you are invisible for. That is a map, not a malfunction.
A sustained move in one direction across many runs of the same query is the one to watch. That is not noise. That is the competitive ground actually shifting under you, and it is the kind of change the ARO Index is built to surface over time.
A business selected by 4 of 4 models on repeated runs sits in a different position than one selected by 2 of 4, even when a single snapshot makes them look alike. Consistency across models and across runs is the thing worth measuring, because that consistency is what a buyer actually experiences when they ask an AI platform for a recommendation. One reading is a dice roll. The pattern across many readings is the truth.
So when an ARO Score moves, the first question is not "what broke." It is "what moved, by how much, and in which direction." The answer tells you whether you are looking at noise, a framing difference, or a real change in how AI platforms see you.