AI visibility tracking data isn’t entirely reliable. Because generative models often produce different responses, the citation shares and rankings on your dashboard are merely snapshots of a continuously changing target, not fixed facts.
A difference between you and a competitor could be genuine or just fluctuation between measurements. A new IQRush paper due for release next week (we had pre-release access) provides a method to distinguish these, showing that no fixed amount of data can definitively settle the question.
The paper is by Ron Sielinski, who co-founded IQRush, who sell software that measures AI visibility the way the paper argues you should. The reason it’s worth your time is that a separate team published a similar repeated-measurement finding in April, so IQRush is not the only one making this case.
How Much These Numbers Move
Repeatedly querying SearchGPT, Gemini, or Perplexity with the same question can produce different sources each time. They’re built to add some randomness to each response, so each citation is just one of many possible URLs it could have pulled. A prior paper by the same author explored this variability, showing that, for example, when testing SearchGPT on running gear, Tom’s Guide made up about 9.5% of citations, while Runner’s World accounted for roughly 6.0%. On the dashboard, Tom’s Guide appeared more often, but the large margin of error meant the figures overlapped. With only one sample, it wasn’t accurate to say Tom’s Guide outperformed Runner’s World, as the 3.5-point difference was within the margin of error. The new paper aims to prevent this mistake by addressing a simple yet often overlooked question: How much data is needed before rankings are truly meaningful?
When A Ranking Is Worth Trusting
The answer has two parts, and both need to be true for a ranking to be reliable. First, the order must stop changing.
In the beginning, rankings may change frequently as new answers are added because no site has a clear edge yet. It’s only after enough answers are collected that the top sites start to stand out clearly, allowing the order to stabilize. Also, it’s important that the top sites are well apart; if they’re very close, the ranking might not be meaningful, as a tight competition doesn’t really show who’s truly ahead. The paper looks at whether the difference between the top sites is bigger than the margin of error for each. When it is, the ranking reflects a real difference. When it isn’t, it’s probably just statistical noise. Both conditions need to be true at the same time, neither alone is enough. In 30 platform-topic tests, the number of answers needed for both conditions to be met ranged from 33 to 94, counting only answers with citations.
Three out of 30 didn’t reach this point even after 125 questions, all on SearchGPT, where top sites were too similar to tell apart. There is no single cutoff applicable everywhere; what works for one…
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