Jianxiaopai

One problem I m curious about with GEO is the natural variation in AI answers. The same query can produce different answers, sources, or competitors at different times, so a single before-and-after test might be misleading.

For NiubiGEO, how would you recommend setting up repeated tests to separate normal AI answer variance from a genuine improvement in brand visibility?

For example, should teams run the same query multiple times across several days and compare the overall patterns rather than individual answers? And what sample size would make the change meaningful enough to act on?

I have been exploring generative engine optimization tools recently and find NiubiGEO quite promising. I would like to know how the platform accurately tracks brand visibility in AI responses when potential customers use general product queries without naming the specific brand. Also, what is the best workflow for a new user to set up and run their initial tests effectively?

AI search tools can give different answers to the same query at different times. If I m tracking how often a brand appears, what would be a good way to repeat the tests and know whether a real improvement happened rather than just normal AI variation?