AI Analytics shows the retry rate on one dorm floor rising across two weeks. RF health elsewhere is flat. An engineer expects the analytics product to push a channel change. What should happen instead?
Select an answer to reveal the explanation.
Short Explanation
The product handed you a trend, not a work order. Your retries on that floor have been climbing for two weeks, and the rest of the building is quiet. Go look at the floor, because analytics is not sitting there to type the channel change for you.
Full Explanation
AI Analytics builds baselines and shows how client experience changes. A retry rate that rises for two weeks on one floor, while the rest of the building is flat, is a trend to investigate on that floor. The analytics product is not a configuration push, so standing by for it to edit the radios will not produce a retune. Deleting the floor removes the evidence. It does not make the retries unreal. The beacon interval is not the mechanism analytics uses to apply a channel, and analytics is not applying one. A rising retry rate is an insight about that floor. The analytics view will not log in and change the channel for you. Confirm the result on one live client before changing anything else. Exam caveat: the trend does not name the cause. Retries can be interference, a failing radio, or more clients. The insight tells you where to look, and the survey tells you why. Operational check: open the floor trend, compare it with a quiet floor, and confirm a radio or channel finding on the affected floor before you change the design.