Your instinct is right and the arithmetic backs it up: at eleven versus seven you cannot detect anything, and the tool telling you otherwise is the tool being used outside its range.
The short version of why. To detect a difference you need enough events that ordinary randomness cannot produce the gap you are seeing. With single-digit conversion counts, randomness produces gaps like yours constantly - flip a coin eighteen times and you will regularly get eleven and seven, and nobody would call that a biased coin.
Two specific traps you have probably also hit:
Peeking. If you check the result as it accumulates and stop when it looks significant, you will find significance almost every time, in noise, guaranteed. Sequential looking at a running test breaks the statistics completely, and every dashboard invites it.
Small effects need enormous samples. People imagine a test detects a ten percent improvement. Detecting that reliably needs sample sizes in the thousands per variant. At forty signups a month you would be waiting years, not a year.
So the honest position: at your size, A/B testing is not a tool you own yet. That is not a failure, it is a scale fact. The good news is that the alternatives are better at this size anyway, and both of the answers below are things you can do this week.