Many ad accounts are not actually unable to get results，
instead，just as they start to improve，they get “ruined by increasing the budget”。

So before scaling，be sure to ask yourself 3 questions。

First question：after costs rise，can you still accept it？
If the project becomes unprofitable as soon as costs rise slightly，then this setup simply does not meet the conditions for scaling。

Second question：after the data deteriorates，can it still recover？

For some ad sets，the data may briefly worsen after scaling，but after running for two or three days，once the system relearns，it can gradually stabilize again。This shows resilience，so you can continue to observe。

But some ad sets，once the data collapses，never recover。This shows the model itself is unstable。

Third question：can it be replicated？

If a model cannot be replicated at all，
then it is most likely just lucky enough to encounter a batch of high-quality traffic，rather than being truly stable。

The task in the testing stage is to “find signals”。The task in the scaling stage is to “scale steadily”。

The problem with many teams is that they rush to hit performance targets before testing is complete。As a result，the budget is burned through，and the model is ruined。

Truly outstanding media buyers never compete on speed。They compete on who can better control the pacing。