On/off tests like that can give you a macro view of things, but in general are not an ideal way to go about this in many scenarios.
First, you risk the hit to your acquisition efforts. For many companies in growth mode, that's the LAST thing you want to do, even if you could gain some valuable data. If anything, you do a plus-up test to increase budget in a measurable way (narrow geo-targeting for example) and look for some lift there. But that's just one approach.
Second, attribution isn't just at the channel level. To REALLY be useful, you need to understand it at low enough levels to make more tactical optimizations to your efforts. A simple channel-level on/off test will never get you that, hence why attribution models are really useful.
It's always increase your ad send, the solution is never to decrease the budget. A macro view seems like the best place to start. Also the anlysis is never based on profit it seems to be a ratio of gross rev and medis cost. There are other variable costs like cost of goods sold that must be considered. If cost of conversion is $15 and it generated $60 in rev wow we have a winner but the COG is $45 oops we made no money.
First, you risk the hit to your acquisition efforts. For many companies in growth mode, that's the LAST thing you want to do, even if you could gain some valuable data. If anything, you do a plus-up test to increase budget in a measurable way (narrow geo-targeting for example) and look for some lift there. But that's just one approach.
Second, attribution isn't just at the channel level. To REALLY be useful, you need to understand it at low enough levels to make more tactical optimizations to your efforts. A simple channel-level on/off test will never get you that, hence why attribution models are really useful.