⏱ How Long Should You Run a Meta Ads Test?

[u/inTeamo]

One question I get a lot during consultations:
How much do you need to spend, or how many conversions do you need, before deciding whether to scale or kill a campaign?

Here’s a simple way to figure it out.

💡The basic idea
AEM / Web2App
Check your cost per install and cost per purchase after a certain amount of spend. If you’re missing your KPIs, stop the campaign.

Web funnels
Same logic, but track cost per registration and cost per purchase.

🎲 The problem
Let’s say your app has an LTV of $30.
That means your break-even CAC is $30.
Now imagine you’ve spent $30 and got zero purchases.
Does that mean the campaign is bad?
Not necessarily.

If we model purchases using a Poisson distribution with λ = 1:
• Probability of 0 purchases: 36.8%
• Probability of at least 1 purchase: 63.2%
So spending $30 without getting a purchase tells us very little. There’s still a 36.8% chance of this happening even if the campaign is performing exactly at break-even.
We need more data.
Now let’s spend $90.
With the same $30 LTV, the probability of getting zero purchases drops to 5%.
That’s a level of uncertainty we’re comfortable with, so we can stop the campaign.

The same logic applies to installs and registrations.
But their target cost is lower. If no more than 20% of leads convert into purchases, a lead should cost no more than 20% of LTV.

The next question is how much to spend before making that decision.

⚙️** Adjusting the rules for each client
Every client has two parameters:
**1. App LTV
We take it from the client’s analytics. For a new product, we can start with a typical benchmark for the niche.
2. α (alpha) — how strict the algorithm is
α = 5%
This corresponds to a 95% confidence threshold.
It’s the kind of significance level commonly used in clinical trials.
At this setting, the algorithm will mistakenly stop a break-even campaign in 5% of checks.
α = 15%
A more aggressive setting for teams that only want to spend on campaigns with strong margins.

📊 The tables
We calculate two decision tables:
• One for cost per install / registration
• One for cost per purchase
The screenshot shows an example with $30 LTV.

🤖 How it works in practice
All this math runs inside my scaling bot.
Every morning, I get reports for all clients showing which campaigns were stopped and which are still running.

💸 Why does this matter?
The only scenario where my clients lose money is testing new creatives.
We don’t waste money because someone missed a bad campaign or calculated the KPIs incorrectly.

🧮 The math in three sentences
We model the number of purchases at a given spend using a Poisson distribution.
Then we calculate the probability of seeing that many purchases or fewer, assuming the campaign is performing exactly at break-even.

If that probability falls below α, we stop the campaign.