Think about the process of analyzing data or running a test.
The more variables you introduce, the more time and money it costs to reach statistical significance. Translation? You won’t have any actionable data to keep optimizing and scaling your spend.
Say you have a daily campaign budget of $250 governing five ad sets each with six ads within them. That’s 30 individual ads that need to be tested in order to determine a winner: 30 variables.
At a minimum, every ad needs 1x AOV to meaningfully evaluate performance: statistical significance. If you’ve got a product with a $200 AOV, the math on that is pretty jamaica mobile database straightforward:
30 (total number of variables) x $200 (AOV) = $6,000 (total spend)
If you take your total spend and divide that by the daily budget of $250, that’s 24 days until you have any actionable information from your campaign.
To calculate the days before you will discover actionable information, first calculate the total number of variables by adding up the number of ads in all your ad sets (ex: 5 ad sets with 6 ads each equals 30 variables.) Next, multiply that number by your average order value and divide by your daily budget. That total gives you a timeline for the number of days it will take before you have actionable information.
Solution: Accelerate your learning as much as possible by reducing the number of ad sets within your CBO.
CBO Mistake 2: Too High of Caps
Before CBOs, bid caps were set at CPA targets.
Now, if you set your bid cap, cost cap, or target cost too low for what Facebook thinks your audience and creative pairing can get you, it won’t spend.
This doesn’t mean that the mistake is low caps. Instead, it’s the exact opposite.
To make Facebook spend your marketing budget, you’ll be tempted to increase your cap or change your audience. Don’t.
Ultimately, this will lead to acquiring customers above your target CPA — sacrificing profitability and limiting your ability to scale.