The average bar (at the bottom of your reporting table) lets you quickly compare the performance of one ad group against the rest of the set. Here's how we calculate it and the best way to use it.

Average Spend is calculated by taking a simple mean of the spend of all groups. (Total spend / total ads).

To calculate the other metrics, we take a weighted average of the metric against ad spend, eliminating all metric values of zero or unknown from the equation.
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This prioritizes the ads that have more spend behind them, rather than considering all ads equally, whether they'd spend $1 or $1000.
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For each metric first we weigh each group based on how much they have spent. Take the example below, which would be calculated to show a weighted average of 2.6 for the metric.

With weighted averages, we get a final result that reflects e performance of the set as a whole, and eliminates potential outliers caused by data that is really high (or really low) but has very low spend (for example, an ad that has very high ROAS due to low ad spend).

Not all ads will be optimized for the same outcomes. For example, in one report you might have some ads that are video ads and other ads that are image ads.

So when we're calculating the average of a metric like video plays, we don't want to skew the data by including image ads with data values of zero.

While this will lead to more accurate averages, keep in mind your averages will always be more precise with longer date ranges and more ads compared in the set.

We rounded the final average digits to the nearest 100 decimal points or the nearest integer, depending on if the metric is decimal-based (such as ROAS) or whole-number-based (such as comments).

The average bar lets you compare the performance of one ad group against the rest of the set, fast.

Keep these two points in mind when using them: