Custom metrics In Meta reporting
When you analyze the efficiency of your advertising efforts sometimes out-of-the-box metrics don't tell the whole story. Creating custom metrics in Meta is done directly in the reporting interface (unlike GA4 calculated metrics which get defined in the admin section). Simply when customizing columns, choose the Custom tab:
The important feature of custom metric in Meta is that you can choose whether you'd like them shared with other users or only private to you - in the dropdown at the bottom:
There's also an interesting choice of proposed metrics on the left menu, depending on which type of campaigns you are running.
In this article we'll show you 6 powerful custom metrics for e-commerce Meta reporting that are not available by default, along with their formulas and specific use cases.
Cart-to-buy ratio
Of all the missing metrics this one is the most surprisingly not there: a percentage of add to cart events that end up with a purchase.
To be precise, this metric is comparing all the add to carts against all purchases (attributed to an ad, ad set or a campaign) so it should be taken with a grain of salt - e.g if one adds 5 items to cart and ends up buying all of them - we'll see 20% - but if it's safe to assume such behavior across various ads/campaigns (that promote same/similar products), this metric becomes a very useful tool for comparing those ads, ad sets or campaigns against each other:
Keep your Adds to cart column nearby - as it helps you quickly control the volume i.e. the significance that a particular row of data has in the overall traffic
With Littledata CAPI feeding your Meta account you can be sure that 100% of add to cart events are properly logged and surfaced in reports!
Once you create the described Cart-to-buy metric you'll know how easy it is to choose and combine metrics from the dropdown of all available numbers in Meta.
In the next chapters we'll focus on metrics specific to Littledata Meta connector as they carry great value for e-commerce advertisers.
By using these metrics you'll be able to distingush between net-new acquisition and returning loyalists (among other benefits) and solve one of the biggest problems in Meta advertising: Attribution Theft (where ads claim credit for customers who would have bought anyway).
Here are 5 advanced custom metrics you can build using Littledata's specific events:
Customer acquisition cost (CAC)
Calculating this metric is simple, right?
CAC = Amount spent / New Customers acquired
Only problem is that New customers is NOT measured in Meta ads.
Unless you use Littledata connector :)
Apart from the standard events we see here - there are 5 more events being sent to your Meta account if you're using Littledata CAPI connector:
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Returning customer purchase
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Recurring purchase
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New customer purchase
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First recurring purchase
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Post purchase upsell
We ordered them just like in the screenshot above, and we're going to use them for calculating eye-opening metrics that help advertisers understand what REALLY performs (and what doesn't!) in Meta.
In order to use these in calculated metrics, you need to register these events as custom conversions.
That step is necessary because the Littledata custom events are not standard in Meta and there are no accompanying metrics for them (e.g just like we have out-of-the-box metric "Purchases').
After registering them you'll be able to see them as column in ANY campaign, ad set or ad, just like this:
In the example above we can see how many new customers are coming from each ad set, and you think it looks cool, right?
Wait till we show you the actual calculated metric which everyone understand (C-suite especially!) Customer acquisition cost!
Now given that you're a doer and not only a reader you'll go ahead right now and replicate the steps explained, and if you do you'll be able to surface a report like this and finally give your ads a fair comparison:
Kaboom! Now you can see what price do you pay for new customers for each ad set! (Can be applied to campaigns and ads as well)
If you're confused with the front¢er column for new-customer purchases Conversion value - it's available by default based on the real value of the proper purchase events.
New Customer Index
Before we show you how easy it is to create the super-insightful metric called New Customer Index let's underline that Meta reporting is event-based, NOT user-based!
We mentioned earlier that calculating metrics and different ratios in Meta reports serves the purpose of comparing assets against each other, and shouldn't be mistaken for absolute truths. It's no accident we repeat it now, because in the CAC example above, have in mind that a new customer purchase can happen only ONCE per user - so it doesn't matter whether we talk about user- or event-scope, the numbers match either way.
In this example we'll use the "return-customer-purchases" count which can happen more than once for any single user so the customer index is somewhat skewed (doesn't represent with 100% accuracy ratio of new vs returning customers - which is why we used "index" and not "rate" to define it)
Our numbers tell quite an interesting story (and it's the last time we'll screenshot how the custom metric looks in the report itself, this looked too good to be skipped):
Again, we're keeping the Purchase column handy, to quickly understand volume impact.
This metric is the "truth serum" for the brand-scaling campaigns. A campaign might show a high ROAS in Meta, but if 80% of those buyers were returning customers, it means you're doing a poor job on acquisition.
It also serves as a diagnostic tool for creative performance. It tells you if a specific creative angle appeals to cold audiences or if it only resonates with people who already know the brand.
Subscription CVR
If you already registered "First Recurring Purchase - Littledata" as a custom conversion, all you need to do is:
Sub CVR = first-recurring-orders / clicks
You can also test Landing page views instead of clicks if you noticed that these two metrics differ significantly. The gap between clicks and LP views happens due to site load speed issues or ad blockers / safe browsers.
Can you create a custom metric that will quantify this problem?
HINT: divide LP views by link clicks (remember - All clicks mean ALL clicks!)
Subscription Retention
This metric is applicable ONLY for long-standing "evergreen" campaigns that promote subscription products.
The formula is:
SR = recurring-orders / first-subscription-orders
It will help you understand which asset produces long term success and which low value customers.
This metric should be used with caution as Meta attribution i.e the way the platform awards conversion credit can strongly affect numbers.
Upsell Rate
Littledata CAPI connector is designed with one thing in mind: to help ecommerce advertisers pull the most out of their dataset.
That's why we made sure to "notify" Meta each time an upsell has happened. Comparing that number against All purchases (or even more specifically new- or returning-customer purchases) can help you better understand how each campaign performs when it comes to upsells!
Uspell rate = upsells-littledata ÷ Total Purchases
Conclusion
Relying on out-of-the-box reporting often obscures true profitability, masking whether you are fueling actual growth or simply recycling existing customers.
By leveraging Littledata’s granular events to calculate metrics like CAC or Subscription CVR, you help Ads optimize for genuine value instead of vanity metrics (like clicks or impressions).