Improving Attribution Accuracy with Popsixle

Learn how to identify misattribution with the Attribution Accuracy report, and why better data = better accuracy

You need to be able to count on your performance data to make informed and confident decisions about your advertising strategies, but Meta's attribution modeling can make it tough to trust what you're seeing -- that’s why we created the Popsixle Attribution Accuracy report. 

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Located on the Popsixle Customer Dashboard (and featured on our Popsixle Health Check) the Attribution Accuracy report is designed to give customers like you a transparent view of platform-reported performance: by measuring Meta-reported purchases against purchases on your site that Popsixle matched to a Facebook click (fbc), the graph and report help you to know if there’s any over- or under-reporting happening in your account.

From there, you can use that information to simply account for the gaps with your decision-making, or take steps to improve the accuracy.  

Why does misattribution happen?

Misattribution (like over- or under-reporting) is caused by a poor data connection . Some CAPI connections (like Shopify’s native CAPI integration) are known to lose up to 60% of data as it’s sent to Meta because of users who opted out of data tracking. When Meta has only a partial view of your purchases, it “guesses” with data modeling. Some days it guesses too high; other days it guesses too low. Unpredictable mis-attribution becomes completely impossible to manage. 

By comparison, better data = better accuracy & performance: As a more powerful data connection, Popsixle is able to restore missing data and send it to Meta at much higher volumes so Meta doesn’t need to rely on data modeling to fill the gaps.

Restoring the flow of data naturally fixes attribution issues and leads to more profitable ad performance, because once Meta understands which users became high-value customers (and which ones didn’t), its optimization algorithm can find more of the right people – which means by sending the best data, the machine learning model is being trained on who your best customers are. 

In addition to its main connection, Popsixle has complementary code features that optimize for accuracy, such as our purchase remapping PRO code that prevents Meta from “taking credit” for nonstandard purchases like recurring subscriptions and offline orders. Learn more in this guide: Popsixle Purchase Routing Optimization (PRO)

How to read the Popsixle Attribution Accuracy report

Watch this 5 minute video tutorial to learn how to read the Attribution Accuracy report on the Popsixle Dashboard: Tutorial: Popsixle's Attribution Accuracy Report

The graph in the Attribution Accuracy report will verify if there is accurate reporting in Ads Manager, or help you to diagnose reporting discrepancies by showing:

  • the total web purchases that processed on your Shopify store each day 
  • how many purchases Meta is “taking credit for" in Ads Manager
  • what Popsixle determines as the “expected range” for what Meta *should* take credit for, based on site visits and site purchases that are matched to a Facebook click (fbc) 
  • the difference between 7-day click and 1-day view-through purchases

✅ Here’s an example of accurate attribution, where you can see reported purchases (blue line) are within the expected range of attribution (purple range)

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⬆️ Here’s an example of over-reporting, where you can see reported purchases (blue line) are higher than the expected range of attribution (purple range)

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How to improve accuracy 

An account that has a lot of over-counting or erratic & unpredictable mis-attribution is problematic for two reasons: 

  1. It's difficult to trust what you’re seeing, and therefore challenging to steer your campaigns effectively
  2. It’s usually a symptom of another underlying problem that could be negatively affecting performance

To get mis-attribution under control, start by running through the checks in the Popsixle Attribution Accuracy Improvement Checklist

The checklist will guide you toward confirming attribution best practices and identifying the source so the Popsixle team can help you to resolve any underlying issues. 

The best goal for accuracy 

Keep in mind that because of certain factors, some accounts may not be able to achieve 100% accurate attribution even with all best practices applied. 

For example, accounts with lower purchase volume (under 50 purchases per week) tend to experience over-counting because with less purchase data to work with, Meta will need to rely on its model to fill the gaps. 

If you’ve ensured best-possible data flow but your account still has some small and consistent gaps in accuracy, that’s very workable.  For instance, if you can see through the Attribution Accuracy graph that Meta always takes 10% more credit than it should, it’s easy enough to account for by mentally minimizing all the numbers you see by 10%. 

Perfect accuracy shouldn’t be the ultimate goal.

Instead, recall the bigger picture: if you have some consistent over-counting but Meta knows exactly who your best customers are and is optimizing ads very well, you’ll be in an enviable spot with profitable and scalable ads. With Popsixle supporting your site, the benefits of machine learning will far outweigh a small degree of over-attribution by unlocking massive gains in performance.

Have questions about the Attribution Accuracy report, or need assistance with improvement? The Popsixle team is happy to help! Send us an email, and we’ll follow up within one business day to support you.