E-commerce attribution in 2026: what actually works

E-commerce attribution has never been more complicated. iOS 14+ killed pixel-based tracking, third-party cookies are gradually disappearing, and attribution windows keep shrinking.

The result: ad platforms have less and less data to attribute conversions. Meta estimates that its pixel misses 30-40% of conversions since iOS 14. Google is also losing accuracy with the end of third-party cookies on Chrome.

Last-click: 100% credit to the last interaction before purchase. Simple but biased — it favors retargeting and brand search.

First-click: 100% credit to the first touchpoint. Better for understanding acquisition but ignores the entire conversion journey.

Linear: credit is split equally among all touchpoints. More fair but dilutes the information.

Data-driven (Google): an algorithmic model that attributes credit based on the estimated impact of each interaction. The most sophisticated, but it's a black box.

Facing the limits of digital attribution, more and more brands are turning to Media Mix Modeling (MMM). The idea: instead of tracking each individual user, you analyze statistical correlations between marketing spend and results.

When you increase your Meta budget by 30%, do your sales increase proportionally? By how much? MMM answers these questions without needing cookies or pixels.

The downside: you need sufficient data volume (at least 6-12 months of history) and it's less granular than click-based attribution.

Use MER as your primary metric. It's the most reliable because it doesn't depend on any attribution model.

Run incrementality tests. Turn off a channel for 1-2 weeks and observe the real impact on your sales. This is the most reliable way to measure each channel's true contribution.

Invest in server-side tracking. Meta's CAPI and Google's server-side tracking recover some of the data lost to cookie blockers.

Automate your reporting. With Dayla, you get a unified view of your spend and revenue without relying on each platform's attribution.