Core Concepts

Attribution Overlap

When multiple advertising platforms claim credit for the same conversion, leading to inflated performance metrics.

Attribution overlap occurs when multiple ad platforms claim credit for the same conversion. For example, a customer might click a Facebook ad, then later click a Google ad before purchasing. Both Facebook and Google would claim 100% credit for that conversion in their respective dashboards. This creates a major problem: if you add up the conversions reported by each platform, the total exceeds your actual conversions. Similarly, if you calculate ROAS for each platform and use those numbers for budget allocation, you're making decisions based on inflated metrics. Attribution overlap is especially common between prospecting channels (like Facebook) and retargeting or branded search (like Google). The solution is to use a unified attribution system that deduplicates conversions and assigns credit based on a consistent model. Alternatively, focus on incrementality testing to understand true causal impact rather than relying on platform-reported attribution.

Frequently Asked Questions

What is attribution overlap?

Attribution overlap occurs when multiple ad platforms claim credit for the same conversion. For example, if someone clicks a Meta ad, then clicks a Google ad before purchasing, both platforms count it as their conversion—even though only one sale occurred.

How much attribution overlap is normal?

Attribution overlap typically ranges from 15-50% depending on how many platforms you use and your attribution windows. Brands running Meta, Google, and TikTok often see 30-40% overlap, meaning platforms collectively claim 30-40% more conversions than actually occurred.

How do I fix attribution overlap?

Fix attribution overlap by: using Shopify revenue as your source of truth, calculating blended ROAS instead of relying on platform ROAS, implementing server-side tracking for more accurate data, shortening attribution windows to 7-day click, and using multi-touch attribution models that de-duplicate conversions.

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