Attribution Model
An attribution model determines which ad interaction gets credit for a conversion when a user interacts with your ads multiple times before purchasing. In Google Ads, data-driven attribution is the standard today.
What is an Attribution Model?
Hardly anyone clicks an ad and buys immediately. Multiple touchpoints are typical: first a generic search, later a click on the brand ad, then the conversion. The attribution model decides which of these clicks gets credit for the conversion and thus influences which campaigns look successful in your reports.
Data-Driven Attribution as the Standard
In Google Ads, data-driven attribution (DDA) is the standard model today. It distributes conversion value across the contributing clicks via machine learning, based on how much each touchpoint actually contributed to the conversion. Google has retired the former rule-based models (linear, position-based, time decay). What remains:
- Data-driven: The standard, dynamic distribution based on your account data
- Last click: The entire value goes to the last click, today mainly useful as a comparison baseline
Why Does It Matter?
The attribution model doesn't change how many conversions you have overall, but how they're distributed across campaigns. Under last click, generic upper-funnel keywords appear weaker than they are because the brand campaign claims the final click.
Pro Tip: Use the comparison report under Attribution in Google Ads to contrast data-driven attribution with last click. Keywords showing significantly more conversions under DDA are your underrated funnel entry points.
Further resources
Frequently Asked Questions
In most cases, data-driven attribution. It's Google's standard and provides the most realistic distribution of conversion value. Last click today mainly serves as a comparison baseline to make differences visible.
The total remains practically the same, but the distribution across campaigns, ad groups, and keywords changes. Individual campaigns may report more or fewer conversions (including decimal values).
DDA is based on machine learning and uses your account's data. Google has significantly lowered the former minimum requirements, making the model available for most accounts. The more conversion data, the more precisely it works.
The model selected in Google Ads only considers Google channels. Cross-channel attribution (e.g. including social ads or email) is found in Google Analytics 4, which is why the numbers systematically differ.
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