[GA4] About attribution and attribution modeling

This article is for anyone who wants to learn about how their ads work together on the path to conversions.

Attribution in Google Analytics 4 properties provides enhanced attribution features—such as a revamped Conversion paths report—and new attribution features—such as property-level attribution modeling—that provide deeper insights and more actionability than ever before.

In this article:

Overview of attribution modeling
Data-driven attribution
Paid and organic rules-based models
Google paid channels model
Admin attribution settings

Overview of attribution modeling

Customers may do several searches and click several of your ads before making a purchase or completing another valuable action on your website. Typically, all credit for the conversion is given to the last ad customers clicked. But was it solely that ad that made them decide to convert? What about the other ads they clicked on before it?

Attribution is the act of assigning credit for conversions to different ads, clicks, and factors along a user's path to completing a conversion. An attribution model can be a rule, a set of rules, or a data-driven algorithm that determines how credit for conversions is assigned to touchpoints on conversion paths.

There are three attribution models available in the Attribution reports in Google Analytics 4 properties: Data-driven attribution, Paid and organic last click, and Google paid channels last click.

To find the Attribution reports, click Advertising on the left. Under Attribution, click either Model comparison or Conversion paths.

Notes:

All attribution models exclude direct visits from receiving attribution credit, unless the path to conversion consists entirely of direct visit(s).

Data-driven attribution

Data-driven: Data-driven attribution distributes credit for the conversion based on data for each conversion event. It's different from the other models because it uses your account's data to calculate the actual contribution of each click interaction.

Each Data-driven model is specific to each advertiser and each conversion event.

How data-driven attribution works

Attribution uses machine learning algorithms to evaluate both converting and non-converting paths. The resulting Data-driven model learns how different touchpoints impact conversion outcomes. The model incorporates factors such as time from conversion, device type, number of ad interactions, the order of ad exposure, and the type of creative assets. Using a counterfactual approach, the model contrasts what happened with what could have occurred to determine which touchpoints are most likely to drive conversions. The model attributes conversion credit to these touchpoints based on this likelihood.

Note: Depending on data availability, data-driven attribution models may leverage aggregate data from Data sharing settings.

The methodology behind data-driven attribution (advanced)

There are two main parts to the data-driven attribution methodology:

Analyzing the available path data to develop conversion rate models for each of your conversion events
Using the conversion rate model predictions as input to an algorithm that attributes conversion credit to ad interactions

Develop conversion probability models from available path data

Data-driven attribution uses path data—including data from both converting and non-converting users—to understand how the presence and timing of particular marketing touchpoints may impact your users’ probability of conversion. The resulting models assess how likely a user is to convert at any particular point in the path, given exposure to a particular ad interaction.

The models compare the conversion probability of users who were exposed to the ad, to the conversion probability of similar users in a holdback group. (In more technical terms, the models compute the counterfactual gains of Google ad exposures by training on data from randomized controlled trials.)

Algorithmically assign fractional conversion credit to marketing touchpoints

The data-driven attribution model assigns credit based on how the addition of each ad interaction to the path changes the estimated conversion probability. The data-driven attribution algorithm uses features including time between the ad interaction and the conversion, format type, and other query signals to calculate this credit.

Example

In the following high-level illustration, the combination of Ad Exposure #1 (Paid search), Ad Exposure #2 (Social), Ad Exposure #3 (Affiliate), and Ad Exposure #4 (Search) leads to a 3% probability of conversion. When Ad Exposure #4 does not occur, the probability drops to 2%, so we know that Ad Exposure #4 drives +50% conversion probability. We repeat this for each ad interaction and use the learned contributions as attribution weights.

Paid and organic last click

Note: The first click, linear, time decay, and position-based attribution models are no longer available as of November 2023. Learn more

Paid and organic last click: Ignores direct traffic and attributes 100% of the conversion value to the last channel that the customer clicked through (or engaged view through for YouTube) before converting. See examples below of how conversion value is allocated:

Examples

Display > Social > Paid Search > Organic Search → 100% to Organic Search
Display > Social > Paid Search > Email → 100% to Email
Display > Social > Paid Search > Direct → 100% to Paid Search

Note:

Paid and organic last click and Last non-direct click are two names for the same attribution model.

An engaged view is counted in data-driven attribution when a user:

Watches an ad for 30 seconds (or until the end if it is less than 30 seconds)
Clicks on a teaser card
Clicks a companion banner or video wall
Clicks on a phrase that is a call to action
Clicks on the end screen
Clicks to visit the advertiser’s website

Google paid channels last click

Google paid channels last click: Attributes 100% of the conversion value to the last Google Ads channel that the customer clicked through before converting. If there is no Google Ads click in the path, as in Example 6, the attribution model falls back to Paid and organic last click.

Examples

Display > Social > Paid Search > Organic Search → 100% to Paid Search
Display > Social > YouTube EVC > Email → 100% to YouTube
Display > Social > Email > Direct → 100% to Email (fallback to last non-direct click)

Admin attribution settings

Users with the Editor role on the property can now select an attribution model and conversion window at the property level to apply to a number of reports. To access this setting, go to Admin > Attribution Settings under Data display. Learn more

Note: The Admin Attribution Settings do not impact attribution models selected in the Attribution reports.

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