This article is about Google Analytics 4 properties. If you're using a Universal Analytics property, refer to the Universal Analytics section of this help center.

[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

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 currently two types of attribution models available in the Attribution reports in Google Analytics 4 properties: cross-channel rules-based models and an Ads-preferred rules-based model.

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

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

Cross-channel rules-based models

Last interaction model iconCross-channel 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
  1. Display > Social > Paid Search > Organic Search → 100% to Organic Search
  2. Display > Social > Paid Search > Email → 100% to Email
  3. Display > Social > Paid Search > Direct → 100% to Paid Search

Note: This is the only last click model that you can export to Google Ads. Ads-preferred last click is only available for reporting purposes.

First interaction model iconCross-channel first click: Gives all credit for the conversion to the first channel that a customer clicked (or engaged view through for YouTube) before converting.

Linear model iconCross-channel linear: Distributes the credit for the conversion equally across all the channels a customer clicked (or engaged view through for YouTube) before converting.

Time-decay model iconCross-channel position-based: Attributes 40% credit to the first and last interaction, and the remaining 20% credit is distributed evenly to the middle interactions.

Position-based model iconCross-channel time decay: Gives more credit to the touchpoints that happened closer in time to the conversion. Credit is distributed using a 7-day half-life. In other words, a click 8 days before a conversion gets half as much credit as a click 1 day before a conversion.

Ads-preferred model

Last interaction model iconAds-preferred 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 Cross-channel last click.

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

Data-driven attribution

Coming soon! Data-driven attribution isn't yet available for all accounts.

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.

 Data-driven model iconEach 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, cross-channel last click and data-driven attribution models can yield the same results in certain situations.

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.
 

Admin attribution settings

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

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