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A Step-by-Step Guide on GA4 Channel Attribution
This step-by-step guide explains Data-Driven, Last-Click, and Google Paid Last-Click Attribution, plus how third-party cookie deprecation impacts conversion tracking. Learn how to choose the best model for your marketing goals
Lisa Ying
Manager, Data Solutions
Understanding Channel Attribution in GA4
Over the past few years, we have seen many Google Analytics users confused about the “Channel Attribution” feature and how marketing credit attribution works behind the scenes.
This blog is dedicated to sharing the Pros and Cons of each channel attribution and giving step-by-step guidance on how to pick a channel attribution that fits your marketing goals. Over the years, discussions have arisen in the industry that foresee possible issues every marketer may encounter when adapting to a cookieless environment (where 3rd party cookies will be gradually deprecated for privacy marketing in the long term). Thus, our blog post will also discuss how the attribution models may be affected by the gradual deprecation of 3rd party cookies and possible solutions to diminish the negative impacts of potential 3rd party cookie inaccessibility.
What is Attribution?
Attribution is the act of assigning credit for conversions to different ads, clicks, and factors along a user's path to completing a conversion. By tracing the credits, analysts can make the right judgment on which user acquisition path is more efficient for converting new prospects, gaining new sales, or raising better brand awareness.
GA4 supports three main attribution models:
Data-Driven Attribution
Paid & Organic Last-Click Attribution
Google Paid Last-Click Attribution
👉 15 Common GA4 Attribution Challenges and How to Solve Them
What is an Attribution Model?
Now that we understand what attribution is, the attribution model is a group of rules Google set up to gauge and give credit to the key customer journey touchpoints. There are benefits and potential drawbacks to each Attribution model, but currently, the default attribution that is being used in Google Analytics 4 is the Paid and Organic data-driven model.
GA4 Attribution Models Explained
1. Data-Driven Attribution
GA4’s default Data-Driven Attribution model uses machine learning to assign credit to interactions based on their likelihood to drive conversions. It considers:
Time from conversion
Device type
Number of ad interactions
Order of ad exposure
Type of creative assets
💡 Drawback: This model may attribute credit to touchpoints that didn’t directly lead to conversions but were likely influential.
2. Paid & Organic Last-Click Attribution
Assigns full credit to the last interaction before conversion (excluding direct traffic).
Helps marketers understand which channels directly drive sales.
💡 Drawback: It ignores all prior touchpoints, potentially undervaluing upper-funnel efforts.
3. Google Paid Last-Click Attribution
Similar to last-click, but Google Ads interactions override all other touchpoints in the conversion path.
Helps optimize for Google Ads conversions in GA4.
💡 Drawback: This model prioritizes paid Google interactions, which may not accurately represent the entire customer journey.
How Attribution Models Work in GA4
Attribution models in GA4 are all over the place and it is hard to understand what you are looking at in the tool. Not surprisingly, reports in different sections of GA4 populate data points based on different attribution models. For user-scoped and session-scoped dimensions, GA4 uses the paid and organic channels' last-click attribution model. User-scoped and session-scoped dimensions are unaffected by changes to the attribution model.
For event-scoped dimensions, GA4 uses the attribution model that you select, but by default, GA4 is set to use the data-driven attribution model. All reports with event-scoped traffic dimensions reflect the attribution model you select.
Lastly, in the Data Attribution comparison report, you can view and compare all models’ reporting results. This report can help you monitor the credit attribution happening on different channels and easily switch to a better-fitted attribution when needed.
👉 Session-Level Attribution in GA4 with BigQuery: A Practical Guide
How to Choose the Best Attribution Model
GA4 allows you to experiment with different attribution models using the Model Comparison Tool and A/B testing to compare the performance of different models. Monitor how changes in attribution impact your understanding of user behaviour journey and conversions. Here are some questions to consider before choosing an attribution model:
✅ What does your user journey look like?
✅ How do customers learn about your brand?
✅ What triggers them to convert?
✅ Are there offline conversions that should be considered?
✅ Where do customers typically drop off?
As you may know, the first click, linear, time decay, and position-based attribution models have been terminated by Google since November 2023. Data-driven attribution, the most commonly used attribution model for conversions, is now also the main support in automated bidding in Google Ads, Thus, in most business cases, data-driven attribution is the most recommended model and will fit most analytical goals and needs.
💡 Since November 2023, First Click, Linear, Time Decay, and Position-Based models have been discontinued. GA4 now primarily supports Data-Driven Attribution, which is also used for automated bidding in Google Ads.
The 2 Last-click attribution models are the most compatible when your GA4 connects with Google Ads, as Google Analytics conversions can be imported into linked Google Ads accounts. This setting allows you to choose which channels are eligible to receive conversion credit for web conversions shared with Google Ads.
A helpful approach to utilize when determining which channels are most effective at converting prospects is last-click attributions. But remember that last-click attribution doesn't give you a whole picture of the client journey, so you might want to think about utilizing alternative models as well.
When to Use Last-Click Models
If GA4 is linked to Google Ads, last-click models help optimize paid ad conversions.
Best for businesses that need a clear view of direct revenue drivers.
📌 Warning: Last-click models may oversimplify the customer journey by ignoring earlier touchpoints.
👉 Advanced UTM Attribution in GA4 Using BigQuery
Challenges & Limitations of Attribution Models
While attribution models provide valuable insights, it is essential to acknowledge their challenges and limitations.
❌ 1. Incomplete Customer Journey Representation
It can be challenging to choose the best marketing attribution model for your company because there isn't one model that works for all businesses. Data-driven model is a normalized and simplified representation of your customer journey and can be contrafactual.
❌ 2. Limited Visibility
Attribution models don't give you a full picture of how customers engage with your business because they concentrate on particular touchpoints along the customer journey.
❌ 3. Lack of Customization
As there are currently only 3 attribution models in GA4, there is also no flexibility to customize your attribution channel based on your rule definitions. Thus, most businesses have no other options other than the Data-Driven model.
👉 Redefining Attribution: Unlocking True Campaign Value Drives Significant Shifts in Media Spend
Impact of 3rd-Party Cookie Deprecation on Attribution
Third-party cookies not only facilitate audience reach but also effectively benefit the retargeting of an individual, who visited a website without making a purchase. With cookies on the way out, targeting, retargeting and attribution will be far more complex.
Unfortunately, we can’t see a clear full picture of how the loss of view-through data might have an impact on credit attribution. The data-driven model reportedly includes Ads exposure details.
Associating Ads exposure details with user activity will diminish over time, which will disproportionately affect Display Ads but may translate to relatively little difference in the perceived output, as the model may adapt using modelling to gauge the influence. Thus, the impact isn't really clear yet, but it would be relatively limited to display ads served as external placements, but not clicked on directly.
Without the 3rd party cookies, it will become difficult to identify, target, frequency-cap, and measure users and generate consistent user IDs across the funnel. It might also present a challenge for the analytical tools that rely on cookies to identify, group, and target audiences as well as report on user behaviour and conversions. To carry out effective marketing attribution beyond the demise of third-party cookies, it will be necessary to focus on three key areas:
KPI Performances: Viewability, reach, and click-through rate KPIs should be consistent across all segments for marketers and publishers. They should anticipate a drop in overall impressions but a rise in click-through rate in the absence of third-party cookies because the industry will be able to target impressions much more accurately using zero and first-party data.
It will be essential for comprehending and matching user ID values. To what extent does GA4 recognize the IDs as you do? You can assess whether unified IDs are as dependable by looking at match rates for pre- and post-third-party cookies.
Revenue attribution reporting: Ad income and the capacity to link it to specific pageviews, users, segments, content types, and other factors will be crucial. Additionally, since it's expected that these costs will rise, the industry must closely monitor the average CPM (the cost of 1,000 ad impressions on a single webpage) before and after third-party cookies.
👉 Google Consent Mode’s Impact on Marketing Platforms
Key Focus Areas for Cookieless Attribution
1️⃣ KPI Monitoring – Expect lower impression counts but higher CTRs due to better targeting.
2️⃣ User ID Matching – Assess GA4's ability to recognize users without cookies.
3️⃣ Revenue Attribution – Track cost-per-impression (CPM) changes post-cookie deprecation.
Conclusion
You can focus on the buyer's journey and determine what aspects are most effective for your customers and what requires improvement by using attribution modelling. It also provides information on how your target demographic is being converted by your marketing channels and touchpoints in concert.
Choose the models that will give you the information that matters most to you, choose the appropriate tool, and begin using the attribution models.
The attribution models utilized in the digital ecosystem—including media teams, agencies, publishers, platforms, and data providers—are changing with the cookieless marketing future.
Key Takeaways:
✔ Choose a model based on your marketing goals and sales cycle.
✔ Experiment with GA4’s Attribution Comparison Tool to find the best fit.
✔ Be aware of the limitations of last-click attribution and explore alternatives.
✔ Prepare for third-party cookie deprecation by leveraging first-party data strategies.With Google’s shift toward a cookieless future, businesses must adapt their attribution strategies to ensure accurate conversion tracking.
Next Steps
🔹 Need help optimizing GA4 attribution? Contact us for expert guidance.
🔹 Want to improve your data-driven marketing strategy? Let’s discuss how GA4 can work for you.
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A Step-by-Step Guide on GA4 Channel Attribution
This step-by-step guide explains Data-Driven, Last-Click, and Google Paid Last-Click Attribution, plus how third-party cookie deprecation impacts conversion tracking. Learn how to choose the best model for your marketing goals
Lisa Ying
Manager, Data Solutions
Understanding Channel Attribution in GA4
Over the past few years, we have seen many Google Analytics users confused about the “Channel Attribution” feature and how marketing credit attribution works behind the scenes.
This blog is dedicated to sharing the Pros and Cons of each channel attribution and giving step-by-step guidance on how to pick a channel attribution that fits your marketing goals. Over the years, discussions have arisen in the industry that foresee possible issues every marketer may encounter when adapting to a cookieless environment (where 3rd party cookies will be gradually deprecated for privacy marketing in the long term). Thus, our blog post will also discuss how the attribution models may be affected by the gradual deprecation of 3rd party cookies and possible solutions to diminish the negative impacts of potential 3rd party cookie inaccessibility.
What is Attribution?
Attribution is the act of assigning credit for conversions to different ads, clicks, and factors along a user's path to completing a conversion. By tracing the credits, analysts can make the right judgment on which user acquisition path is more efficient for converting new prospects, gaining new sales, or raising better brand awareness.
GA4 supports three main attribution models:
Data-Driven Attribution
Paid & Organic Last-Click Attribution
Google Paid Last-Click Attribution
👉 15 Common GA4 Attribution Challenges and How to Solve Them
What is an Attribution Model?
Now that we understand what attribution is, the attribution model is a group of rules Google set up to gauge and give credit to the key customer journey touchpoints. There are benefits and potential drawbacks to each Attribution model, but currently, the default attribution that is being used in Google Analytics 4 is the Paid and Organic data-driven model.
GA4 Attribution Models Explained
1. Data-Driven Attribution
GA4’s default Data-Driven Attribution model uses machine learning to assign credit to interactions based on their likelihood to drive conversions. It considers:
Time from conversion
Device type
Number of ad interactions
Order of ad exposure
Type of creative assets
💡 Drawback: This model may attribute credit to touchpoints that didn’t directly lead to conversions but were likely influential.
2. Paid & Organic Last-Click Attribution
Assigns full credit to the last interaction before conversion (excluding direct traffic).
Helps marketers understand which channels directly drive sales.
💡 Drawback: It ignores all prior touchpoints, potentially undervaluing upper-funnel efforts.
3. Google Paid Last-Click Attribution
Similar to last-click, but Google Ads interactions override all other touchpoints in the conversion path.
Helps optimize for Google Ads conversions in GA4.
💡 Drawback: This model prioritizes paid Google interactions, which may not accurately represent the entire customer journey.
How Attribution Models Work in GA4
Attribution models in GA4 are all over the place and it is hard to understand what you are looking at in the tool. Not surprisingly, reports in different sections of GA4 populate data points based on different attribution models. For user-scoped and session-scoped dimensions, GA4 uses the paid and organic channels' last-click attribution model. User-scoped and session-scoped dimensions are unaffected by changes to the attribution model.
For event-scoped dimensions, GA4 uses the attribution model that you select, but by default, GA4 is set to use the data-driven attribution model. All reports with event-scoped traffic dimensions reflect the attribution model you select.
Lastly, in the Data Attribution comparison report, you can view and compare all models’ reporting results. This report can help you monitor the credit attribution happening on different channels and easily switch to a better-fitted attribution when needed.
👉 Session-Level Attribution in GA4 with BigQuery: A Practical Guide
How to Choose the Best Attribution Model
GA4 allows you to experiment with different attribution models using the Model Comparison Tool and A/B testing to compare the performance of different models. Monitor how changes in attribution impact your understanding of user behaviour journey and conversions. Here are some questions to consider before choosing an attribution model:
✅ What does your user journey look like?
✅ How do customers learn about your brand?
✅ What triggers them to convert?
✅ Are there offline conversions that should be considered?
✅ Where do customers typically drop off?
As you may know, the first click, linear, time decay, and position-based attribution models have been terminated by Google since November 2023. Data-driven attribution, the most commonly used attribution model for conversions, is now also the main support in automated bidding in Google Ads, Thus, in most business cases, data-driven attribution is the most recommended model and will fit most analytical goals and needs.
💡 Since November 2023, First Click, Linear, Time Decay, and Position-Based models have been discontinued. GA4 now primarily supports Data-Driven Attribution, which is also used for automated bidding in Google Ads.
The 2 Last-click attribution models are the most compatible when your GA4 connects with Google Ads, as Google Analytics conversions can be imported into linked Google Ads accounts. This setting allows you to choose which channels are eligible to receive conversion credit for web conversions shared with Google Ads.
A helpful approach to utilize when determining which channels are most effective at converting prospects is last-click attributions. But remember that last-click attribution doesn't give you a whole picture of the client journey, so you might want to think about utilizing alternative models as well.
When to Use Last-Click Models
If GA4 is linked to Google Ads, last-click models help optimize paid ad conversions.
Best for businesses that need a clear view of direct revenue drivers.
📌 Warning: Last-click models may oversimplify the customer journey by ignoring earlier touchpoints.
👉 Advanced UTM Attribution in GA4 Using BigQuery
Challenges & Limitations of Attribution Models
While attribution models provide valuable insights, it is essential to acknowledge their challenges and limitations.
❌ 1. Incomplete Customer Journey Representation
It can be challenging to choose the best marketing attribution model for your company because there isn't one model that works for all businesses. Data-driven model is a normalized and simplified representation of your customer journey and can be contrafactual.
❌ 2. Limited Visibility
Attribution models don't give you a full picture of how customers engage with your business because they concentrate on particular touchpoints along the customer journey.
❌ 3. Lack of Customization
As there are currently only 3 attribution models in GA4, there is also no flexibility to customize your attribution channel based on your rule definitions. Thus, most businesses have no other options other than the Data-Driven model.
👉 Redefining Attribution: Unlocking True Campaign Value Drives Significant Shifts in Media Spend
Impact of 3rd-Party Cookie Deprecation on Attribution
Third-party cookies not only facilitate audience reach but also effectively benefit the retargeting of an individual, who visited a website without making a purchase. With cookies on the way out, targeting, retargeting and attribution will be far more complex.
Unfortunately, we can’t see a clear full picture of how the loss of view-through data might have an impact on credit attribution. The data-driven model reportedly includes Ads exposure details.
Associating Ads exposure details with user activity will diminish over time, which will disproportionately affect Display Ads but may translate to relatively little difference in the perceived output, as the model may adapt using modelling to gauge the influence. Thus, the impact isn't really clear yet, but it would be relatively limited to display ads served as external placements, but not clicked on directly.
Without the 3rd party cookies, it will become difficult to identify, target, frequency-cap, and measure users and generate consistent user IDs across the funnel. It might also present a challenge for the analytical tools that rely on cookies to identify, group, and target audiences as well as report on user behaviour and conversions. To carry out effective marketing attribution beyond the demise of third-party cookies, it will be necessary to focus on three key areas:
KPI Performances: Viewability, reach, and click-through rate KPIs should be consistent across all segments for marketers and publishers. They should anticipate a drop in overall impressions but a rise in click-through rate in the absence of third-party cookies because the industry will be able to target impressions much more accurately using zero and first-party data.
It will be essential for comprehending and matching user ID values. To what extent does GA4 recognize the IDs as you do? You can assess whether unified IDs are as dependable by looking at match rates for pre- and post-third-party cookies.
Revenue attribution reporting: Ad income and the capacity to link it to specific pageviews, users, segments, content types, and other factors will be crucial. Additionally, since it's expected that these costs will rise, the industry must closely monitor the average CPM (the cost of 1,000 ad impressions on a single webpage) before and after third-party cookies.
👉 Google Consent Mode’s Impact on Marketing Platforms
Key Focus Areas for Cookieless Attribution
1️⃣ KPI Monitoring – Expect lower impression counts but higher CTRs due to better targeting.
2️⃣ User ID Matching – Assess GA4's ability to recognize users without cookies.
3️⃣ Revenue Attribution – Track cost-per-impression (CPM) changes post-cookie deprecation.
Conclusion
You can focus on the buyer's journey and determine what aspects are most effective for your customers and what requires improvement by using attribution modelling. It also provides information on how your target demographic is being converted by your marketing channels and touchpoints in concert.
Choose the models that will give you the information that matters most to you, choose the appropriate tool, and begin using the attribution models.
The attribution models utilized in the digital ecosystem—including media teams, agencies, publishers, platforms, and data providers—are changing with the cookieless marketing future.
Key Takeaways:
✔ Choose a model based on your marketing goals and sales cycle.
✔ Experiment with GA4’s Attribution Comparison Tool to find the best fit.
✔ Be aware of the limitations of last-click attribution and explore alternatives.
✔ Prepare for third-party cookie deprecation by leveraging first-party data strategies.With Google’s shift toward a cookieless future, businesses must adapt their attribution strategies to ensure accurate conversion tracking.
Next Steps
🔹 Need help optimizing GA4 attribution? Contact us for expert guidance.
🔹 Want to improve your data-driven marketing strategy? Let’s discuss how GA4 can work for you.
More Insights
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