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Why Set Up Data Driven Attribution Modelling in Google Analytics 4

Why Set Up Data Driven Attribution Modelling in Google Analytics 4

SaaS companies during their first years often spend 80% to 120% of their annual revenue on sales and marketing. That’s why it’s really important to understand which elements of your marketing are providing the best ROI. That’s where attribution modelling in Google Analytics comes in. But there is a step further – data-driven attribution.

Quick Jump To:

1. What is Attribution

2. GA4 Attribution Modelling Examples

a. Cross-Channel Last-Click Attribution Model
b. Cross-Channel First-Click Attribution Model
c. Cross-Channel Linear Attribution Model
d. Time Decay Attribution Model
e. Cross-Channel Position-Based Attribution Model

3. How is Data-Driven Attribution Different From Normal Attribution Models?

4. How Data-Driven Attribution Works

5. How to Change Attribution Models in Google Analytics 4

6. The Future of Data-Driven Attribution

a. BigQuery with Google Analytics 4

 

What is Attribution

On the path to conversion, customers may interact with multiple ads from the same advertiser. Attribution models let you choose how much credit each ad interaction gets for your conversions. For example, Last click attribution assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. In contrast, first click attribution assigns 100% credit to touchpoints that initiate conversion paths.

GA4 Attribution Modelling Examples

Below is an example of a purchase made by one of your customers. The following statements describe how different attribution models would credit the sale.

A customer finds your site by clicking one of your Google Ads ads. She returns one week later by clicking over from a social network. That same day, she comes back a third time via one of your email campaigns and makes a purchase.

Cross-Channel Last-Click Attribution Model

The last touchpoint — in this case, the Email channel — would receive 100% of the credit for the sale.

Cross-Channel First-Click Attribution Model

The first touchpoint — in this case, the Paid Search channel — would receive 100% of the credit for the sale.

Cross-Channel Linear Attribution Model

Each touchpoint in the conversion path — in this case, the Paid Search, Social Network, and Email channels — would share equal credit (33% each) for the sale.

Time Decay Attribution Model

The touchpoints closest in time to the sale or conversion get most of the credit. In this particular sale, the Email and Social network channels would receive the most credit because the customer interacted with them within a few hours of conversion. Since the Paid Search interaction occurred one week earlier, this channel would receive significantly less credit.

Cross-Channel Position-Based Attribution Model

40% credit is assigned to each the first and last interaction, and the remaining 20% credit is distributed evenly to the middle interactions. In this example, the Paid Search and Email channels would each receive 40% credit, while the Social Network channel would each receive 20% credit.

Please note that all attribution models exclude direct visits from receiving attribution credit, unless the path to conversion consists entirely of direct visits.

How is Data-Driven Attribution Different From Normal Attribution Models?

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.

How to Change Attribution Models in Google Analytics 4

  1. Navigate to your GA4 property
  2. Click the “Admin” link from the left-hand navigation
    GA4-admin
  3. Click on “Attribution settings” in the middle tab
    GA4-Attribution-settings
  4. Select “Data-driven” from the dropdown menu for Reporting attribution model.
    GA4-reporting-attribution-model
  5. Click “Save” at the bottom of the page.

The Future of Data-Driven Attribution

We’ve discussed what data-driven attribution is, provided examples of data-driven attribution models, how they work and how to change them in Google Analytics 4, but what about where data-driven attribution is going?

BigQuery with Google Analytics 4

BigQuery is a fully-managed, serverless data warehouse created by Google. To summarise, it will allow analysts to connect multiple data sources, including CRM and offline data with Google Analytics 4. This means that attribution modelling will become even more advanced.

With BigQuery and Google Analytics 4, soon we will be able to provide attribution to more touchpoints in the customer journey. This is really helpful because oftentimes customer interactions take place offline as well as online, even in the same journey. So being able to link these touchpoints together and attribute value to them will paint a more full picture and help you better allocate ad spend.

As if the data-driven leap in attribution modelling wasn’t impressive enough!

Need help?

If you need more assistance, feel free to get in touch with our team by using our contact page.

Phil Pearce
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Phil Pearce
Phil Pearce
Phil is an analytics expert, author, and web analyst. He's also the Analytics Director & Founder of Google Analytics, Google Tag Manager, Google Ads and CRO agency, MeasureMinds Group. Over the past 20+ years, Phil has helped clients improve their analytics and search engine marketing through the introduction of new tools and disruptive techniques.
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