Google Ads Attribution Models

Understanding how customers interact with your brand before making a purchase is important in any digital marketing strategy. Attribution models are powerful tools that help marketers decipher these interactions by distributing credit for conversions across various touchpoints in a user’s journey. From paid ads to organic search and direct visits, each channel plays a unique role in guiding potential customers toward a conversion.

This guide delves into the importance of attribution models, how they function, and how to choose the best one for your business. Whether you're managing a single advertising platform or integrating data across multiple sources, mastering attribution models is key to optimizing your campaigns and driving better results.

What is an Attribution Model in Google Ads?

Attribution models are essential for understanding how to allocate credit for online conversions across the various touchpoints in a user’s journey. There are multiple paths for a user to reach your site: direct visits, organic search, paid ads, email campaigns, and web referrals, to name a few. A single user often interacts with your site multiple times via different channels before completing a conversion—whether it’s becoming a lead or making a purchase. Attribution models help you accurately distribute credit to each interaction, offering insights into the role each channel plays in driving results.

A Conversion Path Example

Meet Jim. He’s on the hunt for the perfect jean jacket. Jim begins his search by googling “jean jacket” and clicking on a paid search ad. He finds a jacket he likes, but he decides to hold off for now.

The next day, Jim starts thinking about that jacket again. This time, he Googles the brand name of the jacket he found the day prior and clicks on another paid search ad. Still undecided, Jim makes two more visits over the next two days. He returns through an organic search result, and then finally, he types the site’s URL directly into his browser and completes his purchase.

Jim’s conversion path looks like this:

Google Paid → Google Paid → Google Organic → Direct

As we explore the different Google Ads attribution models below, we’ll refer back to Jim’s journey to determine how credit for his conversion would be assigned across these sources.

As of recent updates, Google Ads supports two attribution models. Let’s revisit Jim’s conversion path to see how credit for his jean jacket purchase would be allocated under these models:

Data-Driven Attribution

This model uses machine learning to analyze historical data and assign credit to each touchpoint based on its actual influence on the conversion. It evaluates patterns to determine the role each interaction played in the user’s journey.

• Example: For Jim, Data-Driven Attribution might allocate the most credit to the first paid search ad if it significantly contributed to his discovery of the jean jacket. The second paid search ad might receive slightly less credit, while the organic search and direct visit may receive smaller shares based on their contributions to the final decision.

Last Click Attribution

In this model, 100% of the conversion credit is assigned to the last interaction before the purchase.

• Example: For Jim, the last non-direct visit, during which he visited the site organically and completed the purchase, would receive all the credit, ignoring earlier interactions.

Differences Between Google Ads and Google Analytics Conversion Tracking

As an alternative to Google Ads conversion tracking, conversions can be tracked in Google Analytics and then sent to Google Ads through platform integration. The tracking setups are generally the same: you install a tracking code on every page of your website. However, conversion values will be different for tracking that is set up in Google Ads versus in Google Analytics for a variety of reasons, including differences in the attribution models.

Google Ads & Analytics Attribution Model Differences

Google has moved towards Data-Driven Attribution (DDA) as the default model for both Google Ads and Google Analytics. While the attribution method is now aligned across the platforms, there are still notable differences in how each platform evaluates and assigns credit for conversions:

• Google Ads
  DDA in Google Ads considers only Google Paid ad interactions when assigning credit to touchpoints in the conversion path.

• Google Analytics
  DDA in Google Analytics takes a broader approach, analyzing interactions across all channels—such as organic search, email, and direct visits—when distributing credit for conversions.

To clarify how these differences impact reporting, let’s revisit Jim’s jean jacket purchase:

• In Google Analytics (DDA)
  The purchase would be attributed across all interactions—Google Paid, Google Organic, and Direct—based on the influence of each channel as determined by machine learning.
  Example: If Google Organic played a more significant role in guiding Jim to conversion than the Google Paid ads, it would receive a larger share of the credit.

• In Google Ads (DDA)
  The purchase would be attributed exclusively across Google Paid ad interactions.
  Example: Credit would be distributed between the first and second Google Paid ad clicks based on their relative influence, ignoring the Organic and Direct interactions entirely.

Choosing the Right Attribution Method for Your Business

Even though both platforms use DDA, the scope of the model differs. Consider these factors when deciding which method to prioritize:

• Use Google Analytics tracking for a comprehensive view of how all sources contribute to conversions. This approach is especially useful for businesses with a multi-channel strategy.

• Focus on Google Ads tracking if you’re primarily interested in evaluating and optimizing paid search performance.

To streamline your efforts, you can import Google Analytics goals into Google Ads, eliminating the need for duplicate tracking while still benefiting from Analytics’ multi-source insights. Setting up both tracking methods ensures you have a full picture of how conversions are distributed across channels.

Comparing Conversion Data Across Attribution Models

Understanding how different attribution models impact your conversion data can help you evaluate conversion paths, assign value to your keywords, and choose the most effective attribution method for your account. Both Google Analytics and Google Ads offer tools to compare conversion data across various attribution models, depending on where your tracking is set up.

Tools for Attribution Model Comparison

• Google Ads: Attribution Model Comparison: Within Google Ads, you can use the Attribution Modeling tool to compare conversion metrics across different attribution models. This tool is located under:

  • Goals > Measurement > Attribution > Model Comparison.
    Example: By revisiting Jim’s conversion path, you could analyze how different models (e.g., Last Click vs. Data-Driven) would distribute credit among his paid search interactions.

• Google Analytics 4: Attribution Model Comparison: In Google Analytics, you can use the Attribution Model Comparison Tool to evaluate how conversions are divided across models for all tracked interactions. Navigate to:

  • Advertising > Attribution > Attribution Model.
    Example: With Jim’s jean jacket purchase, you could compare how models like Data-Driven Attribution or Last Non-Direct Click would distribute credit across his paid and organic interactions.

Why Use Attribution Model Comparison?

By leveraging these tools, you can:

• Gain insights into the full user journey and how different channels contribute to conversions.

• Determine which keywords or campaigns drive the most value in Google Ads.

• Align your strategy with the attribution model that best fits your business goals.

Utilizing these comparison tools ensures you make data-driven decisions and optimize your marketing efforts effectively.

Closing

Without proper tracking and attribution, it is impossible to get an accurate read on the effectiveness of your digital marketing. If your business could benefit from cleaner data, contact our team to find out how to get started.

This blog post was originally published on December 14, 2017, and was updated and republished on January 2, 2025.