Google Streamlines Models of Attribution: What Advertisers Should Know

Google’s platform is always pushing for more automation and less autonomy from advertisers. This push to adopt newer, “simpler” features often means discontinuing older ones.

The discontinuation of linear, first-click, and position-based models, as well as the discontinuation for time decay, linear, and first-click attributions, is one of the latest victims.

Google announced its phase-out at the beginning of this year. We’re now at the point where you can no longer use these attribution models. You will have to switch any conversions you had previously that were utilizing these attribution models.

What does this mean to advertisers moving forward? This article will explain the changes to Google Ads and how you can use them.

Traditional attribution models

Let’s look at the traditional attribution model and see what it is that makes it different from the last-click and data-driven models.

• Last Click (still available): This model gives all credit to the previous interaction that a user makes with an advertisement before they convert.
• First Click: First click attribution gives credit to the initial interaction of the customer journey regardless of any subsequent interactions.
• Linear: The linear model distributes credit evenly across all touchpoints during the customer journey.
• Time decay time decomposition attribution gives more credit to interactions that are closer to conversion and less credit to earlier interactions.
• Positional-based: The model gives greater credit to the interactions that occur at the beginning and the end and less credit to those in the middle.

What is data-driven attribution?

Let’s look deeper at the data-driven model to understand why Google discontinued traditional attribution models.

Data-driven conversion tracking is a method for Google Ads that allows you to track and attribute conversions based on historical data, machine learning algorithms, and specific keywords. This tracking system is designed to give advertisers more accurate information about the effectiveness of their marketing efforts.

How data-driven attribution is used

Here’s the data-driven attribution in action:

1. Data Collection: Google Ads collects data on user interactions. This includes clicking data and user behavior on the website, as well as conversion data (e.g., purchases, form submissions, or phone calls).
2. Machine Learning Algorithms: Google analyzes this data using machine learning algorithms in order to identify patterns and trends. It examines various factors, such as time of day and device type, to determine what influences conversions.
3. Attribution Modeling: Data-driven conversion tracking uses advanced attribution modeling techniques to assign values to different touchpoints of the customer journey. It takes into account the entire conversion process, including all interactions with your ads prior to a conversion.
4. Prediction of conversion: Based upon historical data and machine-learning insights, Google Ads estimates the likelihood that a transformation will occur for each click made on your ad. This prediction can help determine which clicks on your ad are most likely to result in a conversion.
5. Optimizing: Google Ads utilizes this predictive data in order to optimize your bid strategy. It can adjust bids in real-time and allocate more budgets to keywords or ads that are more likely to convert. You can maximize your return on investment by using this tool.
6. Reporting on performance: In Google Ads, you can view detailed reports that detail how keywords, ads, and campaigns contribute to conversions. This information allows you to make informed decisions regarding your advertising strategy.

Data-driven attribution, on paper at least, is the future for conversion tracking. While I’m not a fan of fewer options for tracking conversions, I do support data-driven attribution.

What is the last-click attribute?

The good news for advertisers who use traditional attribution is that the last-click model has not been axed…yet. Last-click Conversion Tracking is a simplified Google Ads attribution model that assigns credit to the user’s last click on an ad before they convert. It means that even if the user clicks on several ads from different campaigns or keywords during the customer journey, only the last click will be considered to be responsible for the conversion.

Last-click attribution is a way to attribute clicks.

This is how the last-click model works and why it’s still in use despite traditional attribution models being removed:

1. User Interaction: The user interacts with several touchpoints in relation to your ads. They might, for example, click on an advertisement in a search engine result, see a display advertising, and then return directly to your website through a bookmark.
2. Conversion Event: The final conversion event is when the user converts. This could be a purchase or signing up for your newsletter.
3. Credit Assignment: Last-click Attribution assigns all credit for the conversion to the last click, which brings the user to the website. In the above example, the direct visitor would receive 100% credit for conversion.

Last-click attribution: pros and cons

The pros and cons of tracking conversions using the last-click attribute are listed below.

Pros

• Simplicity Last click attribution is simple. It gives a simple and clear view of the ads or keywords that are causing immediate conversions.
• History: The last-click model has been used as the most common attribution method for many years. It’s a familiar model for many advertisers and is the default in most reporting platforms.
• Data accessibility: In certain cases, particularly for smaller advertisers and those with limited tracking capability, the last-click attribute may be the only option available due to data limitations.
• Alignment to direct response goals: Businesses focused on direct response advertising or immediate conversions may find that last-click attribution aligns well with their plans.

You can also find out more about the Cons.

• Not suitable for complex journeys: In today’s digital world, customer travels can be difficult and involve multiple touchpoints on various devices and channels. The last-click attribution is incomplete because it ignores all clicks except the final one.
• Unfair credit distribution: This can reward the last clicked ad even if previous clicks were crucial in the user’s decision-making process.
• Budget misallocation: Relying on the last-click attribute can lead to a budget misallocation since you could overinvest in campaigns or keywords that perform only because they are last in the click pathway.

Last-click attribution continues to be used despite these limitations because it is familiar and easy for users to implement.