Google’s constant drive for automation over advertiser control is evident in its platform updates. Often, this means retiring older features, even if they are widely used, to make way for newer, supposedly simpler ones. The recent removal of first-click, linear, time decay, and position-based attribution models exemplifies this trend.

Google announced their removal earlier this year, and now, these attribution models are no longer available. Any existing conversions using these models will be switched to the data-driven attribution model. What implications does this have for advertisers moving forward? This article will explore these changes and their potential impact on your Google Ads strategy.

Table of contents

• Traditional attribution models
• What is data-driven attribution?
• What is last-click attribution?
• What’s the future of Google Ads attribution models?

Traditional attribution models

Before discussing the implications, let’s examine how traditional attribution models tracked conversions and their differences from the remaining data-driven and last-click models:

• Last click (still available): This model gives full credit to the user’s last interaction with an ad before converting.
• First click: First-click attribution assigns all credit to the initial interaction in the customer journey, disregarding any subsequent interactions.
• Linear: The linear model distributes credit equally among all touchpoints in the customer journey.
• Time decay: Time decay attribution prioritizes interactions closer to the conversion, assigning them more credit, while earlier interactions receive less.
• Position-based: This model emphasizes the first and last interactions, giving them greater weight, while middle interactions receive less. Are you concerned about wasted spending in Google Ads? Get a free, instant audit to find out » Google Ads Performance Grader

What is data-driven attribution?

To understand Google’s decision to retire traditional attribution models, let’s delve into the data-driven attribution model. Data-driven conversion tracking in Google Ads is a sophisticated method for tracking and attributing conversions to specific keywords, ads, and campaigns. It leverages historical data and machine learning algorithms to provide advertisers with more precise insights into the effectiveness of their advertising efforts.

How data-driven attribution works

Here’s a breakdown of how data-driven attribution functions:

1. Data collection: Google Ads gathers extensive data related to user interactions with your ads and website. This includes click data, on-site user behavior, and conversion data such as purchases, form submissions, and phone calls.
2. Machine learning algorithms: Google utilizes machine learning algorithms to analyze this data, identifying patterns and trends. Factors like time of day, device type, and location are considered to understand conversion drivers.
3. Attribution modeling: Data-driven conversion tracking employs advanced attribution models to assign value to different touchpoints in the customer journey, considering the entire path, including multiple ad interactions before a conversion.
4. Conversion prediction: Google Ads predicts the likelihood of a conversion for each ad click based on historical data and machine learning insights. This helps determine which clicks are more likely to convert.
5. Optimization: This predictive data allows Google Ads to optimize your bidding strategy, potentially adjusting bids in real-time to allocate more budget to higher-converting keywords and ads, maximizing ROI.
6. Performance reporting: Google Ads provides detailed performance reports showing how different keywords, ads, and campaigns contribute to conversions, informing your advertising strategy. In theory, data-driven attribution represents the future of conversion tracking. While reducing tracking options isn’t ideal, data-driven attribution offers a potentially superior method for tracking results. Is your Google Ads account optimized for success? Discover now » The Last Guide to Google Ads Account Structure You’ll Ever Need

What is last-click attribution?

Fortunately for those who prefer traditional attribution models, last-click hasn’t been eliminated…yet. For the unfamiliar, last-click conversion tracking in Google Ads is a simple attribution model that attributes a conversion entirely to the last ad the user clicked before converting. Regardless of any other ads clicked during the customer journey, only the final click is credited for the conversion.

How last-click attribution works

Let’s examine how last-click attribution works and why it remains in use despite the removal of other traditional models:

1. User interaction: A user interacts with your ads through multiple touchpoints, such as clicking a search ad, viewing a display ad, and later revisiting your website directly through a bookmark.
2. Conversion event: The user eventually converts, perhaps by making a purchase, subscribing to a newsletter, or completing a form.
3. Credit assignment: Last-click attribution assigns 100% of the conversion credit to the final click that led the user to your website. In the example above, the direct visit would receive full credit.

Pros and cons of last-click attribution

Here are the advantages and disadvantages of using last-click attribution for conversion tracking.

Pros

• Simplicity: Last-click attribution is easy to understand, providing a clear view of which ads or keywords drive immediate conversions.
• Historical use: As a long-standing default and widely used attribution model, many advertisers are familiar with last-click attribution, and it’s the default setting in many reporting platforms.
• Data availability: Last-click attribution may be the only feasible option for advertisers with limited tracking capabilities or smaller data sets.
• Alignment with direct response goals: This model aligns well with businesses focused on direct response advertising and immediate conversions.

Cons

• Inadequate for complex journeys: Last-click attribution fails to capture the nuances of today’s complex customer journeys, which often involve multiple touchpoints across various channels and devices, providing an incomplete picture of user behavior.
• Unfair distribution of credit: By ignoring the influence of earlier touchpoints, last-click attribution can unfairly favor the final clicked ad, even if previous interactions significantly influenced the user’s decision.
• Misallocation of budget: Solely relying on last-click attribution can result in misallocated ad spend, potentially overinvesting in keywords or campaigns that appear to perform well solely due to their position at the end of the click path. Despite these drawbacks, last-click attribution persists due to its familiarity and ease of implementation.

What’s the future of Google Ads attribution models?

Currently, Google offers the choice between data-driven and last-click attribution models. Your preference and how you wish to view performance data may determine your choice. Contrary to some experts, I generally favor last-click attribution for its simplicity and directness in lead generation. It allows for clear tracking of which keyword and ad generated each lead. I find that overly scattered attribution and fractional conversion credits can confuse many clients. Click, lead, opportunity, sale – this is a clear path. However, I’ve also been utilizing data-driven attribution for a while now. I anticipate it becoming the only option eventually, recognizing that it might contribute to more intelligent, machine learning-driven bidding. To some extent, embracing these advancements in artificial intelligence and digital advertising is necessary, regardless of how forced or unnecessary they may seem. Ultimately, the key is selecting the available attribution model that best allows you to measure, track performance, and achieve your marketing goals.