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Google Ads Attribution Models (Last-Click VS Data-Driven)

Google’s decision to reduce the number of attribution models got a lot of marketers wondering how to transition to the new models.

In this guide, we’ll dive into the shift toward simplified Google Ads attribution models and prepare you to transition to new models.

Understanding Attribution Models

In the context of Google Ads, understanding attribution models is fundamental for advertisers who aim to accurately assign credit to various touchpoints along a customer’s path to conversion.

Attribution models are essentially frameworks that allow marketers to determine how much credit each click or interaction receives for a conversion.

This is critical in a digital environment where a customer might interact with multiple ads from the same company before making a purchase or completing a desired action.

The Role of Attribution Models

At its core, an attribution model provides insights into the effectiveness of different advertising efforts across a campaign.

By analyzing which ads, keywords, or campaigns have the greatest impact on your business goals, you can make more informed decisions about where to allocate your budget and how to strategize your ad placements.

It’s about moving beyond the simplistic view of giving all the credit to the last click before conversion and acknowledging the contribution of all touchpoints that led to that final action.

Types of Attribution Models

Currently, there are 2 types of attribution models available in Google Ads:

  1. Last-Click Attribution: This model gives all the credit to the last ad or touchpoint that the customer clicked before converting.
  2. Data-Driven Attribution (DDA): DDA uses advanced machine learning algorithms to dynamically assign credit to each touchpoint based on its actual contribution to the conversion. It analyzes both converting and non-converting paths, adjusting the attribution credit of each touchpoint across the entire customer journey.

Historically, Google Ads offered 6 attribution models:

  1. First-Click Attribution
  2. Last-Click Attribution
  3. Linear Attribution
  4. Time Decay Attribution
  5. Position-Based Attribution
  6. Data-Driven Attribution (DDA)

The Shift to Simplified Attribution Models


In an effort to more accurately reflect the complexities of consumer interactions with digital advertising, Google has revised its approach to attribution in Google Ads.

This revision saw the platform narrowing down its attribution model offerings to two primary models: Data-Driven Attribution (DDA) and Last-Click.

Reasoning Behind Removing Other Models

The decision to remove the First-Click, Linear, Time Decay, and Position-Based models was not taken lightly.

Google analyzed usage patterns and feedback from advertisers, finding that these models were either underutilized or did not offer the depth and flexibility required to accurately measure and attribute conversions in today’s complex digital landscape.

  • First-Click: While valuable for understanding initial engagement, this model was limited in its ability to account for all subsequent interactions that lead to a conversion.
  • Linear: This model’s equal attribution to all touchpoints often oversimplified the contribution of each interaction, not adequately reflecting the varying degrees of influence that different ads have on the final conversion.
  • Time Decay: Although closer to recognizing the importance of timing in the conversion path, this model still did not fully account for the qualitative differences between interactions.
  • Position-Based: This model, while offering a more nuanced view than First-Click or Last-Click by recognizing the importance of the initial and final interactions, still fell short of capturing the full complexity of the consumer journey.

The shift to DDA and Last-Click is grounded in Google’s commitment to providing advertisers with tools that are not only more reflective of consumer behavior but also backed by robust data analysis capabilities.

DDA, in particular, offers a dynamic, algorithmic approach that adjusts credit based on the observed impact of each ad interaction, leveraging machine learning to offer precise, tailored insights into how advertising efforts contribute to conversions.

Implications for Advertisers

The transition to simplified attribution models encourages advertisers to adopt a more holistic view of their marketing strategies.

By reducing the focus on single interactions and instead evaluating the contribution of all touchpoints, businesses can gain a clearer understanding of their customers’ journey.

This not only allows for more informed decision-making regarding ad spend allocation but also enables the optimization of campaigns to better meet the needs and behaviors of their target audience.

Moreover, the emphasis on Data-Driven Attribution reflects a broader trend in digital marketing toward leveraging big data and analytics for strategic insights.

As DDA requires access to a significant amount of data to function effectively, it underscores the importance of comprehensive data tracking and analysis in modern advertising efforts.

Last-Click Attribution vs Data-Driven Attribution Model

Given the recent simplifications Google has made to its attribution models, understanding the nuances of the remaining models is crucial for advertisers aiming to optimize their Google Ads campaigns effectively.

The focus now primarily lies on two models: Last-Click Attribution and Data-Driven Attribution (DDA).

Each model offers distinct perspectives on attributing conversions and understanding customer interactions with ads.

Last-Click Attribution

The Last-Click Attribution model is straightforward: it attributes 100% of the conversion credit to the last ad click before the conversion happens.

This model has been the default for many advertisers due to its simplicity and direct approach. It’s particularly useful for short sales cycles or when the final click is believed to significantly influence the conversion decision.

However, this model has limitations as it overlooks the contribution of all prior interactions, potentially leading to a skewed understanding of what truly drives conversions.

For businesses with a direct and uncomplicated path to conversion, where the last interaction can reasonably be deemed the most decisive factor, Last-Click Attribution can still provide valuable insights.

It enables advertisers to identify and focus on the ads, keywords, or campaigns that are directly leading to conversions, allowing for optimization of these final touchpoints to improve performance.

Data-Driven Attribution (DDA)

The Data-Driven Attribution model represents a more advanced approach, leveraging machine learning to analyze the conversion path and assign credit to various touchpoints based on their actual influence on the conversion.

Unlike Last-Click or any other rule-based model, DDA doesn’t adhere to a fixed attribution rule. Instead, it dynamically assesses all clicks in the conversion path, taking into account factors such as order of exposure, ad interaction, and the time between each interaction and the conversion.

DDA requires a significant amount of data to accurately model and attribute conversions, making it particularly suited for advertisers with high volumes of clicks and conversions.

The model’s strength lies in its ability to provide a granular view of the conversion path, revealing insights into how each touchpoint contributes to conversions.

This allows advertisers to optimize their campaigns across the entire customer journey rather than focusing solely on the last click.

For campaigns aimed at driving awareness or engagement early in the customer journey, DDA offers a sophisticated method to understand and credit these earlier interactions accurately.

It supports more strategic resource allocation and bidding strategies by highlighting the true value of each touchpoint in driving conversions.

Choosing the Right Attribution Model

Selecting the appropriate attribution model is crucial. It affects how you evaluate the performance of different marketing channels and make decisions about where to invest your advertising budget.

While DDA offers a nuanced understanding of the customer journey, it requires access to a substantial amount of data to be effective.

In contrast, Last-Click offers clarity and simplicity, particularly for businesses with shorter sales cycles or less complex paths to conversion.

Preparing for the Transition to New Models

Data Analysis and Collection

If you’re moving to DDA, ensure your Google Ads account has sufficient and accurate conversion data. DDA’s effectiveness hinges on the quality and volume of data it can analyze. You might need to revisit your conversion tracking setup to ensure all relevant interactions are being captured.

Training and Team Alignment

Ensure your team understands the differences between the models and the rationale behind the transition. This might involve training sessions or workshops to familiarize them with DDA’s intricacies, especially if this model represents a significant departure from your current approach.

Test and Learn

Consider running a test phase, if possible, where you compare the outcomes of campaigns using both Last-Click and DDA side by side. This approach can offer insights into how the change might affect campaign performance and ROI, allowing you to make informed decisions about a broader rollout.

Implementing the Change

Switching attribution models in Google Ads is straightforward from a technical perspective—usually just a few clicks in the platform settings. However, the strategic implications are significant. Monitor your campaigns closely in the weeks following the transition. Pay special attention to changes in campaign performance, ROI, and how different channels and touchpoints are valued under the new model.

Analyzing and Optimizing with Attribution Models

When it comes to analyzing and optimizing your Google Ads campaigns with attribution models, the process is both a science and an art. It requires a deep understanding of both your customer journey and how different touchpoints influence conversions. Whether you decide to use Last-Click or Data-Driven Attribution (DDA), each model provides unique insights that can inform your marketing strategies and decisions.

Analyzing Campaign Performance with Attribution Models

The first step in utilizing attribution models for analysis is to access and interpret the data provided by your chosen model within Google Ads. For Last-Click Attribution, this means focusing on the final touchpoints that lead directly to conversions, analyzing which keywords, ad groups, or campaigns are performing best at closing sales or achieving desired actions.

On the other hand, DDA offers a broader view, attributing credit across multiple touchpoints based on their contribution to the conversion. This requires a more nuanced analysis, looking at how early and mid-funnel interactions contribute to conversions, in addition to the last click. It’s important to analyze the performance of different channels and campaigns not just in isolation, but in how they interact and support each other across the customer journey.

Optimizing Campaigns Based on Insights

Once you have a clear understanding of how different touchpoints contribute to conversions, the next step is to optimize your campaigns based on these insights. For campaigns operating under Last-Click Attribution, optimization might focus on increasing bids on high-performing keywords or ad placements that are consistently the last interaction before a conversion.

For those using DDA, optimization could involve a broader range of actions. Since DDA considers the entire conversion path, you might find opportunities to optimize earlier touchpoints that play a significant role in leading customers to the final conversion. This could involve adjusting bids on keywords that start the customer journey or reallocating budget to campaigns that assist conversions, even if they’re not the final click.

Continuous Improvement

Optimization is not a one-time task but an ongoing process. As market conditions change, new products are introduced, or consumer behavior shifts, the effectiveness of different touchpoints may also change. Regularly review the performance data provided by your attribution model to identify new optimization opportunities. Additionally, consider conducting controlled experiments, such as A/B testing different ad creatives or landing pages, to directly measure the impact of changes and refine your approach based on empirical evidence.

Leveraging Technology

Utilizing advanced tools and features within Google Ads, such as automated bidding strategies, can further enhance your optimization efforts. These tools use machine learning to automatically adjust bids in real-time based on a multitude of factors, including the likelihood of conversion. When combined with DDA, automated bidding can dynamically allocate your budget to maximize conversions across the entire customer journey, not just at the final touchpoint.


Moving to the simplified attribution models makes it a decision of either Last-Click or Data-Driven Attribution (DDA).

While Last-Click offers simplicity and directness, beneficial for straightforward conversion paths, DDA affords a nuanced view of the customer journey, attributing credit to touchpoints across the entire path to conversion based on their actual influence.

Continue learning with our guide to Google Ads ad rotation.

Michael Schroder

Michael Schroder

Michael Schroder is a Google Ads and SaaS marketing consultant. He has been managing $200k-$300k monthly ad spend and has worked with 200+ SaaS companies. The thing that makes him unique is his data-led approach and his focus on SaaS businesses.