Google to Sunset Four Attribution Models in Ads and Analytics: Here’s What You Need to Know

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Attribution models are frameworks used to analyze and assign credit to different marketing touchpoints throughout a customer’s journey, from awareness through to conversion. They help determine the effectiveness of various marketing channels and campaigns in driving conversions or sales.

Attribution models are important for several reasons:

  1. Optimize marketing strategies: By understanding which touchpoints are most effective in driving conversions, businesses can optimize their marketing strategies to allocate resources and budget more efficiently.
  2. Measure campaign effectiveness: Attribution models help businesses measure the success of their marketing campaigns and efforts, allowing them to identify high-performing channels and tactics.
  3. Improve customer journey: Analyzing customer touchpoints helps businesses understand how customers interact with their brand and enable them to refine the customer journey, improving overall user experience and engagement.
  4. Better ROI: By identifying the most effective marketing channels and tactics, businesses can focus on those that deliver the best return on investment (ROI), maximizing their marketing spend.
  5. Informed decision-making: Attribution models provide valuable insights into marketing performance, enabling data-driven decision-making and improving overall marketing outcomes.

Google has announced that four attribution models would disappear in both Google Ads and Google Analytics. Those attribution models impacted are:

  1. First Click: Attribution model that assigns 100% credit to the first touchpoint in the customer journey, ignoring all other touchpoints.
  2. Linear: Attribution model that evenly distributes credit among all touchpoints in the customer journey, giving equal importance to each interaction.
  3. Time Decay: Attribution model that assigns more credit to touchpoints closer to the conversion, with earlier interactions receiving less credit.
  4. Position-Based: Attribution model that assigns a majority of credit to the first and last touchpoints in the customer journey, with the remaining credit distributed evenly among other interactions

What Does the Sunsetting Mean for Advertisers?

The sunsetting models assign value to each advertising touchpoint based on predefined rules. However, these models don’t provide the flexibility needed for modern campaigns and evolving consumer journeys. Today, less than three percent of Google Ads conversions are attributed using first-click, linear, time decay, or position-based models.

Advertisers who still use these models will be impacted by this change. Models other than Last Click will be difficult to track as the data-driven formula is account-specific and isn’t visible.

Attribution Models Sunset Timeline.

In June, Google will not allow advertisers to choose First Click, Linear, Time Decay, and Position-Based attribution models. After September, those attribution models will sunset. Post-sunset, any conversion action using the deprecated models will automatically convert to the data-driven attribution model. Advertisers may opt to choose the alternative Last Click.

  • What To Do In May – Accounts that are using any of the affected models should start conversations about how this change might affect campaign performance.
  • What To Do In June – Use the Model Comparison Tool in the Attribution Tab to identify fundamental changes in measurement or tracking that might help drive strategy shifts.
  • What To Do In September – Google will officially sunset the four attribution models in both Google Ads & Google Analytics. The four models affected will be removed from the Model Comparison Report and the Google Ads Overview page. Manually change accounts from the data-driven model to Last Click if the campaign strategy calls for it.

Note: External attribution will not be impacted.

What is Data-Driven Attribution?

Data-driven attribution (DDA) is a type of attribution model used in Google Ads that utilizes machine learning to analyze and assign conversion credit to various touchpoints throughout a customer’s journey. This model takes into account the actual contribution of each marketing touchpoint in driving conversions, based on historical data from your account.

Unlike other attribution models, which rely on predefined rules to distribute conversion credit, data-driven attribution considers the unique interactions of customers with your ads and adjusts the credit distribution accordingly. By analyzing the actual impact of each touchpoint, DDA provides a more accurate representation of your marketing channels’ performance, helping you make more informed decisions about budget allocation and campaign optimization.

To use data-driven attribution in Google Ads, your account must meet certain requirements, such as a minimum number of conversions within a specific time frame, to ensure that there is enough data for the machine learning algorithms to analyze effectively.

The Pros and Cons of Data-Driven Attribution.

With the Data-Driven Attribution model, advertisers want to know how this will affect their measurable insights and what DDA might mean for the future of their campaigns.

Pros

  • Removes Human Error: Uses Google AI to understand the unique impact of each consumer touchpoint, completely removing human bias from the equation to identify patterns that lead to conversions.
  • Higher Efficiency Spend: Combining DDA with an automated bid strategy allows for better allocation of spend toward ads that statistically more likely to drive conversions and business value.
  • Partial and Fractional Conversions: With DDA, conversion “credit” is divided between each consumer touchpoint, allowing for partial credit across multiple campaigns or for fractional web attribution.

Cons

  • Invisible Attribution Model: DDA does not provide insights or data on what percentages or formulas are used to credit conversions toward touchpoints, but instead is an invisible algorithm built on account history.
  • Limited User Journey Stats: DDA is not immune to the tracking difficulties of cross-browser and cross-device journeys. And, though more privacy-centric, is limited by cookie policies.

Data-Driven Attribution analyzes all relevant data and consumer interactions about the consumer journey leading up to a conversion. DDA takes multiple signals into account, including ad format and time between an ad interaction and a conversion.

The model then gives proportional credit to the most valuable interactions on the consumer’s path.

What You Can Do Now

  1. Plan Ahead: Utilize the Google Ads Model Comparison Tool to assess whether DDA or Last Click is a better choice for your business. DDA accounts for partial conversions, as opposed to giving conversion data in whole numbers (e.g. Last Click). If your marketing efforts are cross-channel, Google has upgraded fractional attribution for web conversions from non-Google properties, allowing you to plan for more complete funnel attribution.
  2. Stay Informed: If conversion volume has been a gating factor for switching to DDA, we suggest moving forward as Google has removed previous data requirements (300 conversions or 3,000 clicks). Since DDA is privacy-centric, do also stay informed on what type of insights you may gain/lose in the switch. Stay up to date on expanded Google support for more conversion types — including in-app and offline conversions — as well as Discovery formats such as PMax.
  3. Continue Evaluating: With the majority of advertisers already using DDA, this is not a substantial shift for most accounts. However, we recommend periodically testing that your chosen model is getting the most accurate results for your business. DDA is not a silver bullet solution for all advertisers, but we believe that it provides better insight into how engagement points impact the consumer journey and allows your business to allocate budget and resources more effectively.



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