Attribution modeling is the identification process Google uses to determine how to assign credit for conversions through different clicks and conversion paths. While it’s common for users to go through multiple stages of a conversion funnel before taking the final step towards conversion, attribution modeling provides a deeper level of understanding of how each interaction contributes to a conversion.

Read on to learn about several different attribution modeling options in Google Ads and the pros and cons of each.

Overview of Google Attribution Models


Last Click Attribution 

Last Click attribution gives full attribution credit to the last ad a user clicks on. For example, if a user is searching for your product, clicks on an ad, and visits through other channels in between, Google will assign 100% credit to that final Google ad click.

  • Pros: Last Click attribution is a simple and straightforward approach that takes away confusion and gives insight to the source and campaign that is the final driver of users completing a conversion action.
  • Cons: This attribution model doesn’t show the full picture of a user’s path to conversion, so other sources and campaigns don’t get the credit they deserve. Last Click ignores all other efforts taken to get the conversion, which is why it’s a common trend to see more branded campaign conversions with the Last Click model, as users often go directly to the site via a branded keyword/ad when they are ready to complete a conversion action.

First Click Attribution 

First Click attribution gives full credit to the first ad click interaction. This attribution model is set up similarly to Last Click attribution, in which Google assigns 100% credit to a single action a user takes.

  • Pros: First Click attribution allows you to see how people are discovering your brand. It’s a simple approach that’s easy to implement and understand.
  • Cons: Similar to Last Click attribution, First Click attribution doesn’t show the full picture so other sources/campaigns don’t get the credit they deserve. In the multi-touch conversion process, any click that follows the first click isn’t recognized at all, which gives an inaccurate representation of the conversion process.

Linear Model

The Linear attribution model assigns even credit to all Google ad touchpoints involved in a conversion path. For example, if a user clicked on a non-branded ad, then a remarketing display ad, and then finally completed a conversion action with a branded ad, all actions are granted equal credit (33.3% each) for the conversion.

  • Pros: This attribution model shows much more of the full picture, which allows marketers to analyze all the steps taken to complete conversion actions. This also allows optimization for the entire customer journey, as opposed to a single action.
  • Cons: Because equal credit is distributed between all actions in a multi-touch conversion, it could skew some of the data and grant more or less credit than is deserved to certain clicks. This method also makes it difficult to optimize for the specific keywords and campaigns since it assigns an even weight to all actions.


The Position-Based attribution model gives credit to all touchpoints along the conversion journey by assigning 40% credit to the first click, 40% to the last click, and the other 20% amongst the rest. This attribution model is also referred to as “U-Shaped Attribution.”

  • Pros: The Position-Based attribution model shows more of the full picture of the entire conversion journey because of its weighted approach. It recognizes that the most important steps in the customer conversion journey are generally the first and last clicks, while still giving credit to the clicks in between.
  • Cons: This attribution model can undervalue the touchpoints in between the first and last clicks. A remarketing ad could have been the reason a user kept coming back, but once they’re finally ready to complete a conversion, he or she may have clicked on a branded ad. In this example, the fact that the remarketing ads didn’t get as much credit demonstrates somewhat of a skewed approach.

Time Decay

The Time Decay attribution model assigns credit based on the time: the touchpoints closest in time to the conversion get most of the credit. This multi-touch attribution model acknowledges that different touchpoints have different values, and it assigns credit by assigning each action with more credit than the previous action.

  • Pros: This method puts more emphasis on actions that are lower down the funnel, as more credit is assigned to the clicks closest to conversion. This attribution model can give great insight to advertisers who have a long sales process, as the funnel often replicates the steps users take between a certain time period.
  • Cons: Similar to the Position-Based attribution model, the Time Decay attribution model isn’t exactly accurate in weighting importance and distributing conversion credit. The first click may have been the original way a user has heard of a brand, but if they don’t end up converting for another month, that click will not get the credit it deserves.

Data Driven

Data Driven attribution is Google’s newest attribution model that assigns credit based on how people search for a product or service using the data behind it. Google does this by comparing the click plaths of customers who convert to those who don’t and identify patterns to assign attribution credit to each conversion.

  • Pros: Data Driven attribution is generally Google’s most accurate attribution model, as it takes multiple factors into account when assigning attribution credit. Each model is specific to each account, and once it collects enough data, is a very beneficial way of measuring performance and optimizing accounts.
  • Cons: Google has a strict criteria in place to allow Data Driven attribution to run. This is currently only suitable for high traffic accounts that receive a large volume of conversions (15,000 clicks and 600 conversions within a 30-day period). In addition to the high threshold an account must maintain for this attribution model to apply, it is a relatively complex model and difficult to explain as Google doesn’t provide much insight on how attribution credit is assigned.

Choosing an Attribution Model

When it comes to choosing an attribution model, ultimately what matters most is that you have the data you need to achieve your goals. We generally recommend using a position-based model — or data-driven if you meet the minimum criteria — for insights into the various touchpoints in a user’s conversion journey. However, if your main goal is to increase awareness and drive new customer growth, you might want to consider a first-touch model, or if you have a long sales process, you might want to try a time-decay model.

No matter what, if you’re looking to switch your Google Ads conversion model, we highly recommend looking at the Model Comparison report in the Google Ads Attribution tab. This report allows you to compare how conversions would be distributed across your campaigns for each of the different models available.

Attribution models can give you a better understanding of how your specific campaigns, ads, and keywords perform and can help you optimize across the user’s conversion path. For information on effective advertising and diving deeper into the data of Google Ads, contact our team!