Determining which Google Ads attribution model to use has long been a challenge for advertisers, and it remains so in 2024. Understanding how customers interact with your campaigns and which touchpoints drive conversions is crucial to measuring the effectiveness of advertising. Google Ads attribution models play a significant role in this process, assigning credit to each touchpoint in a customer's journey.
There are six Google Ads attribution models, each with its own advantages and disadvantages. From last-click attribution to data-driven attribution and everything in between, they all provide a unique perspective on how credit is assigned.
In this article, we'll examine the six Google Ads attribution models to help you make an informed decision about which attribution model is best for your business.
Studies show that consumers engage with a product at least eight times before purchasing, and it takes 7-13+ engagements with your business before a lead converts. Therefore using the right attribution model is fundamental for businesses to understand how channels and campaigns perform in relation to all of those touchpoints.
Here are two key reasons why choosing the right attribution model is important:
1. Understanding: Attribution models help businesses understand performance. While the perfect attribution model may not exist (although some claim that data-driven attribution is the closest thing), choosing the right one can lead to a more accurate understanding of performance. This, in turn, leads to better decision-making as regards marketing strategy and advertising spend.Source: Louisaustin.co
Let's take a look at the six Google Ads attribution models available and find out which one is right for you, exploring the pros and cons of each attribution model.
Last-click attribution, as the name suggests, gives all the credit to the last touchpoint before converting. Last-click attribution is straightforward and commonly used, however, there has been a shift in recent years for the need to focus on more than just the last click, taking into account the multiple touchpoints throughout a customer’s journey.
For example, a conversion path might consist of multiple touchpoints, starting with generic keywords, followed by Display and Video ad interactions, and ending with a conversion taking place from branded keywords. In this example, the brand keyword will get all of the credit. However, you could argue the generic keyword that introduced the customer to the business played a role in the conversion or is equally as important as the brand keyword the conversion is attributed to. The same could be said for the video and display interactions.
Perfect for businesses that have few touchpoints with users before a conversion takes place, such as e-commerce businesses with a short sales cycle.
First-click attribution gives all credit to the first touchpoint that a customer interacts with before converting. It is similar to that of last-click attribution, just the other way around. In the example above, the generic keyword that first introduced a user to the business would take all of the credit, disregarding the middle and bottom funnel interactions.
Perfect for businesses that focus on brand awareness and discovery and would like to give credit to channels and campaigns that introduce users to their business.
Position-based attribution gives more credit to the first and last touchpoints that a user interacts with before converting. For example, a generic search campaign may drive some initial interest and later on, the user converts after clicking on a Display Retargeting ad. Position-based attribution will credit both the Search and Display campaigns for having a role in the conversion.
Perfect for businesses that have a mix of branding and direct response campaigns and would like to share the attribution between the first and last touchpoint.
Linear attribution distributes credit equally across all touchpoints in a customer's journey. If there were 3 clicks, then each of these touchpoints would be attributed with a third of the conversion.
Perfect for businesses that want to consider all touchpoints and those that have longer sales cycles and multiple interactions before their customers convert.
Time decay attribution awards more credit to touchpoints that occur closer in time to the conversion event. The most amount of credit will be given to the final touchpoint before a conversion, followed by the touchpoint before that, and so on.
Consider this scenario: a user first clicks on a generic keyword and visits a product page. They are then served video retargeting ads over the course of a week and finally search for the product, clicking on a shopping ad and purchasing. In this example, time decay attribution will accord a larger portion of the credit to the shopping ad, followed by the video campaign, and finally the least amount of credit to the generic keyword.
Perfect for businesses that have shorter sales cycles, but still have multiple touchpoints in their customer journey. It could also be good for businesses with time-sensitive touchpoints.
Data-driven attribution, also known as DDA, is the newest attribution model and one that Google recommends adopting, providing your account meets certain criteria. But you may be wondering how Google Ads data-driven attribution model gives credit for conversions.
Data-driven attribution uses advanced machine learning to analyze data and decide how important each touchpoint is in a customer's journey. Conversions are broken up and attributed to each touchpoint based on its influence and impact on a customer converting.
Source: windsor.ai
Clicks and video engagements are analyzed across Search (including Shopping), YouTube, Display, and Discovery ads in Google Ads to identify patterns that lead to conversions. When using automated bidding, not only do these patterns support DDA to assign conversions, but they will also help the bid strategy leverage data and patterns that lead to conversions to find customers that behave in a similar way. This is what makes data-driven attribution the most advanced attribution model.
Perfect for businesses with complex conversion paths and those that have multiple touchpoints as well as any eligible business with an abundance of data that would like to benefit from machine learning. Since it uses advanced algorithms to decipher data and attribute conversions, DDA can provide better clarity over a campaign, ad group, keyword and ad performance making it a good choice for most accounts.
Pros: Uses machine learning to assign credit to touchpoints based on their impact on conversions. This means it provides a more accurate view of the customer journey.
Cons: Requires a lot of data to function and its fundamental that conversion tracking is accurate. This may prevent businesses with little conversion data and accounts with tracking issues from adopting this attribution model.
Here’s an example of how DDA works in practice:
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An ecommerce beauty brand has the primary goal of selling lipsticks online using Google Ads. Data-driven attribution model finds that on average there are multiple clicks before a purchase is made. DDA also finds that users who first search for lipstick shades, such as ‘coral red lipstick’, and later click on a brand keyword, were the most likely to purchase. Whereas users who search for ‘discount’ and ‘cheap’ related keywords first and click on brand keywords afterwards are the least likely to convert. This results in DDA assigning more credit to color related keywords, ad groups and campaigns lower down the funnel, which is also reflected in reporting. |
DDA uses machine learning and provides more clarity over which clicks are the most impactful, regardless of when the click happened in a user journey. As well as having a better understanding of performance, a recent study involving hundreds of advertisers using DDA revealed that performance improved when compared to last-click attribution.
Here are 3 case studies of real businesses using data-driven attribution:
1. Medpex, the largest mail-order pharmacy in Germany, used data-driven attribution together with smart bidding. This resulted in a +29% increase in the number of conversions and a -28% decrease in cost per acquisition.Most conversion actions, such as purchases, sign-ups and app installs, can be used for data-driven attribution. In fact, DDA is now the default attribution model for all new conversion actions you create, although you can manually switch to a different attribution model.
Source: Google Ads Help
For many conversion actions, there’s no minimum volume needed to run DDA. However, for some, you’ll need at least 300 conversions and 3,000 ad interactions within 30 days to be eligible. These conversions may include:
Data-driven attribution can also use in-app conversion events, such as in-app purchases, and attribute them to specific keywords and ads. You can also import offline conversion events such as phone calls, in-store visits, and purchases made in-person and again, these actions can be matched back to Google Ads interactions using identifiers.
For existing conversion events, if your account is eligible Google will notify you via email and at that point, you can adopt data-driven attribution or opt out. You can also check if you are eligible in the Attribution section of your Google Ads account. Read on to find out how to switch to DDA in Google Ads.
In your Google Ads account, navigate to Tools and Settings and then under Measurement, click on Attribution. From here, you can explore various conversion paths and conversion path metrics and look at assisted conversions as well.
Use the Model Comparison feature in the left-hand menu to compare how conversion data in the account would have been attributed for the various attribution models. This tool is great because you can see how conversions would have been assigned without changing models.
The screenshot above is a comparison between last-click attribution and data-driven attribution, using the default look-back window and the 4 conversion events the account tracks. It shows how two important conversion metrics – conversions and cost/conv – would have performed.
Use this feature to review the attribution models you are interested in adopting before making the change, to ensure conversion data aligns with your business goals.
If you are ready to change your attribution model, this is done at conversion level, so head to Tools and Settings and then Conversions. Click on the conversion event you would like to change the attribution model for and then click on Edit Settings.
Under Attribution model, click on the drop-down menu and change to your desired attribution model.
You can switch to data-driven attribution using the same method above. However, in the Attribution section of your Google Ads account, navigate to ‘Switch to DDA’ on the left-hand menu.
From there, you will be able to see all of the conversion actions in the account, the current attribution model they are using, and whether or not they are eligible to switch to DDA.
As seen in the screenshot above, if eligible, you will have the option to make the switch yourself, or if auto-switch has been applied you can either wait for the switch to happen automatically or opt out if you would prefer not to use DDA.
Once you’ve made the switch to data-driven attribution, there are a number of other steps you can follow to get the most out of DDA:
Choose the right Google Ads attribution model by first weighing up the strengths and weaknesses of each of the 6 attribution models, along with using the handy Google Ads comparison tool to understand how each model impacts your business.
By selecting the attribution model that best aligns with your business and goals, you’ll have a more accurate understanding of performance, be able to improve optimization efforts and increase the overall efficiency of your campaign.
Read also abouta powerful data analytics tool - Google Ads Data Hub