Why Google Ads Attribution Models are Important
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.
2. Optimization: Using the right attribution model is also essential for optimizing advertising campaigns. This is true from both a bid strategy perspective – since Google will use conversion data to optimize campaigns that are on automated bid strategies – as well as for advertisers making manual campaign optimizations based on their conversion data. Different attribution models may reveal insights into which keywords and ads are most effective at driving conversions.
Guide to Google Ads Attribution Models
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
- First-click attribution
- Position-based attribution
- Linear attribution
- Time decay attribution
- Data-driven attribution
1. Last-click Attribution Model
How It Works
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.
- Pros: Simple and easy to implement. This model provides insight into how channels perform on a basic level
- Cons: Ignores all touchpoints except the last one. For this reason, it may not provide a comprehensive overview of the customer journey and the value of how other channels and campaigns contribute to conversions.
2. First-Click Attribution Model
How It Works
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.
- Pros: Provides insight into the customer's initial touchpoint with the brand. This is useful for businesses that focus on brand awareness and the campaigns that are the best at introducing users to the business.
- Cons: Ignores all touchpoints except the first one, so as with last-click attribution, it may not provide a comprehensive view of the customer journey.
3. Position-Based Attribution Model
How It Works
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.
- Pros: Awards credit to touchpoints at the beginning and end of the customer journey, which reflects the idea that these touchpoints are the most influential.
- Cons: This model doesn’t take into account touchpoints in the middle of the customer journey. If a user clicks on 10 of your keywords over a period of time before the purchase, nothing will be attributed to the 8 keywords in the middle.
4. Linear Attribution Model
How It Works
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.
- Pros: Distributes credit equally across all touchpoints in the customer journey, providing a more comprehensive view of performance.
- Cons: Though this model is slightly more insightful than the previous 3 models, and fairer at distributing credit, linear attribution might not accurately reflect the impact of each touchpoint. For example, the first touchpoint may be low-intent compared to the middle and last touchpoints that are high-intent, meaning the middle and last touchpoints might deserve more credit when trying to accurately determine the effectiveness of ad campaigns.
5. Time Decay Attribution Model
How It Works
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.
- Pros: Awards more credit to touchpoints that are closer to the conversion, which reflects the idea that recent touchpoints are the most influential. This attribution model can offer more insight than last-click attribution and provide a more accurate understanding of performance since credit is given to preceding touchpoints.
- Cons: This model may either ignore early touchpoints, or not accurately credit the impact of earlier touchpoints, preventing a true reflection of performance.
6. Data-Driven Attribution Model
How It Works
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.
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.
Data-Driven Attribution Use Case Example
Here’s an example of how DDA works in practice:
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.
2. Select Home Warranty is a provider of household warranty for repair projects in the United States. Using data-driven attribution, they saw a +36% increase in leads and a -20% decrease in CPA.
3. H.I.S. is a global travel agency that operates in over one hundred cities around the world. Using DDA, Smart Bidding and Dynamic Search Ads, H.I.S were able to drive a +62% increase in the number of conversions at the same CPA.
Data-Driven Attribution Data Requirements
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:
- High-value actions: Conversion actions that have a higher value to your business, such as purchases, leads, or sign-ups, may generate fewer conversions or ad interactions than lower-value actions like pageviews or video views.
- Niche products or services: Conversion actions related to niche products or services may have a smaller audience, resulting in fewer conversions or ad interactions.
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.
How Do I Choose An Attribution Model 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.
How to Switch to Data-Driven Attribution
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.
How to Improve your Data-Driven Attribution model
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:
- Adjust bids according to DDA-based conversions by analyzing the conversion data DDA begins attributing to your campaigns.
- Since DDA will measure ad interactions and clicks more accurately throughout the entire path to conversion, go back and review keyword performance to see how keywords earlier in the path are impacting conversions.
- When using data-driven attribution, the recommended approach is to adopt a smart bidding strategy such as Target CPA or Target ROAS. Read a practical guide to Google Ads bidding strategies here.
- Give DDA a couple of weeks to collect and analyze user interaction and conversion data. This period of learning is important and even more so for businesses with longer paths to conversion.
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 about a powerful data analytics tool - Google Ads Data Hub