When running Google Ads, you want to lower the cost of acquisition and increase your return on ad spend, right? Of course, who doesn’t?
Selecting the right attribution models in Google Analytics helps you get optimal performance and helps you understand what marketing channels are working best for you.
In this article, Attribution models Google Analytics, I’ll show you the 7 attribution models, what they are, and when and how to use them.
Watch the video version of this article below:
Attribution Models Google Analytics OVERVIEW
Sometimes people get the perception that visitors click on your Google ads, find your website, and immediately convert into a sale or lead.
That is not the case, especially lately the time delay and multi visits are more common. People have a very short attention span and it’s not uncommon for someone to be on a quick coffee break, on their phone browsing, and making their first visit with your website, then they come back later perhaps on a desktop to continue the buying journey.
That is a common scenario. It’s normal for people to visit your site several times before converting.
This may mean that visitors may come back 5 days later, then on the 6th day, they may click on a remarketing banner ad and finally convert from the banner ad click.
3 touchpoints contributed to getting that conversion and with multiple touchpoints in the buyer journey, each channel plays its part. So in a scenario like this where multi touches are involved, which marketing channel gets the credit?
The attribution models in Google Analytics will analyze and give credit to the touchpoints, or marketing channels that deserve credit for the conversion.
7 attribution models in Google Analytics
1. Last Interaction Attribution
Also referred to as “last-click” or “last-touch”. As the name implies, this model gives 100% of the credit to the last interaction your business had with a lead before they convert.
For example, a visitor finds your website through organic search. A week later they see a Facebook ad and click the ad. Later that day, they go to your website directly and make a purchase.
The direct traffic, in this instance, gets all of the credit for that purchase. 100% of the value is assigned to that last interaction.
This is the default setting in Google Analytics attribution. If you are looking at standard conversion reports in Google Analytics, you’re seeing each goal attributed to the last interaction your customer had with your business.
Pros & Cons:
Last Interaction attribution is the simplest to implement and evaluate.
It is also often the most accurate. Digital marketing today is scattered. People may access from multiple devices, clear cookies, or use multiple browsers. This makes it difficult to track their entire journey.
However, you can always be certain of their last interaction before converting.
The downside is that this model ignores everything that happens before the final interaction. Many of the interactions and touchpoints before that last click will be just as important.
This model may be a good fit for you if you have a short buying cycle or there aren’t many touchpoints before converting. Tracking the last one will give you a good idea of your strongest channels.
2. Last Non-Direct Click
The last non-direct click model is a bit more helpful than a standard last-click model. 100% of the value is still assigned to a single interaction. But, with the last non-direct click, it eliminates any “direct” interactions that occur right before the conversion.
Direct traffic is when anyone goes directly to your site by manually entering your URL or clicking a bookmarked link. So this visitor already knows about your company.
How did they learn about your company? What prompted them to go to your website directly? By eliminating direct traffic in a last-click model, you can better assign value to the marketing strategy that led to the conversion.
Pros & Cons
By eliminating the direct clicks, you get much better insights with this than the last interaction attribution. However, this model assigns 100% of the value to one interaction only.
If your customer had 4 touchpoints before that last non-direct click, and you want to know what that journey was, this model will ignore them and so this is not an ideal model when you want to track the journey.
3. First Interaction Attribution
The first interaction is similar to the last interaction, in that it gives 100% of the credit to one-click/interaction. First Interaction or also called “the first click”, gives all of the credit for a conversion to your business’ first interaction with the customer.
By default, Google Analytics uses 30 days window. For example, if a customer first finds your business on Facebook, and then 4 days later saw a banner ad and clicked that ad on their mobile device on their lunch break, and in the evening went directly to your site from a desktop computer, the very first interaction that happened on Facebook will get 100% of the credit for any sale that happened within the 30-day window.
All those touchpoints along the way that contributed towards the sale don’t matter with this attribution model. The first interaction (in this case Facebook) will get 100% of the credit.
Pros & Cons:
The benefit of using first interaction attribution is it’s simple and straightforward as it is. But this model ignores the effects of any possible marketing that help assist to achieve your goal.
This model can be useful if your industry has a short buying cycle like emergency plumbing or locksmith services, wherein the first touchpoint is especially important. But outside of short cycles like that, I don’t use this model very often.
4. Linear Attribution
With a linear attribution model, you split credit for a conversion equally between all the interactions the customer had with your business.
For example, a customer finds you on LinkedIn and opts into your email marketing list. A week later, you send an email and he clicks on the email link, goes to your website, and makes a $60 purchase.
You had multiple touchpoints. In this case, every touchpoint will get ⅓ of the credit, or a $20 conversion value attributed to the marketing channels when the purchase was made.
Pros & Cons:
Linear attribution gives you a more balanced look at your whole marketing strategy than a single-event attribution model does.
However, this means it also assigns equal importance to everything. Some marketing strategies are more effective than others, and this model will not highlight the most effective strategies.
If you want something simple to understand and you don’t need to balance marketing channel budgets, then linear attribution is a simple and good choice for you.
5. Time Decay Attribution
Time decay attribution is similar to linear attribution. It spreads out the value across multiple events. But unlike linear attribution, the time decay attribution also takes into consideration when the touchpoint occurred.
Interactions that occur closer to the time of purchase have more value attributed to them. The first interaction gets less credit, while the last interaction will get the most.
Pros & Cons:
If relationship building is a big factor in a business’ success, using time decay attribution can be a helpful way to conceptualize that.
It is helpful when you’re dealing with a particularly long sales cycle, such as for expensive B2B purchases.
6. Position-Based Attribution
The position-based attribution model or also called U-shaped attribution splits the credit for a sale between a prospect’s first interaction with your brand and the moment they convert to a lead.
40% of the credit is given to each of these points, with the remaining 20% spread out between any other interactions that happened in the middle.
For example, if a prospect first makes contact with your business through a Google search, looks at your Facebook page, and later signs up for your email newsletter, the first and third touches each receives 40% of the credit, and the Facebook visit receives the remaining 20%.
Pros & Cons:
Position based is a strong attribution model for many business types that have multiple touchpoints before a conversion. It gives partial credit to each of the marketing channels along the journey. But most of the weight goes to what some consider the two most important interactions: the first touchpoint, or how a customer found you and the interaction that caused them to convert.
This model works well for people who don’t need to get so micro involved in the numbers. It’s easy to manage and easy to read reports. You can see the mid touchpoints; they all get credit. Unlike the models that give 100% of the credit, you can give credit across the board and have some really good results with it.
7. Custom or Data-Driven Attribution
Custom attribution or also called algorithmic or data-driven attribution models give you a real in-depth analysis of the customer journey.
You can use it to index every marketing channel that played a role in bringing visitors to your website and converting them into customers.
Compared to other models’ focus on extremes, this model uses algorithms to give each channel the credit it deserves. It helps you better evaluate the performance of each click and interaction.
There is no defined process for the data-driven attribution model. It can vary from organization to the marketing processes. It means you don’t have to be limited to one of the predefined models, but you can set up a model that fits your needs.
First, you’ll need to analyze your historical Google Analytics data, identify your end goals, and evaluate each channel on par with their effectiveness in converting.
We use this model most often because it gives us the ability to assign the most credit to the best channels and eliminates underperforming ones.
Where to find attribution model reports in Google Analytics?
Google Analytics attribution models use the last interaction attribution as the default. However, you can compare different attribution models in your account. You’ll find this tool under Conversions > Multi-channel functions > and Model comparison tool section is shown below:
You can compare models side-by-side. You can see the value each that each of the different marketing channels has in different attribution models.
In the attribution models in Google Analytics Console, the organic search traffic attributed in the last interaction model was 6,703. But the first interaction was 8,803. That’s a 24% difference from using last interaction attribution vs first interaction attribution.
By looking at both models, we can see the importance of the other marketing channels that led to direct traffic and conversions.
Google Analytics attribution models are a very helpful tool for helping you understand what marketing channels are working and not working.
A question I’m often asked is, “Which attribution models in Google Analytics are the best for me to use?” Well, there is no “best” attribution model, it depends on your business type, your goals, and or your buying cycles.
All those factors will vary and so an attribution model for someone else’s business may not be the right one for your business.
Don’t limit yourself to one. Measure data and find the model that works best for your business and your goals and optimize it.