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Google Analytics Attribution Modeling 101

Tyson Kirksey  |  October 30, 2012

An exciting anouncement was made yesterday at the Google Analytics Partner Summit. Attribution Modeling Reports, which up until this point have been exclusively available to GA Premium customers, will now be available to all GA users.

The Attribution Modeling reports build upon the framework of the Multi-Channel Funnel reports that you already can access in Google Analytics today. These reports help you determine how much value different marketing channels or campaigns are providing by looking at all visits prior to a conversion (within 30 days).

With this announcement every GA user will have access to seven pre-defined models, along with the ability to create your own custom model. Remember, there is no one model that is more correct or more accurate; each organization should determine what makes sense depending on factors like the length of a sales cycle and which channels are in the mix.

Let's look more closely at each of these models and the strengths and weaknesses of each one.

Understanding the Default Attribution Model in GA

By default, Google Analytics gives credit to the last non-direct traffic source prior to a conversion. This approach uses cookies which remember traffic sources for up to six months. Here's an example:

Default Attribution Model_Google Analytics Attribution Modeling_Vertical Nerve blog


Drawbacks of the Standard Model


Standard Attribution Model_Google Analytics Attribution Modeling_Vertical Nerve blog


The last-click model described above is commonly accepted as a good approach among many, but no single model fits all organizations. Furthermore, some campaigns and marketing channels tend to attract visitors who are higher in the buying funnel (display and social, for example), while others are more transactional (email & search). This means a last-click attribution model may skew ROI numbers and misrepresent the value provided by upper-funnel channels like display and social. 

Furthermore, specific types of segments inside of a channel can skew data one way or another as well. For example, inside a search channel, you may have non-brand phrases and brand phrases which behave very differently.

Consider this common example:

  • A shoe shopper sees a banner ad highlighting new shoes and clicks through to
  • During the visit, the shopper decides to purchase the shoes but gets interrupted and does not complete the purchase.
  • Four hours later, the same user goes to and searches for "" (a common navigational search) to return to the site.
  • During this second visit, the user completes the purchase.

Attribution Model_Google Analytics Attribution Modeling_Vertical Nerve blog

In this common scenario, a last-click model would attribute 100% of the credit to “google /organic.” Any reasonable person, however, would conclude that the banner ad, which brought the user to the site in the first place, was much more responsible for the ultimate purchase. This is the drawback that custom attribution modeling reports in Google Analytics can help to solve. Now let's look at some of the available models.

Last Interaction

The Last Interaction model attributes 100% of the conversion value to the last channel with which the customer interacted before buying or converting. Google Analytics uses this model by default when attributing conversion value in non-Multi-Channel Funnel reports.

When it's useful: Because the Last Interaction model is the default model used for non-Multi-Channel Funnel reports, it provides a useful benchmark to compare with results from other models. In addition, if your ads and campaigns are designed to attract people at the moment of purchase, or your business is primarily transactional with a sales cycle that does not involve a consideration phase, the Last Interaction model may be appropriate.

First Interaction

The First Interaction model attributes 100% of the conversion value to the first channel with which the customer interacted.

When it's useful: This model is appropriate if you run ads or campaigns to create initial awareness. For example, if your brand is not well known, you may place a premium on the keywords or channels that first exposed customers to the brand.


The Linear model gives equal credit to each channel interaction on the way to conversion.

When it's useful: This model is useful if your campaigns are designed to maintain contact and awareness with the customer throughout the entire sales cycle. In this case, each touch point is equally important during the consideration process.

Time Decay

If the sales cycle involves only a short consideration phase, the Time Decay model may be appropriate. This model most heavily credits the touch points that occurred nearest to the time of conversion.

When it's useful: If you run one-day or two-day promotion campaigns, you may wish to give more credit to interactions during the days of the promotion. In this case, interactions that occurred one week before have only a small value compared to touch points near the conversion. The Time Decay model allows you to appropriately credit touch points during the days leading up to conversion.


The Position-Based model allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction, and assign 20% credit to the interactions in the middle.

When it's useful: If you most value touchpoints that introduced customers to your brand and final touchpoints that resulted in sales, use the Position Based model.

So Which One is Right for Me?

Again, there is no right or wrong model for every business; the important thing is to understand how each one works. I'm particularly a fan of time-decay and position-based, but I also usually customize them to discount brand keyword searches or direct access visits.

Tyson Kirksey headshot

Tyson Kirksey

Tyson began working in the SEO field in 2003, when tags really mattered and Yahoo! nearly bought Google (oops!). Along with his vast experience in search engine optimization, Tyson has knowledge of programming and web development. He has successfully managed high-profile accounts including LaQuinta Hotels, Compass Bank, Pizza Hut and MetroPCS. He is certified with Google Analytics, Google AdWords and Microsoft AdCenter. He is a graduate of Harding University with a bachelor’s in Interactive media. In his spare time, he enjoys spending time with his family and playing golf.

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