Last Updated: February 25, 2022

This article is in conjunction with the article: Beginners guide to Attribution Modelling in Google Analytics, where I introduced the concept of attribution modelling in the context of Google Analytics and explained the various components of attribution modelling in great detail.

So if you have not already read that article, then I would strongly suggest you read it first. The knowledge acquired through that article will help you greatly in understanding the present article.

Introduction to Google Analytics attribution models

An attribution model is a set of rules that determine how credit for conversions should be attributed/distributed to various touchpoints in a conversion path.

In the context of Google Analytics, attribution models can be broadly classified into two categories:

1. Default attribution models
2. Custom attribution models

Introduction to default attribution models in Google Analytics

The default attribution model (also known as the baseline model) are pre-built models that define how credit for conversion should be distributed to various interactions (or touchpoints) in a conversion path before the custom credit rules are applied.

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Different types of default attribution models in Google Analytics

Google Analytics provides eight different types of attribution models. They are:

1. Last interaction attribution model (popularly known as the last touch attribution model)
2. First interaction attribution model (popularly known as the first touch attribution model)
3. Linear attribution model
4. Time decay attribution model
5. Position based attribution model
6. Last non-direct click model
7. Last Ad Click
8. Data-Driven Attribution Model

#1 Last interaction attribution model (last touch attribution model)

The Last interaction attribution model assigns 100% credit for a conversion to the last interaction (or touchpoint) in a conversion path. Google Analytics uses this model by default for Multi-channel funnel reports.

Issues with Last Touch Attribution Model

In the last touch attribution modelling, 100% conversion is attributed to the last acquisition channel/touchpoint.

For example, let us consider that I followed the following conversion path:

1. I read a blog post on your website.
2. After 3 days I saw your display ad on a website
3. After 2 days I read a review of your product on some website.
4. After 4 days I decided to make a purchase. So I made a search using a non branded keyword and clicked on your PPC ad on Google.
5. Just to make sure that I am going to get the best deal, I went to a product comparison site. Being satisfied with your product pricing I decided to make a purchase during the weekend.
6. During the weekend I again searched on Google but this time used a branded keyword and clicked on your organic search listing.
7. I made a purchase from your website.

Here I was exposed to multiple acquisition channels (blog post, display ad, product review, paid search, etc). Each of these exposures is considered a touch. So I was exposed to six different acquisition channels before I made a purchase.

Now according to the last touch attribution model, the conversion (making a purchase) is attributed to organic search.

Most analytics software by default use last touch attribution so they will also report to you that I searched for your website on Google through a branded keyword and then made a purchase. So the acquisition channel responsible for your sale is ‘organic search’.

As you can see from the chart above, this is not true. six acquisition channels have played an important role in the conversion on your website.

Another issue with the last touch attribution model is that it is not truly the last touch as it doesn’t take into account those last touches which happened offline or on devices (like a smartphone) where the online behaviour/conversion can not be easily tied to the person who started the conversion process.

So for example after clicking on the organic search result, I saw an ad in a magazine and then made a purchase. The last touch attribution model in Google Analytics will still give credit for the conversion to organic search as Google Analytics can’t associate me with the magazine ad.

#2 First interaction attribution model

The First interaction attribution model assigns 100% credit for a conversion to the first interaction (or touchpoint) on a conversion path.

Issues with First Touch Attribution Model

In the first touch attribution model, 100% conversion is attributed to the first touch (in my case blog post). So according to this model, I read your blog post and then made a purchase decision on that basis.

This is also not true. I also saw your display ad, read a review of your product, clicked on your PPC ad, visited a product comparison website and clicked on your organic search listing before making a purchase. All these acquisition channels influenced my purchase behaviour.

Just as the last touch attribution can lead to misallocation of resources, over-crediting the first touch can mislead as well. Neither first touch nor last touch provides a good understanding of the buying behaviour.

#3 Linear attribution model

The Linear attribution model assigns equal credit for a conversion to each interaction on a conversion path.

#4 Time decay attribution model

The Time Decay attribution model assign more credit to the interactions which are closest in time to the conversion.

#5 Position-based attribution model

The Position-based attribution model assigns 40% credit to the first interaction, 20% credit to the middle interactions and 40% credit to the last interaction.

#6 Last non-direct click attribution model

The Last non-direct click attribution model assigns all the credit for conversions to the last non-direct click on a conversion path.

Google Analytics uses this model by default for non-Multi channel funnel reports.

#7 The last Google Ads click attribution model

The Last Google Ads click attribution model assigns all the credit for conversions to the last Google Ads click on a conversion path.

#8 Data-driven attribution model

The data-driven attribution model uses an algorithm to assign credit for conversions to various touchpoints on a conversion path. In this model, values are assigned to touches in proportion to their contribution to conversion. 

To learn more about the data driven attribution model, check out this article: Google Analytics Data Driven Attribution Model Tutorial

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Misconceptions about Attribution Models

Many optimizers have declared good and bad attribution models. But there is no good, bad, best or flawed attribution model:

For example, there is a common misconception that the last-touch attribution model is innately bad. That when you do attribution modelling, you must abandon the last-touch attribution model.

Some optimisers even went one step further and declared that last-touch attribution modelling is dead.

No attribution model is good, bad, the best, or flawed. We select an attribution model on the basis of our business model, advertising objectives, and the hypothesis we want to test.

Attribution models are used for testing purposes. They are a means to an end.

For example, you can use the last-touch attribution model if you are an FMCG (fast-moving consumer goods) company like ‘Tesco’ or ‘Walmart’.

This is because with FMCG the least amount of buying consideration is involved. So there is no big need to assign more conversion credit to the first and middle interactions in your conversion path.

So the last-touch attribution model has its own use and place.

Attribution is driven by experiments.

In order to increase ROI across multiple marketing channels, you have to test different types of attribution models all the time.

It is only through continuous testing that you can determine the acquisition channels that deserve the maximum credit for conversions at a particular point in time or product life cycle.

Therefore you should not ignore any attribution model or declare it flawed or useless.

Which attribution model to use in Google Analytics?

There is no one size fit all attribution model. The selection of the attribution model depends mainly on your business model and your advertising objectives.

For example, use the last touch attribution model when the least amount of buying consideration is involved. Use the first touch attribution model, if brand building/brand awareness is very important for you.

To learn more about selecting an attribution model, read this article: Which Attribution Model to use in Google Analytics?

How to view default attribution models available to you in Google Analytics?

To view the default attribution models available to you, follow the steps below:

Step-1: Navigate to ‘Model Comparison Tool’  (under Conversions > Attribution) in your Google Analytics view:

Step-2: Click on ‘Select Model’ drop-down menu:

You can then see the list of all available default attribution models in your GA view:

Note: If you don’t see ‘data-driven attribution’ model in the ‘Select Model‘ dialog box and you use GA premium/360, then it means this model is not enabled for your GA view.

Read this article, to enable this model: How to set up Data-driven attribution model in Google Analytics

Custom attribution models in Google Analytics

As the name suggests these attribution models are developed by people like me and you. They are user-defined attribution models.

When you build your own attribution model, you create your own rules to assign credit to different interactions in a conversion path. These rules are known by the name ‘custom credit rules’ in Google Analytics.

The data-driven attribution model is a good example of a custom attribution model. This model assigns credit to interactions in proportion to their contribution to conversion. This model is not supported by Google Analytics.

You can learn more about creating your attribution models, through this article: How to create a Custom Attribution Model in Google Analytics.

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8. Understanding Missing Touchpoints in Attribution Modelling
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11. Data-Driven Attribution Model in Google Analytics – Tutorial
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16. Introduction to Nonline Analytics – True Multi Channel Analytics
17. Conversion Types Explained in Google Analytics Attribution
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19. Differences Between Google Attribution & Multi-Channel Funnel Reports
20. Introduction to TV Attribution in Google Analytics Attribution 360
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22. Conversion Paths Report Explained in Google Analytics Attribution
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43. Introduction to Attribution Beta – Attribution Project in Google Analytics

Frequently Asked Questions About Default & Custom Google Analytics Attribution Models