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:
Default attribution models
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.
Get my best selling books on Attribution Modelling
Learn to implement attribution modelling in your organisation
Understand the customer purchase journey across devices
Determine the most effective marketing channels for investment
Click book covers to find out more
Different types of default attribution models in Google Analytics
Google Analytics provides eight different types of attribution models. They are:
Last interaction attribution model (popularly known as the last touch attribution model)
First interaction attribution model (popularly known as the first touch attribution model)
Linear attribution model
Time decay attribution model
Position based attribution model
Last non-direct click model
Last Ad Click
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:
I read a blog post on your website.
After 3 days I saw your display ad on a website
After 2 days I read a review of your product on some website.
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.
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.
During the weekend I again searched on Google but this time used a branded keyword and clicked on your organic search listing.
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.
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.
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.
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.
Get my best selling books on Attribution Modelling
Learn to implement attribution modelling in your organisation
Understand the customer purchase journey across devices
Determine the most effective marketing channels for investment
Click book covers to find out more
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.
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.
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.
Frequently Asked Questions About Default & Custom Google Analytics Attribution Models
What is an attribution model?
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.
What are default attribution models in Google Analytics?
The default attribution model (also known as 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.
Which attribution models are available in Google Analytics?
Google Analytics provides eight different types of attribution models. They are:
1) Last interaction attribution model (popularly known as last-touch attribution model) 2) First interaction attribution model (popularly known as 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
Which attribution model should I 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.
What are 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.
Register for the FREE TRAINING...
"How to use Digital Analytics to generate floods of new Sales and Customers without spending years figuring everything out on your own."
Here’s what we’re going to cover in this training…
#1 Why digital analytics is the key to online business success.
#2 The number 1 reason why most marketers are not able to scale their advertising and maximize sales.
#3 Why Google and Facebook ads don’t work for most businesses & how to make them work.
#4 Why you won’t get any competitive advantage in the marketplace just by knowing Google Analytics.
#5 The number 1 reason why conversion optimization is not working for your business.
#6 How to advertise on any marketing platform for FREE with an unlimited budget.
#7 How to learn and master digital analytics and conversion optimization in record time.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and BeyondSECOND EDITION OUT NOW!
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma
Founder, OptimizeSmart.com
Over 15 years of experience in digital analytics and marketing
Author of four best-selling books on digital analytics and conversion optimization
Nominated for Digital Analytics Association Awards for Excellence
Runs one of the most popular blogs in the world on digital analytics
Consultant to countless small and big businesses over the decade
Learn and Master Google Analytics 4 (GA4) - 126 pages ebook
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Cookie
Duration
Description
cookielawinfo-checkbox-analytics
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional
11 months
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy
11 months
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.