Last Interaction Attribution Model in Google Analytics Explained

The last interaction attribution model (also known as the last touch attribution model) assigns 100% credit for a conversion to the last interaction on a conversion path

Google Analytics uses this model by default for multi-channel funnel (MCF) reports. There is a common misconception that the last touch attribution model is innately bad or flawed. And that when you do attribution modelling you must abandon the last touch model. But this is not true. 

For example, let us suppose you are an FMCG (fast-moving consumer goods) company like Tesco or Walmart. Since you sell products (like toothpaste) that involve the least amount of buying consideration, your customers’ conversion paths tend to be short. 

So you do not need to assign more conversion credit to the first and middle interactions on your conversion paths. Consequently, you can use the last touch attribution model. So the last touch model has its own use and place. 

The following examples will help you in understanding how the conversion credit is calculated in the case of the last interaction attribution model:

Example-1: Consider the following conversion path with a path length of four:

conversion path1

This conversion path can also be represented by the following data table:

data table1

Here your customer was exposed to various campaigns in the following order before the conversion occurred on your website: 

Campaign A > Campaign B > Campaign B > Campaign C.

Under the last interaction model, the last touch gets 100% credit for the conversion. So conversion credit distribution would look like the one below:

conversion credit distribution1
  • Campaign A gets 0% credit for the conversion.
  • Campaign B gets 0% credit for the conversion.
  • Campaign C gets 100% credit for the conversion.
attribution modelling

Get the E-book (52 Pages)

Example-2: Consider the second conversion path with a path length of three:

conversion path2

This conversion path can also be represented by the following data table:

data table2

Under the last interaction model, the last touch gets 100% credit for the conversion. So conversion credit distribution would look like the one below:

conversion credit distribution2 1
  • Campaign A gets 100% credit for the conversion.
  • Campaign B gets 0% credit for the conversion.
  • Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).

Total conversion credit for campaign A so far

 = (conversion credit for campaign A on the first conversion path + conversion credit for campaign A on the second conversion path) / 2 

= (0% + 100%) / 2 

= 50%

Total conversion credit for campaign B so far 

= (conversion credit for campaign B on the first conversion path + conversion credit for campaign B on the second conversion path) /2 

= (0% + 0%) / 2

= 0%

Total conversion credit for campaign C so far 

= (conversion credit for campaign C on the first conversion path + conversion credit for campaign C on the second conversion path) /2 

= (100% + 0%) / 2 

= 50%

Example-3: Consider the third conversion path with a path length of two:

conversion path3

This conversion path can also be represented by the following data table:

data table3

Now the conversion credit distribution would look like the one below:

Last Interaction Attribution Model in Google Analytics
  • Campaign A gets 0% credit for the conversion.
  • Campaign B gets 100% credit for the conversion.
  • Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).

Total conversion credit for campaign A so far

= (conversion credit for campaign A on the first conversion path + conversion credit for campaign A on the second conversion path + conversion credit for campaign A on the third conversion path) /3  

= (0% + 100% + 0%) / 3 

= 33.33%

The total conversion credit for campaign B so far 

= (conversion credit for campaign B on the first conversion path + conversion credit for campaign B on the second conversion path + conversion credit for campaign B on the third conversion path) /3  

= (0% + 0%+ 100%) / 3 

= 33.33%

Total conversion credit for campaign C so far 

= (conversion credit for campaign C on the first conversion path + conversion credit for campaign C on the second conversion path + conversion credit for campaign C on the third conversion path) / 3 

= (100% + 0% + 0%) / 3

= 33.33%

Now if we assume that there are only three conversion paths in our selected time period, then the sum of conversion credit for campaigns A, B and C under the last interaction attribution model would be 33.33% + 33.33% + 33.33% = 99.99% (rounded off to 100%). 

Since the sum of the conversion credit for three campaigns is 100%, this proves that our conversion credit distribution calculations are all correct. 

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
error: Alert: Content is protected !!