If you have a business model where each interaction is equally important for your conversions then you can use the linear interaction model.
For example, if you run a customer support service then each interaction with your customers is equally important for you. In that case, you can use the linear model.
The following examples will help you to understand how the conversion credit is calculated in the case of the linear attribution model:
Consider the following conversion path with a path length of four:
This conversion path can also be represented by the following data table:
Under the linear attribution model, the conversion credit for each interaction would be calculated as 1 / conversion path length.
Since the length of our conversion path is 4, the conversion credit for each interaction would be calculated as 1 / 4 = 25%:
Campaign A gets 25% credit for the conversion.
Campaign B gets 50% (25% + 25%) credit for the conversion.
Campaign C gets 25% credit for the 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
Consider the second conversion path with a path length of three:
This conversion path can also be represented by the following data table:
Under the linear attribution model, the conversion credit for each interaction would be calculated as 1 / conversion path length.
Since the length of our conversion path is 3, the conversion credit for each interaction would be calculated as 1 / 3 = 33.33%
Campaign A gets 66.66% (33.33%+33.33%) credit for the conversion.
Campaign B gets 33.33% credit for the conversion.
Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).
The 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
= (25% + 66.66%) / 2
= 45.83%
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
= (50% + 33.33%) / 2
= 41.67%
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
= (25% + 0%) / 2
= 12.5%
Consider the third conversion path with a path length of two:
This conversion path can also be represented by the following data table:
Since the length of our conversion path is two, the conversion credit for each interaction would be calculated as 1 / 2 = 50%
Campaign A gets 50% credit for the conversion.
Campaign B gets 50% credit for the conversion.
Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).
The 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
= (25% + 66.66% + 50%) / 3
= 47.22%
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
= (50% + 33.33% + 50%) / 3
= 44.44%
The 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
= (25% + 0% + 0%) / 3
= 8.33%
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 linear attribution model would be: 47.22% + 44.44% + 8.33% = 99.99% (rounded off to 100%)
Since the sum of the conversion credit for three campaigns is 100%, this proves that our conversion credit calculations are all correct.
Other articles on Google Analytics Attribution Models
"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 All”, 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.