# Conversion Credit distribution for Attribution Models in Google Analytics

Following article is an excerpt from my best selling book: **Attribution Modelling in Google Analytics and Beyond **which** **I am sharing it here for the first time, to benefit the wider audience:

In the present article I will show you, how Google Analytics distribute conversion credit to various touch points in a conversion path for different attribution models.

This insight will greatly improve your understanding of various GA attribution models.

But before we move forward, there is one caveat.

This article is bit advanced.

It is advanced in the sense that, it assumes that you already have a working knowledge of attribution modelling in GA.

Attribution Modelling itself is quite an advanced topic in GA for many but the present article takes this subject to a new level.

I won’t be explaining the very basics of attribution modelling in the present article.

So if you are brand new to attribution modelling then read this article first: **Beginners Guide to Google Analytics Attribution Modeling**.

In order to get the most out of the present article make sure you understand:

## Conversion Credit Calculations for Last interaction attribution model

The ‘Last interaction attribution model’ (also known as last touch attribution model) assign 100% credit for conversion to the last interaction in a conversion path.

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

Following example will help you in understanding how the conversion credit is calculated in case of last interaction attribution model.

**Consider following conversion path of ‘path length’ 4**:

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

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

Here,

Campaign ‘A’ gets 0% credit for conversion.

Campaign ‘B’ gets 0% credit for conversion.

Campaign ‘C’ gets 100% credit for conversion.

**Consider second conversion path of path length 3:**

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

Under last interaction model, the last touch gets 100% credit for conversion.

So conversion credit distribution would look like the one below:

Here,

Campaign ‘A’ gets 100% credit for conversion.

Campaign ‘B’ gets 0% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion (as campaign ‘C’ is not in the conversion path)

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path) /2 = (0% + 100%) / 2 = 50%

Total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path) /2 = (0% + 0%) / 2 = 0%

Total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path) /2 = (100% + 0%) / 2 = 50%

**Consider third conversion path of length 2:**

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

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

Here,

Campaign ‘A’ gets 0% credit for conversion.

Campaign ‘B’ gets 100% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path + Conversion credit for campaign ‘A’ in 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’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path + Conversion credit for campaign ‘B’ in the third conversion path) /3 = (0% + 0%+ 100%) / 3 = 33.33%

The total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path + Conversion credit for campaign ‘C’ in the third conversion path) /3 = (100% + 0% + 0%) / 3 = 33.33%

If we assume that there are only three conversion paths in our selected time period, then the sum of conversion credit for campaign ‘A’, ‘B’ and ‘C’ under 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 prove that my conversion credit distribution calculations are all correct.

## Conversion Credit Calculations for First interaction attribution model

The ‘First interaction attribution model’ (also known as First touch attribution model) assign 100% credit for conversion to the first interaction in a conversion path.

Following example will help you in understanding how the conversion credit is calculated in case of first interaction attribution model.

**Consider following conversion path of ‘path length’ 4:**

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

Here,

Campaign ‘A’ gets 100% credit for conversion.

Campaign ‘B’ gets 0% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

**Consider second conversion path of path length 3:**

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

Here,

Campaign ‘A’ gets 0% credit for conversion.

Campaign ‘B’ gets 100% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path) /2 = (100% + 0%) / 2 = 50%

Total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path) /2 = (0% + 100%) / 2 = 50%

Total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path) /2 = (0% + 0%) / 2 = 0%

**Consider third conversion path of length 2:**

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

Here,

Campaign ‘A’ gets 100% credit for conversion.

Campaign ‘B’ gets 0% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path + Conversion credit for campaign ‘A’ in the third conversion path) /3 = 100% + 0% + 100%) / 3 = 66.67%

The total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path + Conversion credit for campaign ‘B’ in the third conversion path) /3 = (0% + 100% + 0%) / 3 = 33.33%

The total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path + Conversion credit for campaign ‘C’ in the third conversion path) /3 = (0% + 0% + 0%) / 3 = 0%

If we assume that there are only three conversion paths in our selected time period, then the sum of conversion credit for campaign ‘A’, ‘B’ and ‘C’ under first interaction attribution model would be = 66.67% + 33.33% + 0% =100%

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

## Conversion Credit Calculations for **Linear attribution model**

The ‘Linear attribution model’ assign equal credit for conversion to each interaction in a conversion path.

Following example will help you in understanding how the conversion credit is calculated in case of linear attribution model.

**Consider following conversion path of path length 4:**

Under 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, so conversion credit for each interaction would be calculated as: 1/4 = 25%

Here,

Campaign ‘A’ gets 25% credit for conversion.

Campaign ‘B’ gets 50% credit for conversion.

Campaign ‘C’ gets 25% credit for conversion.

**Consider second conversion path of path length 3:**

Under 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, so conversion credit for each interaction would be calculated as: 1/3 = 33.33%

Here,

Campaign ‘A’ gets 66.66% credit for conversion.

Campaign ‘B’ gets 33.33% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path) /2 = (25% + 66.66%) / 2 = 45.83%

Total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path) /2 = (50% + 33.33%) / 2 = 41.67%

Total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path) /2 = (25% + 0%) / 2 = 12.5%

**Consider third conversion path of length 2:**

Since the length of our conversion path is 2, so conversion credit for each interaction would be calculated as: 1/2 = 50%

Here,

Campaign ‘A’ gets 50% credit for conversion.

Campaign ‘B’ gets 50% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path + Conversion credit for campaign ‘A’ in 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’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path + Conversion credit for campaign ‘B’ in 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’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path + Conversion credit for campaign ‘C’ in 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 campaign ‘A’, ‘B’ and ‘C’ under 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 prove that my conversion credit calculations are all correct.

## Conversion Credit Calculations for **Position based attribution model**

By default, 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.

Following example will help you in understanding how the conversion credit is calculated in case of position based attribution model.

**Consider following conversion path of ‘path length’ 4:**

Under position based model, both the first interaction and last interaction gets 40% credit for conversion and the remaining 20% credit is equally divided among middel interactions.

So conversion credit distribution would look like the one below:

Here,

Campaign ‘A’ gets 40% credit for conversion.

Campaign ‘B’ gets 20% credit for conversion.

Campaign ‘C’ gets 40% credit for conversion.

**Consider second conversion path of path length 3:**

Under position based model, the conversion credit distribution would look like the one below:

Here,

Campaign ‘A’ gets 60% credit for conversion.

Campaign ‘B’ gets 40% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion (as campaign ‘C’ is not in the conversion path)

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path) /2 = (40% + 60%) / 2 = 50%

Total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path) /2 = (20% + 40%) / 2 = 30%

Total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path) /2 = (40% + 0%) / 2 = 20%

**Consider third conversion path of length 2:**

Under position based model, Campaign ‘A’ gets 40% credit for conversion and campaign ‘B’ gets 40% credit for conversion.

Since there are no middle interactions, the remaining credit of 20% is equally divided about campaign ‘A’ and campaign ‘B’.

So,

- Campaign ‘A’ gets 50% credit for conversion.
- Campaign ‘B’ gets 50% credit for conversion.
- Campaign ‘C’ gets 0% credit for conversion (as campaign ‘C’ is not in the conversion path)

The conversion credit distribution would look like the one below:

So total conversion credit for campaign ‘A’ so far = (Conversion credit for campaign ‘A’ in the first conversion path + Conversion credit for campaign ‘A’ in the second conversion path + Conversion credit for campaign ‘A’ in the third conversion path) /3 = (40% + 60% + 50%) / 3 = 50%

The total conversion credit for campaign ‘B’ so far = (Conversion credit for campaign ‘B’ in the first conversion path + Conversion credit for campaign ‘B’ in the second conversion path + Conversion credit for campaign ‘B’ in the third conversion path) /3 = (20% + 40% + 50%) / 3 = 36.67%

The total conversion credit for campaign ‘C’ so far = (Conversion credit for campaign ‘C’ in the first conversion path + Conversion credit for campaign ‘C’ in the second conversion path + Conversion credit for campaign ‘C’ in the third conversion path) /3 = (40% + 0% + 0%) / 3 = 13.33%

If we assume that there are only three conversion paths in our selected time period, then the sum of conversion credit for campaign ‘A’, ‘B’ and ‘C’ under position based model would be = 50% + 36.67% +13.33% = 100%

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

## Conversion Credit Calculations for **Last non direct click model**

The ‘Last non direct click model’ assign 100% credit for conversions to the last non-direct interaction in a conversion path.

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

Following example will help you in understanding how the conversion credit is calculated in case of last non direct click attribution model. Consider following conversion path of ‘path length’ 4:

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

Under last non direct model, the last non-direct interaction gets 100% credit for conversion.

The last non direct interaction here is ‘Referral’.

So ‘Referral’ gets 100% credit for conversion.

The conversion credit distribution would look like the one below:

## Conversion Credit Calculations for **Last Adwords Click model**

The ‘Last Adwords Click model’ assign all the credit for conversions to the last Google Adwords click in a conversion path.

Following example will help you in understanding how the conversion credit is calculated in case of last Adwords click attribution model.

Consider following conversion path of ‘path length’ 5:

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

Under last Adwords click model, the last Adwords interaction gets 100% credit for conversion.

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

## Conversion Credit Calculations for **Time Decay attribution model**

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

This model takes ‘**half life in exponential decay’ **into account while calculating conversion credit distribution.

Following is the formula to calculate half life in exponential decay:

Source: https://en.wikipedia.org/wiki/Half-life

Here,

N(t)denotes the quantity that has not decayed after a time ‘t’

N(o)denotes the initial amount before measuring decay. In our case this initial amount is 100% conversion credit.

‘t’denotes the time span an interaction is away from conversion.

t ½denoteshalf-lifewhich is the time it takes for a quantity to reduce to half its original value.

By default, the time decay attribution model has a half-life value of 7 days. What that means, a interaction which occurred 7 days prior to conversion gets ½ the credit of the interaction that occurred on the day of conversion.

Consider the following conversion path of path length 3:

Let us assume that interaction with position 1 occurred 14 days ago, interaction with position 2 occurred 7 days ago and interaction with position 3 occurred on the day of conversion.

Under time decay attribution model, the conversion credit for each interaction would be calculated as:

Campaign ‘A’ gets 85.7% (57.1% + 28.6%) credit for conversion.

Campaign ‘B’ gets 14.3% credit for conversion.

Campaign ‘C’ gets 0% credit for conversion.

The actual calculation of the conversion credit distribution in case of time decay model is quite complex and can’t be done or demonstrated manually.

## Conversion Credit Calculations for **Data driven attribution model**

Data driven attribution (DDA) model is an algorithmic attribution model.

It uses data modelling in which predictive algorithms are used to determine marketing touchpoints (both online and offline touchpoints) which should get more conversion credit than others.

Consequently, the conversion credit distribution for a DDA model cannot be demonstrated manually.

## Other articles on Attribution Modelling in Google Analytics

- Touch Point Analysis in Google Analytics Attribution Modelling
- 8 Google Analytics Conversions Segments You Must Use
- Default and Custom Attribution Models in Google Analytics
- Attribution Model Comparison Tool in Google Analytics
- Which Attribution Model to use in Google Analytics?
- How to create Custom Attribution Model in Google Analytics

- How to do ROI Analysis in Google Analytics
- Google Analytics Attribution Modelling – Complete Guide
- Guide to Data Driven Attribution Model in Google Analytics
- Conversion Credit distribution for Attribution Models in Google Analytics
- You are doing Google Analytics all wrong. Here is why

- Marketing Mix Modelling or Attribution Modelling. Which one is for you?
- Introduction to Nonline Analytics – True Multi Channel Analytics
- How to set up Data driven attribution model in Google Analytics
- How to valuate Display Advertising through Attribution Modelling
- Understanding Shopping Carts for Analytics and Conversion Optimization

- View-through conversion tracking in Google Analytics
- Understanding Missing Touch Points in Attribution Modelling
- Guide to Offline Conversion Tracking in Google Analytics
- How to explain attribution modelling to your clients
- 6 Keys to Digital Success in Attribution Modelling

- How to use ZMOT to increase Conversions and Sales exponentially
- How to Measure and Improve the Quality of SEO Traffic through Google Analytics
- How to analyse and report the true value of your SEO Campaign
- How to allocate Budgets in Multi Channel Marketing
- What You Should Know about Historical Data in Web Analytics

- Google Analytics Not Provided Keywords and how to unlock and analyze them
- Selecting the Best Attribution Model for Inbound Marketing
- Introduction to TV attribution in Google Analytics Attribution 360
- Cross Device Reports in Google Analytics via Google Signals
- Data-Driven Attribution Model Explorer in Google Analytics
- What is Attribution Modelling and why it is the ‘key’ to online business success?
- How Does Attribution Work?
- How is Attribution Modelling helpful for e-commerce and non-e-commerce websites?

## Do you want to Learn and Master Web Analytics in 8 Weeks?

I get dozens of emails every week from people asking for career advice in web analytics.The questions range from ‘where do I start?’, ‘which books should I read?’, ‘how do I prepare for GAIQ?’ to how do I become a good analyst?

So I thought why not dedicate a whole video on answering these burning questions.

**Here is what we are going to cover, in the next few minutes, in this video lesson:**

#1 Who am I? What’s my story and why you should listen to me?

#2 The number 1 reason, why most marketers and business owners are not able to scale their advertising and maximize sales for themselves and/or for their clients.

#3 How in just couple of years, I went from a complete novice in web analytics to the author of three best selling books on web analytics? And how you can achieve the same transformation in your career and that too in much shorter time frame.

#4 What is the difference between web analytics and Google Analytics?

## Take your analytics knowledge to the next level. Check out my best selling books on Amazon

**Attribution Modelling in Google Ads and Facebook** NEW

This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. Learn how to leverage the knowledge of attribution modelling, understand the customer purchasing journey and determine the most effective marketing channels for investment.

**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 Beyond**

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.