Conversion Credit Distribution for Attribution Models in Google Analytics

The 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:

Attribution Modelling in Google Ads and FacebookAttribution Modelling in Google Analytics and Beyond

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  • Learn to implement attribution modelling in your organisation
  • Understand the customer purchase journey across devices
  • Determine the most effective marketing channels for investment

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In the present article, I will show you how Google Analytics distributes conversion credit to various touchpoints 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 a 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. 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) assigns 100% credit for the conversion to the last interaction in a conversion path.

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

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

 

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

conversion-path1

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

table1

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

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.

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Consider the second conversion path of path length 3:

conversion-path2

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

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:

distribution2

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 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 the third conversion path of length 2:

conversion-path3

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

table3

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

distribution3

Campaign ‘A’ gets 0% credit for the conversion.

Campaign ‘B’ gets 100% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 campaigns ‘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 proves 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 the first touch attribution model) assigns 100% credit for the conversion to the first interaction in a conversion path.

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

 

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

conversion-path1

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

distribution1

Campaign ‘A’ gets 100% credit for the conversion.

Campaign ‘B’ gets 0% credit for the conversion.

Campaign ‘C’ gets 0% credit for the conversion.

 

Consider the second conversion path of path length 3:

conversion-path2

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

distribution2

Campaign ‘A’ gets 0% credit for the conversion.

Campaign ‘B’ gets 100% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 the third conversion path of length 2:

conversion-path3

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

distribution3

Campaign ‘A’ gets 100% credit for the conversion.

Campaign ‘B’ gets 0% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 campaigns ‘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 the linear attribution model

The linear attribution model assigns equal credit for the conversion to each interaction in a conversion path.

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

 

Consider the following conversion path of path length 4:

conversion-path1

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

distribution1

Campaign ‘A’ gets 25% credit for the conversion.

Campaign ‘B’ gets 50% credit for the conversion.

Campaign ‘C’ gets 25% credit for the conversion.

 

Consider the second conversion path of path length 3:

conversion-path2

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

distribution2

Campaign ‘A’ gets 66.66% credit for the conversion.

Campaign ‘B’ gets 33.33% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 the third conversion path of length 2:

conversion-path3

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

distribution3

Campaign ‘A’ gets 50% credit for the conversion.

Campaign ‘B’ gets 50% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 campaigns ‘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 proves that my conversion credit calculations are all correct.

Conversion credit calculations for the 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.

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

 

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

conversion-path1

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

So conversion credit distribution would look like the one below:

distribution1

Campaign ‘A’ gets 40% credit for the conversion.

Campaign ‘B’ gets 20% credit for the conversion.

Campaign ‘C’ gets 40% credit for the conversion.

 

Consider the second conversion path of path length 3:

conversion-path2

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

distribution2

Campaign ‘A’ gets 60% credit for the conversion.

Campaign ‘B’ gets 40% credit for the conversion.

Campaign ‘C’ gets 0% credit for the 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 the third conversion path of length 2:

conversion-path3

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

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

  • 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 in the conversion path)

The conversion credit distribution would look like the one below:

distribution3

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 campaigns ‘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 proves that my conversion credit calculations are all correct.

Conversion credit calculations for the 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. 

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

conversion-path

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

table1

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

The last non-direct interaction here is ‘Referral’. So ‘Referral’ gets 100% credit for the conversion.

The conversion credit distribution would look like the one below:

distribution1

Conversion credit calculations for last adwords click model

The last Adwords click model assigns all the credit for conversions to the last Google Adwords click in a conversion path.

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

Consider the following conversion path of ‘path length’ 5:

conversion-path

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

table

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

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

distribution

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:

half-life

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 ½ denotes half-life which 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, an interaction that 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:

conversion-path

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 the 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 the conversion.

Campaign ‘B’ gets 14.3% credit for the conversion.

Campaign ‘C’ gets 0% credit for the conversion.

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

Conversion credit calculations for the data-driven attribution model

The 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. 

Attribution Modelling in Google Ads and FacebookAttribution Modelling in Google Analytics and Beyond

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

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Himanshu Sharma

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Himanshu helps business owners and marketing professionals in generating more sales and ROI by fixing their website tracking issues, helping them understand their true customers' purchase journey and helping them determine the most effective marketing channels for investment.

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