How to Create Custom Attribution Model in Google Analytics

This article is related to attribution modelling in Google Analytics. If you are brand new to attribution modelling then I would suggest reading this article first: Google Analytics Attribution Modeling Tutorial

Today I will show you, how to create and use your own attribution model (aka ‘Custom Attribution Model’) in Google Analytics using the model comparison tool.

What is a Custom Attribution Model in Google Analytics?

A custom attribution model is a user-defined attribution model. You can create this model through the Model Comparison Tool. You can create up to 10 custom attribution models per Google Analytics reporting view.

An attribution model is a rule, set of rules or algorithm which is used to determine, how credit for conversions should be attributed/distributed to different touchpoints on a conversion path.

A conversion path can be made up of one or more touchpoints.

Why do you need a Custom Attribution Model?

At this point you may be thinking, what is the point of creating an attribution model when there are already so many default attribution models available in GA? 

The purpose of creating your own attribution model is to valuate your marketing from a different perspective.

When you use a different attribution model, it impacts the valuation of your marketing channels. Through attribution models, you can evaluate the effectiveness of your marketing campaigns.

You can evaluate your assumptions in your conversion path data.

You use the attribution model output, to increase or decrease investment in a marketing channel and then monitor how it affects your conversions and sales over time.

Through attribution models, you can test your assumptions by experimenting. Attribution models are used for experimenting/testing purposes.

For example, under the last-click attribution model, your display advertising may be heavily undervalued because of your unique customer’s purchase behaviour. 

Maybe the majority of your customers are getting influenced by your display ads in their conversion journey but they are not clicking on these ads before making a purchase.

But how can you know for sure whether or not display advertising is undervalued or overvalued without creating and/or comparing an attribution model with the last touch attribution model?

This is where custom attribution models come into the picture. You create a hypothesis and then test it by creating a custom attribution model.

You then compare your custom model with the last touch or some other default attribution model.

The hypothesis you create is based on your analysis and the attribution issues you want to fix.

For example, your hypothesis could be something like:

“If a user completes a goal conversion on my website within 12 hours of viewing (but not clicking) one of my display ads then the display ad impressions should get two times more conversion credit than the other interactions on the conversion path”

You can then test this hypothesis by creating a custom attribution model. 

 

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What are the requirements for creating a Custom Attribution Model in Google Analytics?

The primary technical requirement for creating a custom attribution model in Google Analytics is Goal conversion tracking and/or ecommerce tracking setup

However, in order to get the best results from your custom attribution model, make sure that the following requirements are also met:

#1 You have fixed all major data collection and data integration issues before you create and use a custom attribution model. Make sure that you are tracking all of the website usage data and that data is as accurate as technically possible and you are not facing any data sampling issues.

#2 You are tracking all but only the relevant goal conversions. Track only those goals which are really useful for your business. Irrelevant goals (like ‘time spent on a webpage’) can greatly skew your conversion volume and conversion values, pollute your MCF data and can make your entire attribution modelling flawed.

#3 You are tracking goal conversions with their correct goal value. A goal conversion without a goal value (or economic value) is a bogus conversion as it does not add any value to the business bottomline. 

#4 You have imported cost data from all Google and non-Google accounts in your GA property. You may be getting tons of sales and other conversions but if you do not keep an eye on cost per acquisition then the ‘cost’ can literally kill your marketing ROI. Attribution modelling is most useful when it takes cost into account.

#5 Fix cross-device attribution issues.

If your website has got a considerable amount of cross-device attribution issues then you need to fix them first. Otherwise, your attribution modelling is going to produce flawed results.

This happens because the attribution modelling reports provided by GA, mainly report on single device single browser attributions.

#6 Create your hypothesis in advance before you even think about creating a custom attribution model. We create a custom attribution model in order to test a hypothesis. The hypothesis you create should be based on your data analysis and the attribution issue you are trying to fix.

#7 Your reporting view has collected at least 30 days of historical data so that your attribution analysis can be statistically significant. The historical data should include conversion data, cost data and website usage data. 

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How to create a custom attribution model in Google Analytics?

Follow the steps below to create your own attribution model in Google Analytics:

Step-1: Navigate to the Model Comparison Tool report in your GA reporting view:

model comparison tool google analytics

Step-2: Click on the ‘Select Model’ drop-down menu:

select model drop down menu

Step-3: Scroll down and then click on the link ‘Create new custom model’:

create new custom model

Step-4: Give a name to your custom model and then select the attribution model you want to use as the baseline model from the drop-down menu:

name your attribution model

Here, I have selected ‘Position Based’ as the baseline model.

Related Article: Which Attribution Model to use in Google Analytics?

Note: Google Analytics does not allow you to create a custom attribution model from scratch. The attribution model that you create will be built on top of a default attribution model (also known as the baseline model). 

You use the baseline model as a starting point for your custom model. So before you can create your own attribution model, you would first need to select a baseline model for your custom model.

Step-5: Set conversion credits and specify the lookback window:

set conversion credit set the lookback window

Note: The sum of the conversion credits that you set must be 100%. For example, in the screenshot above, the sum of my conversion credits is 20% + 30% + 50% = 100%

Step-6: Scroll down and then adjust conversion credit for impressions and/or adjust conversion credit based on user engagement and/or apply custom credit rules:

adjust credit for impressions based on user engagement custom credit rules

Note: You will see the option to adjust credit for impressions only when you are using the GA360 enabled reporting view and you have set up view-through conversion tracking.

Step-7: Click on the ‘Save and Apply’ button.

Step-8: Compare your new custom attribution model with other attribution models and valuate your marketing channels and campaigns from different perspectives:

Compare your attribution model with other models

Quick recap of conversion credit and weighting

Conversion credit is the credit given to an interaction/touchpoint on a conversion path for assisting or directly completing a conversion.

This conversion can be a goal conversion or ecommerce conversion. In the context of attribution modelling, a conversion credit is often referred to as credit.

Conversion credit weighting refers to the adjustments made to a conversion credit.

You can change the conversion credit weighting by giving more or less credit to a touchpoint on a conversion path. Conversion credit weighting is often referred to as credit weighting.

What are Conversion credit weighting rules?

Attribution models can use rules to determine how conversion credit should be distributed/adjusted among various touchpoints on a conversion path. These rules are called conversion credit weighting rules or simply credit rules.

There are two types of conversion credit weighting rules:

  1. Default conversion credit weighting rules.
  2. Custom conversion credit weighting rules.

What are the default conversion credit weighting rules (aka default credit rules)?

The conversion credit weighting rules that the default attribution models use are called the default conversion credit weighting rules.

For example, by default, the first interaction model assigns 100% conversion credit to the first interaction on a conversion path. This is the default conversion credit weighting rule used by the first interaction model.

Similarly,

By default, the position-based attribution model assigns 40% of the conversion credit to the first interaction, 20% of the conversion credit to the middle interactions and 40% of the conversion credit to the last interaction on a conversion path.

This is the default conversion credit weighting rule used by the position-based attribution model.

Some default attribution models allow you to change their default credit rules when used as a baseline model while others don’t.

For example, you can change the default credit rules for the time decay and position-based attribution models when you used these models as baseline models:

default credit rules that can be changed

However, you can not change the default credit rules for the following default attribution models: First Interaction, Last Interaction, Linear and Data-Driven.

A default attribution model is considered to be used as a baseline model when it is used as a starting point for creating a custom attribution model.

This baseline model defines how conversion credit should be distributed to various interactions on a conversion path before the custom credit rules are applied. 

Important points about the default credit rules

#1 You can only change the default credit rules via the model comparison tool while creating or editing a custom attribution model.

#2 If you are using the time decay model as the baseline model and you want to know the optimum setting for the half-life of decay then use the ‘Time lag in days’ metric found in the ‘Time Lag’ report (under ‘Conversions’ > ‘Multi-Channel Funnels’). 

#3 If you are using the position-based model as the baseline model and you want to change the default conversion credit rules then make sure that the sum of the new credit rules is not greater than 100%.

#4 Other than the time decay and position based models, all other default attribution models apply default credit rules internally and you cannot change these rules. For example, you cannot change the default credit rule used by the last interaction model.

#5 The default credit rules are applied before the custom credit rules.

What are Custom conversion credit weighting rules?

Custom conversion credit weighting rules (or custom credit rules) are the credit rules created by people like you and me to greatly expand the way a custom attribution model is defined.

Your custom model is based on a baseline model and the baseline model is already following its default credit rule.

So when you create and apply custom credit rules to your custom model, you are applying multiple weighting rules to the model. Each custom credit rule is based on one or more conditions:

default credit rule custom credit rules

You can create a custom credit rule by creating one or more conditions that identify touchpoints on a conversion path according to their characteristics (position, traffic source type). And then distribute conversion credit to the touchpoints.

When you create custom credit rules, you need to do two things:

  1. Define the touchpoints you wish to identify on a conversion path.
  2. Specify how conversion credit will be distributed to these touchpoints.

Note: All conversion credit distribution is relative.

How to create a custom credit rule?

Let’s create a custom credit rule which tells Google Analytics to give direct traffic only half of the conversion credit of other touchpoints on a conversion path, provided direct traffic is the last user interaction.

Follow the steps below to create such a rule:

Step-1: Navigate to the Model Comparison Tool report in your GA reporting view.

model comparison tool google analytics

Step-2: Click on the ‘Select Model’ drop-down menu:

select model drop down menu

Step-3: Scroll down and then click on the link ‘Create new custom model’.

create new custom model

Step-4: Give a name to your custom model and then select the attribution model you want to use as the baseline model from the drop-down menu:

name your attribution model

Step-5: Change the default conversion credits (optional) and specify the lookback window:

Change the default conversion credits

Step-6: Scroll down and then click on the toggle button next to ‘Apply custom credit rules‘:

Apply custom credit rules

Step-7: Define the custom credit rules as shown below:

Define the custom credit rules as shown below

Step-8: Click on the ‘Save and Apply’ button.

Another Example

The following is the custom credit rule which tells Google Analytics to give ad impressions only half of the conversion credit of other touchpoints on a conversion path, provided an ad impression is the first user interaction:

give ad impressions only half of the conversion credit

Note: You can also add custom credit rules to your DDA model by creating a new custom attribution model and using the DDA model as the baseline model. 

Overlapping conversion credit weighting rules

You can also create multiple conversion credit weighting rules that apply to the same touchpoint on a conversion path.

But when you do that, you overlap conversion credit rules and you multiply the credit weighting. 

For example, let us suppose the first credit rule gives display ad impressions two times more conversion credit than the other interactions on the conversion path.

The second credit rule gives the display ad impressions three times more conversion credit than the other interactions on the conversion path.

Now the display ad impressions may end up getting six (2 * 3) times more conversion credit than the other interactions on the conversion path.

How to adjust Credit for Impressions?

Adjusting credit means distributing conversion credits to various interactions on a conversion path.

By adjusting the credits for interactions you can decide how the interactions are valued on a conversion path.

For example, by adjusting credit for impressions you can decide how impressions are valued on a conversion path.

If you are a GA360 customer and you have set up view-through conversion tracking then you see the option to ‘Adjust Credit for Impressions’ while creating or editing a custom attribution model. 

Let us suppose you want to tell Google Analytics that if a user completes a conversion on your website within 12 hours of viewing (but not clicking) one of your display ads then the display ad impressions should get two times more conversion credit than the other interactions on the conversion path.

To achieve this objective, adjust your credit for impressions like the one below: 

adjust credit for impressions

How to adjust Credit based on user engagement?

While creating or editing a custom attribution model, you also get the option to distribute conversion credit proportionally based on user engagement (time on site or page depth):

adjust Credit based on user engagement

When you adjust conversion credit based on user engagement you are telling GA to give more conversion credit to those interactions on a conversion path that are part of a more user-engaged GA session. 

Time on site

If you select ‘time on site’ as the user engagement metric, then you are telling Google Analytics to give more conversion credit to those interactions on a conversion path that are part of a GA session(s) in which users spent more time on your website.

For example, if one user spent two minutes on your website after clicking on a display ad (ad 1) and another user spent three minutes on your website after clicking on another display ad (ad 2), then the click on ad 2 would get more conversion credit on the conversion path. 

Page Depth

If you select ‘page depth’ as the user engagement metric, then you are telling Google Analytics to give more conversion credit to those interactions on a conversion path that are part of GA session(s) in which users saw a higher number of pages on your website. 

For example, if one user saw five pages on your website after clicking on ad 1 and the other user saw three pages on your website after clicking on ad 2, then the click on ad 1 would get more conversion credit on the conversion path. 

How to share a custom attribution model?

You can share your custom attribution model with others.

To do that follow the steps below:

Step-1: Navigate to the Model Comparison Tool report in your GA reporting view:

model comparison tool google analytics

Step-2: Click on the ‘Select Model’ drop-down menu:

select model drop down menu

Step-3: Click on the person icon button next to the name of the custom attribution model you want to share it with others:

share custom attribution model google analytics

You can now either share the custom model’s template link or share your model in the Google Solutions Gallery:

select a method for sharing the custom attribution model

Note(1): When you share a custom attribution model, only the attribution model template is shared and not your attribution data. 

Note(2): When you share your custom model in the Google Solutions Gallery, you are sharing the template of your custom model with the general public.

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