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 to read this article first: Beginners Guide to Google Analytics Attribution Modelling.

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. 

Through model comparison tool you can create a new attribution model.

You can create up to 10 custom attribution models per GA reporting view.

If you have never used the model comparison tool before, then read this article:Attribution Model Comparison Tool in Google Analytics

Quick recap

An attribution model is a set of rules which is used to determine, how credit for conversions should be attributed/distributed to different touch points in a conversion path.

A conversion path can be made up of: one interaction, two interactions, four interactions etc.

An interaction is an exposure to a marketing channel.

Since interaction and touchpoints are the same thing, a conversion path can be made up of: one touch point, two touch points, four touch points etc.

An attribution model divides conversion credit across all touch points.

So if you are using a linear attribution model, then the conversion credit will be divided equally across all touch points.

In other words, all touch points will get equal credit for conversions.

However if you are using a last click model, then the conversion credit will not be divided across all touch points and the last interactions get all the credit for conversions.

Same is the case with first click model, where the first interactions get all the credit for conversions.

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Why create your own attribution model?

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

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

When you use a different attribution model, it impacts the valuation of your marketing channels. 

Through attribution model you can evaluate the effectiveness of your marketing campaigns.

You can evaluate yours 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 model you can test your assumptions by experimenting.

Attribution models are used for experimenting/testing purpose.

For example, under last click attribution model, your display advertising may be heavily undervalued because of your unique customers’ purchase behavior. 

May be 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.

So may be under last click attribution model, display advertising is heavily undervalued.

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

Here custom attribution models comes into picture.

You create a hypothesis and then test it by creating a custom attribution model.

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

The hypothesis you create is based on your analysis.

Your hypothesis could be something like:

“If a user completes a goal conversion on my website within 12 hours after 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 in the conversion path”

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

But before you can create our own attribution model, you need to first learn about the various nuts and bolts of custom attribution modelling.

Technical requirements for creating Custom attribution model

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

In Google Analytics, there are two types of attribution models:

#1 Baseline attribution models (which are pre-built models)

#2 Custom attribution models (which are user defined models)

In GA, you can not create a custom attribution model from scratch.

The attribution model that you create, will be built on top of a baseline attribution model.

You use default 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. 

This baseline model defines, how credit for conversions should be attributed to various touch points in a conversion path before custom credit rules are applied.

When you build your own attribution model, you create your own rules to assign credit for conversions to different interactions/touch points in a conversion path.

These rules are known by the name ‘custom credit rules’ in Google Analytics. 

Adjusting credit means, distributing credit for conversions to various interactions in a conversion path.

By adjusting credit for interactions, you can customize how interactions are valued.

Follow the steps below to create your own attribution model.

Step-1: Develop ‘great’ understanding of your business.

You have developed that ‘great’ understanding when you can confidently look beyond data and raw numbers and can make business and marketing decisions based on:

  • Context.
  • Faith (a collective know-how of your organization, target audience and industry).
  • All business and marketing activities which are outside the digital realm.

Attribution modelling is a very advanced stage of business analysis where we determine most effective marketing channels for investment and there is almost always, lot of money at stake.

An incorrect attribution can result in huge monetary loss for you or your client.

Therefore great understanding of the business, for which you wish to carry out attribution modelling is mandatory.

Otherwise your attribution modelling is most likely to be flawed.

Step-2: Fix data collection issues.

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.

Step-3: Set up Goal Conversion tracking and/or ecommerce tracking 

This is primary requirement for creating a custom attribution model.

Step-4: Track only relevant goals.

Make sure that you are tracking all but only relevant goals (and not something like ‘time spent on website’) along with their correct goal value.

A conversion without a goal value (or economic value) is a bogus conversion as it does not add any value to the business bottomline.

Track only those goals which are really useful for your business. Irrelevant goals can greatly skew your conversion volume and conversion rates, pollute your multi channel funnel data and make attribution modelling flawed.

Step-5: Fix cross device attribution issues.

If your website has got 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 happen because the attribution modelling reports provided by GA, mainly report on single device single browser attributions.

Step-6: Import cost data for all paid marketing campaigns/channels into Google Analytics.

You may be getting tons of sales and conversions but if you don’t keep an eye on ‘cost per acquisition’ then the cost can literally kill your marketing ROI.

Attribution modelling is most useful when it take ‘cost’ into account.

Step-7: Create your hypothesis.

You create a hypothesis and then test it by creating a custom attribution model.

The hypothesis you create is based on your analysis and the attribution problem you are trying to solve.

Step-8: Create Custom Attribution Model

#1 Navigate to Model Comparison Tool (under Conversions > Attribution in your GA view):

#2 From the ‘select model’ drop down menu click on ‘create new custom model’:

create-new-model

#3 Name your model and select a baseline model (like linear, time decay or position based):

select-baseline-model

#4 Select baseline model on the basis of your business model and advertising objectives.

To learn more, read this article: Guidelines for selecting an Attribution model in Google Analytics

#5 Set conversion credits and specify look back window:

conversion-credit

#6 Adjust conversion credit based on user engagement.

The user engagement can be in the form of  ‘time on site’ or ‘page depth’:

time-on-page

Note: Step-6 is optional.

#7: Create and apply new custom credit rules.

These rules define how credit should be distributed to various touch points / interactions in a conversion path.

Each rule is based on one or more conditions:

custom-credit-rule3

#8 Once you have added all the custom credit rules, Click on ‘save and apply’ button.

#9 Compare your attribution model with other models and valuate your marketing channels and campaigns.

model-comparison

For practical use of model comparison, read this article: Attribution Model Comparison Tool in Google Analytics

Sharing custom attribution models

You can share your custom attribution models with others, by clicking on the ‘share’ button, next to the custom model you wish to share:

attribution-model-share

attribution-model-share2

When you share custom attribution model, only the attribution model template is shared and not your attribution data.

Learn about the Google Analytics Usage Trends Tool

The Google Analytics usage trend is a new tool which is used to visualise trends in your Google Analytics data and to perform trend analysis.


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Himanshu Sharma

Certified web analyst and founder of OptimizeSmart.com

My name is Himanshu Sharma and I help businesses find and fix their Google Analytics and conversion issues. If you have any questions or comments please contact me.

  • Over eleven years' experience in SEO, PPC and web analytics
  • Google Analytics certified
  • Google AdWords certified
  • Nominated for Digital Analytics Association Award for Excellence
  • Bachelors degree in Internet Science
  • Founder of OptimizeSmart.com and EventEducation.com

I am also the author of three books:

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