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Last Updated: August 20, 2022

As the name suggests, the Model Comparison Tool is used to compare different attribution models to each other in Google Analytics. 

Such comparison is carried out to valuate a marketing channel from a different perspective and to identify new optimization opportunities. 

Through the Model Comparison tool, you can:

1. Compare different default attribution models to each other. 
2. Compare different custom attribution models to each other.
3. Compare default and custom attribution models to each other.
4. Create new custom attribution models.
5. Import custom attribution models from the Google Analytics Solutions Gallery.
6. Create new conversion segments.
7. Apply one or more existing conversion segments.
8. Create a new custom channel grouping.

Through the Model Comparison Tool, you can get answers to questions like:

• How can I make my PPC campaigns more effective?
• If I change my display advertising budget, how will it affect my website sales?
• Is organic search advertising (SEO) undervalued or overvalued?
• If I invest in SEO, then how much incrementality does SEO can bring to my business bottomline?

Eligibility criteria for using the Model Comparison Tool

Following are the requirements for using the Model Comparison Tool:

1. Your reporting view needs to have goal conversion and/or ecommerce conversion data.
2. Your reporting view needs to have cost data from Google Ads and/or non-Google Ads campaigns.
3. At least 30 days of historical data in your reporting view in order to make the data statistically significant for data analysis. 

If you do not have an ecommerce tracking and/or goal conversion tracking setup, in your GA reporting view, you won’t see any data in the Model Comparison Tool report. 

What you will see instead is the following message: 

“This report requires goals and/or ecommerce tracking to be enabled for this view”

By default, the model comparison tool report does not show the ‘Spend’ column in its report. 

In order to see the ‘Spend’ column, you would need to import cost data into your GA property. 

Once the cost data is imported into your GA property, you should start seeing the ‘Spend’ column. 

If you see dash sign (-) under the ‘Spend’ column for a particular marketing channel then it means no cost data is available for that channel in the selected time period:

How to access the Model Comparison Tool

To access the Model Comparison Tool follow the steps below: 

1. Navigate to the reporting view which has collected the conversion data.
2. Navigate to Conversions > Multi-Channel Funnels > Model Comparison Tool.
   
3. Select at least two attribution models via the ‘Select model’ drop-down menu.

Note: You can compare up to three attribution models side by side through the model comparison tool.

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Conversions & CPA

By default, the model comparison tool report does not show the ‘Conversions & CPA’ drop-down menu in its report. 

In order to see this drop-down menu, you would need to compare at least two attribution models to each other. 

As soon as you select the second attribution model, the ‘Conversions & CPA’ drop-down menu would automatically appear in the middle of the report:

Once you click on the ‘Conversions & CPA’ drop-down menu, you can then select one of the following metrics combinations:

1. Conversion value and ROAS.
2. Conversions and value.

The CPA (cost per acquisition) metric in the model comparison tool report is calculated for each marketing channel and for each attribution model. 

If you see dash sign (-) under the CPA column for a particular marketing channel then it means no CPA data is available for the channel in the selected time period:

Conversion Value & ROAS

By default, the model comparison tool report does not show the ‘Conversion Value & ROAS’ drop-down menu in its report. 

In order to see this drop-down menu, you would need to compare at least two attribution models to each other and then select the ‘Conversion Value & ROAS’ option from the drop-down menu:

Just like CPA, the ROAS (return on ad spend) metric in the model comparison tool report is calculated for each marketing channel and for each attribution model. 

If you see a dash sign (-) under the ROAS column for a particular marketing channel then it means no ROAS data is available for the channel in the selected time period.

Conversions & Value

By default, the model comparison tool report does not show the ‘Conversions & Value’ drop-down menu in its report. 

In order to see this drop-down menu, you would need to compare at least two attribution models to each other and then select the ‘Conversions & Value’ option from the drop-down menu:

Just like CPA & ROAS, the conversions and conversion value metrics in the model comparison tool report are calculated for each marketing channel and for each attribution model. 

Here the ‘conversions’ metric denotes conversion volume.

The % change column of the model comparison tool

The percentage change column of the model comparison tool shows either the percentage change in conversions or percentage change in conversion value across attribution models:

In order to display the % change column, you would need to compare at least two attribution models to each other, via the model comparison tool. 

Note: The % change column of the model comparison tool does not report on the percentage change in CPA or percentage change in ROAS across attribution models. 

Google does not report on the % change in CPA across attribution models because CPA is a calculated metric. So the % change in CPA between attribution models is going to be identical to that of % change in conversions.

Similarly, Google does not report on the % change in ROAS across attribution models because ROAS is also a calculated metric. So the % change in ROAS  between attribution models is going to be identical to that of percentage change in conversion value. 

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Comparison and Reference Attribution Models

The % change column of the model comparison tool shows the percentage change in conversions or the percentage change in conversion value among the comparison attribution models and the reference attribution model:

For example, in the screenshot above, the Last Interaction Model is the reference attribution model. Whereas, Last Non-Direct click and Data-Driven are comparison attribution models.

Under the % change column, you can see different symbols next to percentages. Different symbols have got different meanings. 

For example, if you see a grey dot next to percentage change, it means Google Analytics did not detect any identifiable percentage change between the comparison and reference attribution models:

If you see an upward arrow next to percentage change, it means Google Analytics detected a percentage change which is in favour of the comparison attribution model but not in the favour of the reference model:

From the screenshot above we can conclude that if we use the last interaction model, to distribute conversion credit to a marketing channel then the channel deserves 6.44% more credit for conversion in comparison to the linear attribution model. 

In other words, the marketing channel is undervalued by 6.44% under the linear attribution model when this model is compared with the last interaction model. 

This is the kind of insight you can get from the % change in conversions. 

If you see a green upward arrow next to percentage change, it means Google Analytics detected a positive percentage change which is 10% or higher and this change is in favour of the comparison attribution model but not in the favour of the reference model:

If you see a downward arrow next to percentage change, it means Google Analytics detected a percentage change which is not in favour of the comparison attribution model but is in favour of the reference model:

From the screenshot above we can conclude that if we use the time decay attribution model, to distribute conversion credit to a marketing channel then the marketing channel deserves 1.88% less conversion credit in comparison to the linear attribution model. 

In other words, the marketing channel is overvalued by 1.88% under the linear attribution model when this model is compared with the time decay model. 

This is the kind of insight you can get from the % change in conversions. 

If you see a red downward arrow next to percentage change, it means Google Analytics detected a negative percentage change which is 10% or higher and this change is not in favour of the comparison attribution model but is in the favour of the reference model:

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Case Study – How organic search can be valued from a different perspective

We can use the model comparison tool to determine how a marketing channel like organic search can be valued from a different perspective. 

Follow the steps below:

Step-1: Navigate to the model comparison tool report in your GA reporting view.

Step-2: Set the date range to the last three months or more.

Step-3: Compare the last interaction model with the last non-direct click model and the time decay model. Now you may be wondering, why I selected these three particular attribution models for my analysis. 

• I selected the last interaction model because this is the default model used in the multi-channel funnel reports in Google Analytics. 
• I selected the last non-direct click model because this is the default model used in non-MCF reports in GA. 
• I selected the time decay model because in many cases, it is better than the first interaction, linear, Last Google Ads click and position based models. It is better in terms of assigning more accurate conversion credits.

Step-4: Select ‘Conversions & Value’ from the drop-down menu, located in the middle of your model comparison tool report:

Step-5: Now look at the column named ‘% change in conversion (from last interaction)’ for the Organic Search channel:

From the screenshot above we can conclude that the % change in conversions for the organic search channel from the last interaction model to the last non-direct click attribution model is 21.61%. 

What that means, if you use the last non-direct click attribution model (instead of the last interaction model) to distribute conversion credit to organic search, then the organic search channel deserves 21.61% more credit for conversions. 

In other words, organic search is undervalued by 21.61% under the last interaction attribution model when this model is compared with the last non-direct click attribution model. 

The upward green arrow next to 21.61% indicates a positive change in conversions from the last interaction model.

Similarly, the % change in conversion for organic search from the last interaction model to the time decay attribution model is 8.60%. 

What that means, if you use the time decay attribution model (instead of the last interaction model) to distribute conversion credit to organic search then the organic search deserves 8.60% more credit for conversions.

In other words, organic search is undervalued by 8.60% under the last interaction attribution model when this model is compared with the time decay attribution model. 

The upward grey arrow next to 8.60% indicates a positive change in conversions from the last interaction model.

So what insight have we gained from this analysis? 

The insight is that overall the organic search marketing channel is undervalued by (21.61 + 8.60) / 2 = 15.10% under the last interaction attribution model.

You can now show this report to your client or boss and can demand more budget for the organic search campaigns. However, do not take the value of 15.10% too seriously. 

Conversely, if overall, organic search turned out to be overvalued by ‘X%’,  you know that your ad budget would be better spent in investing in other marketing channels or finding a new SEO service provider. 

Similarly, through the model comparison tool, you can valuate other marketing channels like paid search, email, display, social media etc.

Case Study – How organic search can be valued from a different perspective via the DDA model

If you are eligible to use the DDA model then you should be using this model instead of the time decay model for valuating organic search from a different perspective. 

I would select the DDA model over the time decay model for two main reasons:

1. The DDA model can analyze data not only from my GA account but also for all those Google and non-Google accounts which are linked to my GA account.
2. The DDA model assigns conversion credits algorithmically. Such a type of conversion credit distribution is much more reliable than the conversion credit distribution by the time decay model.

Once you have selected the DDA model in your model comparison tool report, look at the column named ‘% change in conversion (from last interaction)’ for the organic search marketing channel:

From the screenshot above we can conclude that the % change in conversion for the organic search marketing channel from the last interaction model to the data-driven attribution model is 22.66%. 

What that means, if you use the DDA model (instead of the last interaction model) to distribute conversion credits to organic search, then the organic search deserves 22.66% more credit for conversions.

In other words, organic search is undervalued by 22.66% under the last interaction attribution model when this model is compared with the DDA model. 

So we can now conclude with confidence that in this particular case, the organic search marketing channel is undervalued and is undervalued by (21.61 + 22.66) / 2 = 22.135% under the last-interaction attribution model.

Other articles on attribution modelling

1. How to analyse and report the true value of your SEO Campaign
2. How to valuate Display Advertising through Attribution Modelling
3. Understanding Shopping Carts for Analytics and Conversion Optimization
4. 6 Keys to Digital Success in Attribution Modelling
5. Google Analytics Attribution Modeling Tutorial
6. How to Measure and Improve the Quality of SEO Traffic through Google Analytics
7. How to explain attribution modelling to your clients
8. Default and Custom Attribution Models in Google Analytics
9. Understanding Missing Touchpoints in Attribution Modelling
10. What You Should Know about Historical Data in Web Analytics
11. Model Comparison Report Explained in Google Analytics Attribution
12. Data-Driven Attribution Model in Google Analytics – Tutorial
13. Conversion Lag Report Explained in Google Analytics Attribution
14. Selecting the Best Attribution Model for Inbound Marketing
15. How to do ROI Analysis in Google Analytics
16. Conversion Credit Models Guide – Google Analytics Attribution
17. Introduction to Nonline Analytics – True Multi Channel Analytics
18. Conversion Types Explained in Google Analytics Attribution
19. Attribution Channels Explained in Google Analytics Attribution
20. Differences Between Google Attribution & Multi-Channel Funnel Reports
21. Introduction to TV Attribution in Google Analytics Attribution 360
22. Conversion Credit Distribution for Attribution Models in Google Analytics
23. Conversion Paths Report Explained in Google Analytics Attribution
24. Attribution Model Comparison Tool in Google Analytics
25. Touchpoint Analysis in Google Analytics Attribution Modelling
26. Attributed Conversions & Attributed Revenue Explained in Google Attribution
27. Which Attribution Model to use in Google Analytics?
28. Google Attribution Access and User Permissions – Tutorial
29. Conversion Path Length Report Explained in Google Analytics Attribution
30. How to set up a data-driven attribution model in Google Analytics
31. View-Through Conversion Tracking in Google Analytics
32. Offline Conversion Tracking in Google Analytics – Tutorial
33. How to Create Custom Attribution Model in Google Analytics
34. 8 Google Analytics Conversions Segments You Must Use
35. You are doing Google Analytics all wrong. Here is why
36. How to Use ZMOT to Increase Conversions and Sales Exponentially
37. Connected Properties Explained in Google Analytics Attribution
38. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
39. How is attribution modelling helpful for ecommerce and non-ecommerce websites?
40. Conversion Time & Interaction Time Explained in Google Analytics Attribution
41. How to Allocate Budgets in Multi Channel Marketing
42. How Does Attribution Work?
43. Data-Driven Attribution Model Explorer in Google Analytics
44. Introduction to Attribution Beta – Attribution Project in Google Analytics