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Last Updated: July 4, 2022

This article is in conjuntion with the article Data-Driven Attribution Model in Google Analytics – Tutorial where I introduced the concept of MCF data-driven attribution model and then explained in it great detail.

In the current article, you will learn to set up Data-driven attribution model in Google Analytics.

A quick recap of MCF data-driven attribution model eligibility criteria

In order to be eligible to use the MCF data-driven attribution model you must meet all of the following criteria: 

1. You have access to GA360 enabled property.
2. Your GA360 enabled reporting view is collecting goal conversion and/or ecommerce conversion data. 
3. Your Google ads account is linked to your GA360 property.
4. Your Google Ads account has recorded at least 15,000 clicks on Google search and a conversion action with at least 600 conversions within 30 days.
5. Your GA360 enabled reporting view meets the minimum conversion threshold for generating the DDA model. 
6. You have enabled MCF data-driven attribution model for your GA360 enabled reporting view.

Before you enable MCF data-driven attribution model do this

If you want Google to generate the most accurate MCF DDA model possible then make sure that you complete the following tasks before you enable MCF data-driven attribution model for your GA360 enabled reporting view:

#1 Link all your Google accounts to your GA360 property. These Google accounts could be Google Ads, Google Search Console, BigQuery, Campaign Manager 360, Search Ads 360, Display and Video 360 etc.

#2 Link all your non-Google data sources to your GA360 property. These non-Google data sources could be your CRM, shopping cart, phone call tracking software etc.

#3 Set up and collect enhanced ecommerce data in your reporting view. The advantage of using enhanced ecommerce is that it provides much more ecommerce data than standard ecommerce tracking. You can then feed this data to your DDA model and produce more accurate conversion probability and attribution recommendations.

#4 Collect at least 30 days of conversion data in your reporting view. So that your conversion data is statistically significant. 

#5 Import cost data for all non-Google marketing campaigns. In order to do ROI analysis in Google Analytics using the DDA model, you would need cost data in your GA reports. 

#6 Import refund data for ecommerce transactions.

#7 Import user data such as lifetime value. 

#8 Contact your GA360 account manager and ask them to enable the GDN impression reporting. This feature allows you to see ad impressions and rich-media interactions on the conversion paths.

#9 Fix all cross-domain tracking issues.

#10 Fix all cross-device tracking issues.

#11 Track as many offline conversions in your reporting view as possible.

#12 Minimize any other data collection and data integration issues.

The more data from different channels and devices you feed into your DDA model, the more accurate conversion probability and attribution recommendations would be.

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

 Click book covers to find out more

How to enable the MCF data-driven attribution model in Google Analytics?

By default, the DDA model is not enabled in a GA360 reporting view. 

To enable the DDA model, follow the steps below:

Step-1: Make sure you have the ‘Edit’permission at the account, property or view level.

Step-2: Navigate to the ‘Admin’ section of your GA360 enabled reporting view.

Step-3: Click on the ‘View Settings’ link under the ‘View’ column:

Step-4: Scroll down until you see the ‘Modeling Settings’ section.

Step-5: Turn on the toggle button named ‘Enable Data-Driven Models’:

Step-6: If your GA360 property is linked to Campaign Manager 360 then you should also select the Floodlight conversion types for which DDA models will be generated. You can do that via the drop-down menu (see the screenshot above). You can select up to 20 Floodlight conversion types.

Note: Floodlight is the conversion tracking system for Google Marketing Platform.

Step-7: Click on the ‘Save’ button.

Step-8: Wait for a week for Google Analytics to analyze your data and generate a DDA model. Until then you may see the following notification ‘A Data-Driven model has not yet been created’.

Step-9: After a week has elapsed navigate to the Model Explorer report (under ‘Conversions’ > ‘Multi-Channel Funnels’) in your GA360 enabled reporting view.

You should now be able to see your DDA model for one or more conversion types:

Issues with Traditional Multi-Touch Attribution Models

Let us consider that I followed the following conversion path:

From the conversion path above, we can conclude the following:

1. I read a blog post on your website.
2. After 3 days I saw your display ad on a website
3. After 2 days I read a review of your product on some website.
4. After 4 days I decided to make a purchase. So I made a search using a non branded keyword and clicked on your PPC ad on Google.
5. Just to make sure that I am going to get the best deal, I went to a product comparison site. Being satisfied with your product pricing I decided to make a purchase during the weekend.
6. During the weekend I again searched on Google but this time used a branded keyword and clicked on your organic search listing.
7. I made a purchase from your website.

In multi-touch attribution modelling, the conversion is attributed to multiple acquisition channels instead of just the first touch or last touch attribution. Here the middle touches also come into the picture.

This model aligns well the real-life situations as people rarely make a purchase through one or two acquisition channels. For e.g. it is highly unlikely for someone to read your blog post and then just make a purchase.

Similarly, it is highly unlikely for someone to see your display ad on a website and then directly make a purchase. He may read reviews of your product, go to a couple of product comparison sites before making a purchase. So we need to take all of the touches into account.

However, the problem with the typical multi-channel attribution model is the way credits for conversions are distributed to different marketing channels.

For example:

• Linear attribution model gives equal credit to each marketing channel/touchpoint in a conversion path.
• Position based attribution model gives more credit to first and last touch.
• Time decay attribution model gives more credit to the touchpoints which occur closest in the time to conversions.

In the real world, not all acquisition channels are equally valuable. 

For example, in the example above, I read product reviews and went to a product comparison site before making a purchase. These two touches were more valuable to me than the exposure to the blog post, display ad and the PPC ad as they play a very important role in my purchase decision.

Had I not been satisfied with the product review or pricing, I wouldn’t have made a purchase. Consequently, these touches should be given more credit.

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

 Click book covers to find out more

Why is Data Driven Attribution Model better than traditional models?

It is better because of the following two reasons:

1. Data driven attribution model is a real-world attribution model.

That means it takes into account the back and forth activities of customers between multiple devices both online and offline while distributing credit for conversions. Because of that property, it has the ability to provide a much better picture of the conversion path followed by your customers. It is the first generation of truly multi-channel analytics modelling tools.

2.Data driven attribution model provides more flexibility than traditional models

The data driven attribution model takes into account your business model, marketing objectives, sales cycle, customers’ activities and seasonality as it allows you to assign credit to different marketing channels/touchpoints in proportion to their contribution to the conversion process.

Thus it provides more flexibility than linear, position-based and time decay models in terms of credit distribution.

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