Data-Driven Attribution Model Explorer in Google Analytics

 

 
This article is related to Attribution Modelling in Google Analytics.

If you are brand new to Google Analytics Attribution then read this article first: Beginners Guide to Google Analytics Attribution Modelling

Model Explorer (also known as Data Driven Attribution model explorer) is an attribution modelling tool through which you can determine how the Data Driven Attribution (DDA) model values were calculated in Google Analytics and how the DDA model distributed conversion credit to various marketing channels, defined in the ‘default channel grouping’.

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Criteria to be eligible to use Data-Driven Attribution Model Explorer

The primary requirement to be eligible to use the Model Explorer tool is access to Google Analytics 360/premium account.

The model explorer tool is not available in the standard version of Google Analytics.

Following are other critical requirements to be eligible to use this tool effectively:

#1 ‘Goal conversion’ tracking setup in your chosen GA premium view. So that goal data is available to your DDA model.

#2 ‘ecommerce tracking’ or enhanced ecommerce tracking set up in your chosen GA premium view. So that ecommerce data is available to your DDA model. 

#3 Data Driven Attribution Model set up in your chosen GA premium view.

#4 Availability of cost data in GA reports.

#5 At least 60 days of historical data in Google Analytics (in order to make the data statistically significant). This data include: ecommerce data, goal conversion data, cost data and website usage data.

 

To access the Model explorer tool follow the steps below:

Step-1: Login to your GA premium account and then navigate to the reporting view for which the Data Driven Attribution model has been enabled.

Step-2: Navigate to Conversions > Attributions > Model Explorer:

You will then see the data driven attribution model explorer report.

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How to read the Data Driven Attribution Model Explorer Report?

This report is going to look like the one below:

read-model-explorer-report

DDA model determine and use up to four most influential interactions/touchpoints in the last 90 days prior to each conversion. These interactions are reported by the Model explorer tool.

This tool assigns ‘weighted average credit’ to each of the 4 interactions for each marketing channel. 

Determining the overall weight of marketing channels in a particular time period

In order to determine the overall weight of marketing channels in a particular time period, follow the steps below:

Step-1: Select ‘All’ from the ‘conversion’ drop down menu in the model explorer report:

all-conversions

Step-2: Look at the ‘Conversions’ column in the model explorer report:

data-driven-conversions

The marketing channel with higher data driven conversions carries more weight in a particular time period.

If you want to determine the weight of a marketing channel for a particular conversion then select the conversion from the ‘conversion’ drop down menu in the model explorer report:one-conversion-type-selected

You can also download the latest data driven attribution model into excel for further analysis by clicking on the ‘Download the full model button at the top right of the ‘Model explorer tool’:download-the-full-model

The downloaded DDA model include only those conversions whose conversion path length is 2 or more interactions and which were recorded in the last 28 days from the time the DDA model was last generated.

The DDA model you see in the model explorer report show conversion credits based on the conversion type and time period you select for the report. Because of this reason your downloaded model may not match with the DDA model you see, in your model explorer report.

As such, a traditional MMM model is not suitable for carrying out digital marketing mix modelling aka attribution modelling.

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

 Click book covers to find out more

Other articles on Attribution Modelling in Google Analytics

  1. Touch Point Analysis in Google Analytics Attribution Modelling
  2. 8 Google Analytics Conversions Segments You Must Use
  3. Default and Custom Attribution Models in Google Analytics
  4. Attribution Model Comparison Tool in Google Analytics
  5. Which Attribution Model to use in Google Analytics?
  6. How to create Custom Attribution Model in Google Analytics

  1. How to do ROI Analysis in Google Analytics
  2. Google Analytics Attribution Modelling – Complete Guide
  3. Guide to Data Driven Attribution Model in Google Analytics
  4. Conversion Credit distribution for Attribution Models in Google Analytics
  5. You are doing Google Analytics all wrong. Here is why

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  2. Introduction to Nonline Analytics – True Multi Channel Analytics
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  5. Understanding Shopping Carts for Analytics and Conversion Optimization

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  3. Guide to Offline Conversion Tracking in Google Analytics
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  6. What is Attribution Modelling and why it is the ‘key’ to online business success?
  7. How Does Attribution Work?
  8. How is Attribution Modelling helpful for e-commerce and non-e-commerce websites?
 

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Himanshu Sharma

Digital Marketing Consultant and Founder of Optimizesmart.com

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.

He has over 12 years experience in digital analytics and digital marketing.

He was nominated for the Digital Analytics Association's Awards for Excellence.

The Digital Analytics Association is a world renowned not-for-profit association which helps organisations overcome the challenges of data acquisition and application.

He is the author of four best-selling books on analytics and conversion optimization:

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