Last Updated: February 25, 2022

This article is related to attribution modelling in Google Analytics. If you are brand new to attribution modelling then read this article first: Google Analytics Attribution Modeling – Beginners Guide

What is the MCF Data-Driven Attribution model in Google Analytics?

The MCF data-driven attribution (DDA) model is an algorithmic attribution model which is available in the multi-channel funnel (MCF) reports of the GA360 enabled reporting view.

The DDA model generates custom conversion probability models.

These probability models are based on sophisticated predictive algorithms and on the data set from the following (but not limited to) data sources:

1. Your Google Analytics 360 enabled property.
2. All of the Google products that are linked to your GA360 property like Google Ads, Google Search Console, BigQuery, Campaign Manager 360, Search Ads 360, Display and Video 360 etc.
3. The data imported to your GA360 property via the Cost Data Upload feature. 
4. All of the available path data from users that completed conversions on your website (aka converting users).
5. All of the available path data from users that did not complete conversions on your website (aka non-converting users).

How does the Data-driven attribution model assign conversion credit to touchpoints?

The DDA model assigns partial conversion credit to touchpoints. 

It uses data modelling in which predictive algorithms find and analyse statistically significant data from multiple data sources and then assigns conversion credit to the four most influential marketing touchpoints on a conversion path, in the last 90 days prior to conversion. 

These touchpoints are reported by the Model Explorer Tool:

To learn more about Model Explorer (or Data-driven attribution model explorer), read this article: Understanding Model Explorer Tool in Google Analytics

The DDA model takes into account the order in which each touchpoint occurs on a user’s path and assigns different conversion credits for different path positions and channels. 

The DDA model carries out the conversion credit weighting algorithmically and automatically once a week. 

The conversion credit weighting refers to the adjustments made to the conversion credit of marketing channels in the default channel grouping. This adjustment is made by giving more or less credit to a marketing touchpoint on a conversion path.

The DDA model automatically refreshes (i.e. re-generate) once a week by updating the conversion credit weighting of marketing channels in the default channel grouping. 

It is also worth noting that the DDA model does not give conversion credit to direct traffic if a non-direct interaction resulted in a conversion within the last 24 hours. 

Important points about the Data-driven attribution model

#1 The DDA model is valid only for a particular time period as this model automatically changes with the change in the conversion data.

#2 Not every business is eligible to use the DDA model whether or not they use GA360. There are not just one but several stringent requirements that need to be met as well as maintained in order to use and benefit from the DDA model.

#3 A new DDA model is generated once every week. If the model was not generated for a particular week then Google Analytics displays the most recently generated DDA model.

#4 Sometimes a DDA model is not created because of an internal error. If that’s the case then contact your account manager.

#5 If you want to see how your DDA model distributed conversion credit to each of your marketing channels then use the Model Explorer report. This report is available under Conversions > Multi-Channel Funnels in your GA360 enabled reporting view.

#6 In the context of Google Analytics, the DDA model act as a baseline model. What that means, you can use DDA model to create a new custom attribution model. 

This new custom DDA model will distribute conversion credit to various touchpoints in a conversion path according to the DDA model and before the custom credit rules are applied.

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Advantages of using the Data-driven attribution model 

If you have access to GA360 and you are eligible for data-driven attribution then you should definitely use the DDA model. 

Following are the main reasons for that:

#1 The DDA model assigns credit for conversions algorithmically. Such a type of conversion credit distribution can be much more reliable than manually or arbitrarily assigned conversion credits.

#2 Unlike other GA attribution models, the DDA model can analyse the data not only from your GA property but also from all the Google and non-Google accounts which are linked to your GA property. 

You can feed the following types of data to your DDA model:

• Marketing data.
• Economic data.
• Competitive data.
• Weather data.
• Seasonality data.
• Data from CRM and several other non-Google data sources.

#3 Unlike other attribution models, the DDA model can analyse both conversion and non-conversion path data. Conversion path data is the data from the users who converted on your website. Non-conversion path data is the data from the users who did not convert on your website. 

#4 The DDA model carries out the conversion credit weighting algorithmically and automatically once a week. Such a type of credit weighting can be much more reliable than manual credit weighting via a custom attribution model.

#5 The DDA model is best suited for carrying out hybrid attribution. 

#6 Since the DDA model is an algorithmic attribution model which uses machine learning, it can be used to make better decisions about where to invest marketing resources than the non-algorithmic attribution models like first touch and last touch. 

Note: The one thing that you should not conclude is that only the DDA model is good and the rest are all flawed or useless. This is because attribution is driven by experiments. And in order to increase ROI across multiple marketing channels, you have to test different types of attribution models all the time.

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Data-Driven Attribution Model eligibility checklist

Not every business is eligible to use an algorithmic attribution model like the DDA model.

There are not just one but several stringent requirements that need to be met as well as maintain before a business can use and benefit from data-driven attribution:

1. Access to Google Analytics 360
2. Immediate access to a high volume of high-quality data
3. Alignment of Goals and KPIs
4. Conversion tracking setup
5. Meet the minimum conversion threshold
6. Maintain the minimum conversion threshold
7. Meet and maintain minimum conversion threshold for each conversion type

Requirement #1: Access to Google Analytics 360

The primary technical requirement to be eligible to use the DDA model is access to a Google Analytics 360/Premium account.

If you do not have access to GA 360, then you can not use the DDA model.

Requirement #2: Immediate access to a high volume of high-quality data

Your DDA model is only as good as the data you feed to it. If you feed it garbage, it will produce garbage. Garbage in garbage out. 

This is where most organizations fail miserably to benefit from data-driven attribution. They can afford to purchase GA 360. They can even afford to hire best analysts but cannot, in most cases, are able to build and maintain a high volume of high-quality data from multiple data sources.

As a result, the data-driven attribution insight they get is most likely to be flawed, misleading and sometimes downright dangerous to use.

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

Requirement #3: Alignment of Goals and KPIs

Your chosen goals and KPIs must align across marketing channels and organizations.

If you measure success differently for different marketing channels then there will be no alignment of goals and KPIs and data-driven attribution model won’t work.

For example, if the main goal of your Facebook campaigns is to increase website sales then the main goal of your Twitter campaigns should also be to increase website sales.

So if you choose ‘ increasing Twitter followers’ as the main goal of your Twitter campaigns then it means there is no alignment between Facebook and Twitter marketing channels’ goals.

Requirement #4: Conversion tracking setup

Goal conversion tracking and enhanced ecommerce tracking set up in the GA Premium view for which you want to generate the DDA model.

Without conversion data, Google Analytics will not be able to generate the DDA model for you, whether or not you are eligible to use the DDA model.

Requirement #5: Meet the minimum conversion threshold

In order to enable MCF data-driven attribution model for your GA360 reporting view, your view must meet the minimum conversion threshold for setting up a DDA model.

The minimum conversion threshold is at least 400 conversions per conversion type with a conversion path length of 2+ interactions and at least 10,000 conversion paths recorded in the last 28 days.

So your selected reporting view must have recorded at least 400 Goal conversions and/or at least 400 transactions with a conversion path length of 2+ and at least 10,000 conversion paths, in the last 28 days period.

If your selected view does not meet the minimum conversion threshold then Google Analytics will not be able to generate a DDA model for you.

Requirement #6: Maintain the minimum conversion threshold

The GA360 view for which you have enabled the DDA model must also ‘maintain’ the minimum conversion threshold. 

Just because your GA view met the minimum conversion threshold once, does not automatically make you eligible for ongoing and uninterrupted data-driven attribution analysis in GA.

Requirement #7: Meet and maintain minimum conversion threshold for each conversion type

Just because Google Analytics can generate a DDA model for your reporting view does not necessarily mean that it will also generate a DDA model for all individual conversions tracked through that view. 

A DDA model is generated separately for each conversion type. 

If your GA view does not meet the minimum conversion threshold for a particular conversion type then the DDA model will not be generated for that specific conversion. 

For example, one of the minimum conversion threshold requirements is that the conversion path length is made up of two or more interactions. 

So if a large number of paths for a particular conversion type have a path length of one then the DDA model will not be generated for that conversion.  

Similarly, if the conversion path data is not statistically significant (i.e. not statistically meaningful) for a particular conversion type then the DDA model will not be generated for that conversion type. 

Conversion path data can become statistically insignificant if it is made up of a small number of user interactions or infrequent users’ paths.

Therefore, there is always a possibility that a DDA model is generated for some conversions and not for others. 

Whenever a DDA model is not generated for a particular conversion then Google Analytics display a warning message at the top of a MCF report which reads: 

“For one or more of your selected conversions, a data-driven model couldn’t be generated due to insufficient data”.

You will also receive this warning message if one or more of your selected conversions dropped below the minimum conversion threshold required to generate a DDA model. 

Once a selected conversion drops below the minimum conversion threshold, it creates a gap in your attribution data. 

This gap can be as long as the period for which the drop occurred and the DDA model will not be able to create a custom model for that time period. In that situation, the DDA model will try to fill such gaps by using historical data.

Note: There is always a possibility that one or more of your selected conversion types may never meet the minimum conversion threshold and hence can not benefit from the data-driven attribution. In that case, use a different conversion type for attribution modelling.

How to set up the MCF Data Driven Model in Google Analytics?

If you want to set up the MCF Data Driven Model in Google Analytics then checkout this step by step guide: Learn to set up Data-driven attribution model in Google Analytics

How to use the Data-Driven Attribution Model to valuate organic search channel?

Navigate to ‘Model comparison tool’ and compare ‘last interaction model’ with ‘last non-direct click’ and ‘Data-Driven’ model as shown below:

I selected the ‘last interaction’ model because it is the default model used in multi-channel funnel reports in GA.

I selected the ‘last non-direct click’ model because it is the default model used in non-multi-channel funnel reports in GA.

I selected ‘data-driven attribution model over the ‘Time decay’ model for two main reasons:

#1 Data-driven attribution model can analyze the data not only from my GA account but also from all those Google Accounts (like Doubleclick Campaign Manager, Google Adwords, etc) which are linked to my GA account.

#2 Data-driven attribution model assigns credit for conversions algorithmically, which I trust much more than manual conversion credits and/or the credits assigned via the time decay model.

Now look at the column named ‘% change in conversion (from last interaction)’ for ‘organic search’:

Form this report we can conclude that the % of change in conversion for organic search from the last interaction model to the data-driven attribution model is 22.66%.

What that means, if you use ‘data-driven attribution model (instead of last interaction model) to distribute credit for conversions to organic search, then the ‘organic search’ deserve 22.66% more credit for conversions.

In other words, ‘organic search’ is undervalued by 22.66% under the last-click attribution model (when this model is compared with the ‘data-driven attribution model).

So we can now conclude with confidence, that in this particular case, organic search is undervalued and is undervalued by (21.61 + 22.66)/2 = 22.135%

Note: You can download the data-driven attribution model into excel by clicking on the ‘Download the full model’ button at the top right of the ‘Model explorer tool’.

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

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