Advanced Attribution Modelling in Google Universal Analytics

 

Attribution modelling is an advanced topic in itself but this post has been developed to take this topic to a whole new level and to talk about things which are rarely discussed in great detail in the analytics world.

Here is what you are going to learn through this post:

1. How to use the ‘Model Comparison Tool’ to determine most effective marketing channels for investment

2. What really is ‘Data Driven Attribution model’ and how to use it

3. What is ‘Model Explorer’ and how to use it

But before we move forward, I want to make sure that we all are on the same page.

I won’t be talking about the very basics of attribution modelling in this ‘advanced’ post.

If you are new to attribution modelling or at the intermediate level, I would strongly suggest you to read the following posts first (if you have not already read them):

  1. Beginners guide to Attribution Modelling
  2. 6 Keys to Digital Success in Attribution Modelling
  3. The Geek Guide to implementing Attribution Modelling

 

The Model Comparison Tool (or Attribution Models Comparison Tool)

As the name suggest, this attribution tool is used to compare different attribution models to each other.

An attribution model is a set of rules which is used to determine how credit for conversions should be attributed to different marketing channels.

Attribution models can be broadly classified into two categories:

  1. Baseline Attribution Models
  2. Custom Attribution Models

Baseline attribution models are the pre-built attribution models available in Universal Analytics.

Custom attribution models are user defined attribution models.

Following are the examples of baseline attribution models available in Universal Analytics:

  1. First touch attribution model
  2. Last touch attribution model
  3. Linear attribution model
  4. Position based attribution model
  5. Time Decay attribution model
  6. Last Non Direct click attribution model
  7. Last Adwords Click attribution model
  8. Data Driven Attribution model

I won’t be explaining these attribution models (except data driven model) in detail in this post.

If you want to learn more about these attribution models (including how to create custom attribution models), then please check out the following two posts:

  1. Google Analytics Attribution Modelling – Beginners Guide
  2. 6 Keys to Digital Success in Attribution Modelling

Through Model Comparison tool you can compare different baseline and custom attribution models to each other.

This comparison is carried out to determine how a marketing channel can be valued from different perspective.

For example let us determine how organic search can be valued from different perspective.

Follow the steps below:

Step-1: Click on ‘Model Comparison Tool’ link (under Conversions > Attribution menu) in your Universal Analytics View (or profile)

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

Step-3: Compare the ‘last interaction’ model with ‘last non-direct click’ and ‘time decay’ models.

model-comparison-tool

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

conversion-value

 

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

percent-change-conversion

Form this report we can conclude that the % of change in conversion for organic search from last interaction model to ‘last non-direct click’ attribution model is 21.61%.

That means that if you use ‘last non-direct click’ attribution model (instead of last interaction model) to distribute credit for conversions to organic search then the ‘organic search’ deserve 21.61% more credit for conversions.

It also means that ‘organic search’ is undervalued by 21.61% under last click attribution model when this model is compared with ‘last non-direct click’ attribution model.

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

  • Universal Analytics make an arrow coloured when the change in conversions is 10% or more.
  • If the change is positive and is 10% or more than the arrow gets green color.
  • If the change is negative and 10% or more than the arrow gets red color.

Similarly,

The % of change in conversion for organic search from last interaction model to ‘time decay’ attribution model is 8.60%.

That means that if you use ‘time decay’ attribution model (instead of last interaction model) to distribute credit for conversions to organic search then the ‘organic search’ deserve 8.60% more credit for conversions.

It also means that ‘organic search’ is undervalued by 8.60% under last click attribution model when this model is compared with ‘time decay’ attribution model.

The upward arrow next to 8.60% indicates positive change in conversions from last interaction model. Since the change in conversions is less than 10%, Universal Analytics has not made this arrow coloured.

 

So what insight we have got from this analysis?

The insight is that overall organic search is undervalued by (21.61 + 8.60)/2 = 15.10%

You can now show this report to your client/boss and demand more budget for organic search campaign.

Note: Don’t take the value of 15.10% too seriously because after all it is just an average value and we all are committed to report and analyse above average.

Conversely, if overall organic search turned out to be overvalued by …%, you know that, your money would be better spend in investing in other marketing channels or finding a new SEO service provider.

Similarly,

Through model comparison tool you can valuate other marketing channels like Paid Search, Email, Display, Social etc.

Now you may want to know why I picked these 3 particular attribution models for analysis: Last interaction, last non-direct click and time decay.

I selected last interaction model because this is the default model used in multi-channel funnel reports in Universal Analytics.

I selected last non-direct click model because this is the default model used in non-multi-channel funnel reports in Universal Analytics.

I selected ‘time decay’ model because it is less crappy than first click, linear, position based and last adwords click attribution models.

To know more about why other attribution models are more crappy, please read the post: 6 Keys to Digital Success in Attribution Modelling

But if I have to use the model comparison tool, I would use ‘data driven attribution model’ instead of ‘time decay’ model. You would know the reason in just few minutes.

 

What is Data Driven Attribution Model and why it is incrementally better?

In short this model is somewhat a practical implementation of the attribution model I proposed back in 2011 called Proportional Multi Touch Attribution Model.

It seems someone from the Google Analytics Attribution team might be reading my blog posts :)

distribution-credit-conversion

 

  • Just like ‘Proportional Multi Touch Attribution Model‘, the data driven attribution model assign credit for conversions to different marketing channels/touch points in proportion to their contribution in conversions.
  • The marketing channel/touch point which assists the most gets the maximum credit for conversion regardless of it being the first touch, last touch or middle touch.
  • All other channels/touches would get credit in proportion to their contribution in the conversion.

Since the assignment of credit for conversions is decided on the basis of the present conversion data and not on the basis of the position of the touch points, the attribution is data driven and hence the model is known as data driven attribution model.

Note:Google used the word ‘impact’ instead of the word ‘contribution’ when it talks about data driven attribution model.

The really cool thing about data driven model is that, it automatically assign credit for conversions to different marketing channels/touch points based on your most recent conversion data and some algorithm used by Google.

Which means there is no need to assign arbitrary credit for conversions to different channels/touch points any more.

Let us determine how organic search is valued under ‘Data driven attribution model’.

Follow the steps below:

Step-1: Click on ‘Model Comparison Tool’ link (under Conversions > Attribution menu) in your Universal Analytics View (or profile)

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

Step-3: Compare the ‘last interaction’ model with ‘last non-direct click’ and ‘data driven’ models.

select-data-driven

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

Step-5: Now view the column named % change in conversion (from last interaction)’ for ‘organic search’:

percent-change-conversion2

Form this report we can conclude that the % of change in conversion for organic search from last interaction model to ‘Data driven’ attribution model is -1.04%.

That means that 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 1.04% less credit for conversions.

It also means that ‘organic search’ is overvalued by 1.04% under last click attribution model when this model is compared with ‘data driven’ attribution model.

 

So what insight we have got from this analysis?

The insight is that overall organic search is still undervalued but now by (21.61 -1.04)/2 = 10.28%

This percentage of 10.28% is much lower than the percentage of 15.10% we got when we didn’t use the ‘data driven model’.

But fortunately organic search is still undervalued and you can still demand more budget for your organic campaigns.

 

Model Explorer (or Data driven attribution model explorer)

Mode explorer tool is used to determine how data driven attribution model is assigning credit for conversions to different marketing channels/touch points.

This tool is available under Conversions > Attribution menu:

model-explorer

 

Model Explorer Universal Analytics

Through the report above we can see that data driven attribution model has assigned following credit for conversions to organic search:

Organic search has been assigned 16% credit for conversions when it is the first touch point before a conversion, 29% credit for conversions when it is the second touch point, 39% credit for conversions when it is the third touch point and 40% credit for conversions when it is the last touch point before a conversion.

Also note that

Data driven attribution model is valid only for a particular time period as this model automatically changes with the change in the conversion data.

This is the beauty of this attribution model. It is dynamic :)

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’. However the data I got didn’t make much sense to me.

Other post you will find informative: How to use Agile Analytics to quickly solve your Conversion problems 

 

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