Attribution Model Comparison Tool in Google Analytics

 

# 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?

# How can I make my PPC campaigns more effective?

# If I change my display adverising budget, how it will effect my website sales?

You can get answers to such questions by using the ‘Model Comparison Tool’ of Google Analytics.

Please note: This articles is related to Attribution modelling in Google Analytics.

If you are brand new to Attribution Modelling then I would suggest to read this article first: Beginners Guide to Google Analytics Attribution Modeling.

 

Introduction to Model Comparison Tool

As the name suggest, the model comparison tool (or Attribution Models Comparison Tool) is an attribution tool in Google Analytics, which 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.

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.

Attribution models in Google Analytics, can be broadly classified into two categories:

  1. Baseline Attribution Models
  2. Custom Attribution Models

 

Introduction to Baseline & Custom Attribution Models

Baseline attribution models (also known as default models) are the pre-built attribution models, available in Google Analytics.

Custom attribution models are user defined attribution models.

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

  1. First Interaction
  2. Last Interaction
  3. Linear
  4. Position based
  5. Time Decay
  6. Last Non Direct click
  7. Last Adwords click
  8. Data Driven (available only in GA premium)

To view these baseline attribution models, follow the steps below:

Step-1: Navigate to ‘Model Comparison Tool’  (under ‘Attribution menuConversions) in your GA view:

model comparison tool

Step-2: Click on ‘Select Model’ drop down menu:

select model drop down menu

You can then see the list of all available baseline attribution models (default models) in your GA view:

attribution model lists

 

Practical use of ‘Model Comparison Tool’

Let us use ‘Model Comparison Tool’ to determine how organic search can be valued from different perspective.

Follow the steps below:

Step-1: Navigate to ‘Model Comparison Tool’  (under ‘Attribution menuConversions) in your GA view:

model comparison tool

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 tool2

Now you may want to know, why I selected these 3 particular attribution models for analysis.

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

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

I selected ‘time decay’ model because it is less crappy than other attribution models (First interaction, Linear, Last Adwords click and position based models).

 

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

conversion and value

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

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%.

What that means, 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.

In other words, ‘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.

# Google 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 positive and is less than 10% than the arrow gets grey 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%.

What that means, 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.

In other words, ‘organic search’ is undervalued by 8.60% under last click attribution model (when this model is compared with ‘time decay’ attribution model).

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

Now the million dollars question,

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.

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 media etc.

 

Using Data Driven Attribution Model to valuate organic search channel

Now since I have the option to use ‘data driven attribution model’, I would prefer to use it, instead of ‘time decay’ model:

model comparison tool3

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

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

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

change in conversion

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 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 last click attribution model (when this model is compared with ‘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%

 

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


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Himanshu Sharma

Certified web analyst and founder of OptimizeSmart.com

My name is Himanshu Sharma and I help businesses in finding and fixing their Google Analytics and conversion issues.
  • More than ten years' experience in SEO, PPC and web analytics
  • Certified web analyst (master level) from MarketMotive.com
  • Google Analytics certified
  • Google AdWords certified
  • Nominated for Digital Analytics Association Award for Excellence
  • Bachelors degree in Internet Science
  • Founder of OptimizeSmart.com and EventEducation.com
I am also the author of the book Maths and Stats for Web Analytics and Conversion Optimization If you have any questions or comments please contact me