# How can my display advertising be valued from a different perspective?

# Is display advertising undervalued or overvalued, under last click and last non direct click attribution model?

# If I invest in display advertising, how much incrementality can display channel bring to my business bottomline?

# How can I make my display campaigns more effective?

# If I change my display ad spend, how it will effect my website conversions?

You can get answers to such questions by adjusting conversion credit for impressions, while creating an attribution model.

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.

Before you can adjust conversion credit for ‘impressions’, while creating an attribution model, you first need to set up view-through conversion tracking in Google Analytics.
Without view-through conversion tracking setup, you won’t get the option to adjust credit for ‘impression’ while creating a new attribution model in Google Analytics.

If you are brand new to view-through conversions then read this article first: Understanding view-through conversions in Google Adwords where I explained in great detail, what view through conversions are and when they are reported.

 

In order to valuate display advertising through attribution modelling, follow the steps below:

Step-1: Navigate to ‘Model Comparison Tool’  (under Conversions > Attribution):

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

Step-3: Set date range to the last 3 months or longer.

Step-4: Click on the link ‘create new custom model’:

Step-5: Give your new attribution model, a name and then select ‘Data Driven’ as baseline model:

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

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

# Custom attribution models are user defined attribution models.

I selected ‘data driven’ attribution model as ‘baseline’ model because

it analyse the data (related to organic search traffic, direct traffic, referral traffic, uploaded cost data etc) 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, in order to algorithmically generate a custom attribution model.

Also when I choose data driven attribution model, I do not have to worry about how to set up the lookback window option to get optimum results from my analysis.

Note: lookback window option has no effect on attribution when you use ‘data driven’ attribution model as ‘baseline’ model.

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

Step-6: Switch on the toggle button next to ‘Adjust credit for impressions’:

Step-7: Click on the ‘Advanced option…’ link:

Step-8: Adjust credit for impressions like the one below:

Here I have set my attribution model, to give display ad impressions, two times more conversion credit than the other interactions in the conversion path, provided the display ad impressions occurs within 12 hours before a visit is recorded for my website which results in a conversion.

In other words, I have set my attribution model, to give display ad impressions, two times more conversion credit than the other interactions in the conversion path, provided a user completes a goal on my website within 12 hours after viewing (but not clicking) one of my display ads.

That means I can decide which ad exposure should be valued more, based on my understanding of the context/business/marketing.

Bear in mind that,

More an ad impression occurs closer in time to conversion, the more effective it can be considered, in driving conversion.

In other words, an ad impression which resulted in sales within 6 hours, can be considered more effective than an ad impression which resulted in sales after 1 day.

Similarly, an ad impression which resulted in sales within 1 day, can be considered more effective than an ad impression which resulted in sales after 7 days.

In a world of multi-channel, multi-device marketing, there are many touch points which can influence the buying behavior of a customer, in a short span of time.

This makes it quite difficult to effectively assign credit for conversions to a comparatively weak touch point, such as ‘impression’.

So I would suggest to keep the lookback window (not to be confused with the lookback window option in the custom attribution model) as short as possible.

 

Step-9: Click on ‘Save and apply’ button. This will create a new attribution model which will take ad impressions in the conversion paths, into account and help in the valuation of display advertising:

Step-10: Compare the ‘Last Interaction’ model with ‘Display Advertising Model’ and ‘Last Non-Direct Click’ model:

Step-11: Select ‘Conversions and Value’ from the drop down menu located in the middle of your ‘model comparison tool’ report:

Step-12: Now look at the column named ‘% change in conversion (from last interaction)’ for ‘display’:

Form this report we can conclude that, the % of change in conversion for ‘Display’ from last interaction model to ‘Display Advertising Model’ is 398.39%.

What that means, if you use ‘Display Advertising Model’ (instead of last interaction model) to distribute credit for conversions to Display advertising then the ‘Display Advertising’ deserves 398.39% more credit for conversions.

It also means that ‘Display’ is undervalued by 398.39% under last click attribution model (which is used in GA multi channel funnel reports by default), when this model is compared with ‘Display Advertising Model’.

The upward green arrow next to 398.39% 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 negative and is 10% or more than the arrow gets red color.

Similarly, from the report above, we can conclude that, the % of change in conversion for ‘Display’ from last interaction model to ‘Last Non-Direct click Model’ (which used by default for all GA non-MCF reports) is 236.88%.

What that means, if you use ‘Last Non-Direct click Model’ (instead of last interaction model) to distribute credit for conversions to Display advertising then the ‘Display Advertising’ deserves 236.88% more credit for conversions.

It also means that ‘Display’ is undervalued by 236.88% under last click attribution model, when this model is compared with ‘last non-direct click model’.

So what insight we have got from this analysis?

The insight is that overall display advertising is undervalued.

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

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7. How to do ROI Analysis in Google Analytics
8. Data-Driven Attribution Model Explorer in Google Analytics
9. Guide to Data Driven Attribution Model in Google Analytics
10. Conversion Credit distribution for Attribution Models in Google Analytics
11. You are doing Google Analytics all wrong. Here is why

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12. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
13. Introduction to Nonline Analytics – True Multi Channel Analytics
14. How to set up Data driven attribution model in Google Analytics
15. Google Analytics Attribution Modelling – Complete Guide
16. Understanding Shopping Carts for Analytics and Conversion Optimization

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17. View-through conversion tracking in Google Analytics
18. Understanding Missing Touch Points in Attribution Modelling
19. Guide to Offline Conversion Tracking in Google Analytics
20. How to explain attribution modelling to your clients
21. 6 Keys to Digital Success in Attribution Modelling

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22. How to use ZMOT to increase Conversions and Sales exponentially
23. How to Measure and Improve the Quality of SEO Traffic through Google Analytics
24. How to analyse and report the true value of your SEO Campaign
25. How to allocate Budgets in Multi Channel Marketing
26. What You Should Know about Historical Data in Web Analytics

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27. Google Analytics Not Provided Keywords and how to unlock and analyze them
28. Selecting the Best Attribution Model for Inbound Marketing
29. Introduction to TV attribution in Google Analytics Attribution 360
30. Cross Device Reports in Google Analytics via Google Signals