Last Updated: February 25, 2022

Note: This article is related to attribution modelling in Google Analytics. If you are brand new to attribution modelling then I would suggest reading this article first: Google Analytics Attribution Modeling Tutorial.

You may have the following questions:

• How can my display advertising be valued from a different perspective?
• Is display advertising undervalued or overvalued, under the last click and last non-direct click attribution model?
• If I invest in display advertising, how much incrementality can the display channel bring to my business bottom-line?
• How can I make my display campaigns more effective?
• If I change my display ad spend, how it will affect my website conversions?

You can get answers to such questions by adjusting conversion credit for impressions while creating or editing a custom attribution model in Google Analytics:

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But before you can adjust conversion credit for impressions, you first need to set up view-through conversion tracking in Google Analytics.

Without view-through conversion tracking set up, you won’t get the option to adjust credit for impressions 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: Log in to your GA360 account and then navigate to the reporting view for which the data-driven attribution model is available.

Step-2: Navigate to ‘Model Comparison Tool’ report (under ‘Conversions’ > ‘Multi-Channel Funnels’):

Step-3: Set the date range of the ‘Model Comparison tool’ report to the last 3 months (or longer if required):

Step-4: Click on the ‘Select Model’ drop-down menu:

Step-5: Scroll down a bit and then click on the link ‘create new custom model’:

Step-6: Name your new attribution model (say ‘Display Advertising Model‘) and then select ‘Data-Driven’ as baseline model:

I selected the data-driven attribution model as the baseline model because it can algorithmically generate a custom attribution model based on the data not only from my Google Analytics account but also from all those Google and non-Google accounts which are linked to my GA account. 

The data-driven attribution model can analyze data from the following Google accounts:

• Google Ads
• Google Search Console
• BigQuery
• Campaign Manager 360
• Search Ads 360
• Display and Video 360 etc

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

This is because the lookback window option has no effect on attribution when you use the DDA model as the baseline model.

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Step-7: Switch on the toggle button next to ‘Adjust credit for impressions’:

Step-8: Click on the ‘Advanced option…’ link:

Step-9: Adjust credit for impressions like the one below:

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

In other words, I have set my custom attribution model, to give display ad impressions, two times more conversion credit than the other interactions on 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 and marketing.

The 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 that resulted in sales within six hours can be considered more effective than an ad impression that resulted in sales after one day.

Similarly, an ad impression that resulted in sales within one day, can be considered more effective than an ad impression that resulted in sales after seven days.

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

This makes it quite difficult to effectively assign conversion credit to a comparatively weak touchpoint, such as ‘ad impression’.

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

Step-10: Click on the ‘Save and Apply’ button:

This action will create a new attribution model which will take ad impressions on the conversion paths, into account and help in the valuation of display advertising:

Step-11: Compare the ‘Last Interaction’ model with the ‘Display Advertising Model’ and the ‘Last Non-Direct Click’ model:

Step-12: Select ‘Conversions & Value’ from the drop-down menu located in the middle of your ‘model comparison tool’ report:

Step-13: Now look at the column named ‘% change in conversion (from last interaction)’ for the ‘display’ channel:

From this report, we can conclude that the % of change in conversion for ‘display’ from the last interaction model to the display advertising model is 398.39%.

What that means is if you use the display advertising model (instead of the last interaction model) to distribute conversion credit to display advertising then display advertising deserves 398.39% more credit for conversions.

In other words, the ‘Display’ channel is undervalued by 398.39% under the last-click attribution model (which is used in GA multi-channel funnel reports by default), when this model is compared with the display advertising model.

The upward green arrow next to 398.39% indicates a positive change in conversions from the last interaction model.

Google Analytics makes 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 colour. However, if the change is negative and is 10% or more than the arrow gets red colour.

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

What that means, if you use the last non-direct click model (instead of the last interaction model) to distribute conversion credit to display advertising then display advertising deserves 236.88% more credit for conversions.

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

So what insight we have got from this analysis? 

The insight is that overall display advertising is undervalued.

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