# 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 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: Beginners Guide to Google Analytics Attribution Modelling.

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 set up, 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 analyses 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 Ads, etc) which are linked to my GA account, in order to algorithmically generate a custom attribution model.

Also when I choose the 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: The lookback window option has no effect on attribution when you use the ‘data-driven’ attribution model as the ‘baseline’ model.

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 occur 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.

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

Similarly, an ad impression that 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 touchpoint, such as ‘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-9: Click on the ‘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 a positive change in conversions from 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 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 the 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.