# 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 a custom attribution model in Google Analytics.

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.

Get the E-book (52 Pages)

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: Log in to your Google Analytics Premium account and then navigate to the property 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: Give your new attribution model, a name (say ‘Display Advertising Model’) and then select ‘Data-Driven’ as baseline model:

1. An attribution model is a set of rules which is used to determine, how credit for conversions should be attributed to different marketing channels.
2. Baseline attribution models are the pre-built attribution models available in Google Analytics.
3. Custom attribution models are user-defined attribution models.

I selected the data-driven attribution model as the baseline model because it analyses the data (related to organic search traffic, direct traffic, referral traffic, uploaded cost data, etc) not only from my Google Analytics account but also from all those Google Accounts (like Doubleclick Campaign Manager, Google Ads, etc) which are linked to my GA account 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-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 in 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 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 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 which 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 which resulted in sales after seven 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-10: Click on the ‘Save and Apply’ button:

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

Step-12: Select ‘Conversions and 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 ‘display’:

Form 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, if you use the display advertising model (instead of the last interaction model) to distribute credit for conversions to display advertising then display advertising deserves 398.39% more credit for conversions.

It also means that ‘Display’ 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.

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 credit for conversions 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.

Other Articles on Attribution Modelling

1. How to analyse and report the true value of your SEO Campaign
2. How to valuate Display Advertising through Attribution Modelling
3. Understanding Shopping Carts for Analytics and Conversion Optimization
4. 6 Keys to Digital Success in Attribution Modelling
5. Google Analytics Attribution Modeling Tutorial
6. How to Measure and Improve the Quality of SEO Traffic through Google Analytics
7. How to explain attribution modelling to your clients
8. Default and Custom Attribution Models in Google Analytics
9. Understanding Missing Touchpoints in Attribution Modelling
10. What You Should Know about Historical Data in Web Analytics
11. Model Comparison Report Explained in Google Analytics Attribution
12. Data-Driven Attribution Model in Google Analytics – Tutorial
13. Conversion Lag Report Explained in Google Analytics Attribution
14. Selecting the Best Attribution Model for Inbound Marketing
15. How to do ROI Analysis in Google Analytics
16. Conversion Credit Models Guide – Google Analytics Attribution
17. Introduction to Nonline Analytics – True Multi Channel Analytics
18. Conversion Types Explained in Google Analytics Attribution
19. Attribution Channels Explained in Google Analytics Attribution
20. Differences Between Google Attribution & Multi-Channel Funnel Reports
21. Introduction to TV Attribution in Google Analytics Attribution 360
22. Conversion Credit Distribution for Attribution Models in Google Analytics
23. Conversion Paths Report Explained in Google Analytics Attribution
24. Attribution Model Comparison Tool in Google Analytics
25. Touchpoint Analysis in Google Analytics Attribution Modelling
26. Attributed Conversions & Attributed Revenue Explained in Google Attribution
27. Which Attribution Model to use in Google Analytics?
28. Google Attribution Access and User Permissions – Tutorial
29. Conversion Path Length Report Explained in Google Analytics Attribution
30. How to set up a data-driven attribution model in Google Analytics
31. View-Through Conversion Tracking in Google Analytics
32. Offline Conversion Tracking in Google Analytics – Tutorial
33. How to Create Custom Attribution Model in Google Analytics
34. 8 Google Analytics Conversions Segments You Must Use
35. You are doing Google Analytics all wrong. Here is why
36. How to Use ZMOT to Increase Conversions and Sales Exponentially
37. Connected Properties Explained in Google Analytics Attribution
38. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
39. How is attribution modelling helpful for ecommerce and non-ecommerce websites?
40. Conversion Time & Interaction Time Explained in Google Analytics Attribution
41. How to Allocate Budgets in Multi Channel Marketing
42. How Does Attribution Work?
43. Data-Driven Attribution Model Explorer in Google Analytics
44. Introduction to Attribution Beta – Attribution Project in Google Analytics