How to valuate Display Advertising through Attribution Modelling

# 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 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.

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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: Login to your Google Analytics Premium account and then navigate to the property for which data-driven attribution model is available.

Step-2: Navigate to ‘Model Comparison Toolreport (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 ‘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 ‘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 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.

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

adjust credit for conversions

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

advanced option link

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

adjust credit for impressions

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 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-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:

display advertising model

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’:

percent change coversions

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 is 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.

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

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Himanshu Sharma

Digital Marketing Consultant and Founder of Optimizesmart.com

Himanshu helps business owners and marketing professionals in generating more sales and ROI by fixing their website tracking issues, helping them understand their true customers' purchase journey and helping them determine the most effective marketing channels for investment.

He has over 12 years of experience in digital analytics and digital marketing.

He was nominated for the Digital Analytics Association's Awards for Excellence. The Digital Analytics Association is a world-renowned not-for-profit association that helps organisations overcome the challenges of data acquisition and application.

He is the author of four best-selling books on analytics and conversion optimization:

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