Touchpoint Analysis in Google Analytics Attribution Modelling

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 Modeling.

Introduction to Interaction (Touchpoint)

Interaction is an exposure to a marketing channel. Interaction is also known as ‘touch or touchpoint’. For example in the chart below, a visitor is exposed to 6 different marketing channels before he made a purchase:

conversion path

Each exposure is known by the name of ‘interaction’ in multi-channel funnel reports.

Note:  Google Analytics can record up to 5000 interactions per conversion path.

Types of Interactions

There are several types of interactions. For example:

  1. Interactions based on position – First interaction, middle interaction and last interaction. These interactions are also known by the name: first touch, middle touch and last touch which are more commonly used in the web analytics world.
  2. Interactions based on type – click interactions (which is basically clicking on ads), impression interactions (which is basically viewing an ad), direct visit interactions, etc.
  3. Interactions based on campaign or traffic source type – keyword interactions, campaign interactions, Facebook interactions, etc.

Any interaction other than the last interaction is called the assist interaction.

Any interaction other than the first and last interaction is called the middle interaction.

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Types of Interaction Analysis

In Google Analytics you can do two types of interaction analysis:

  1. Assisting Interaction Analysis
  2. First Interaction Analysis

types of interaction analysis

As the name suggests, the ‘Assisting Interaction analysis’ is the analysis of assist interactions (any interaction other than the last one) and the ‘First Interaction Analysis’ is the analysis of the first interactions.

You can do such type of analysis in the ‘Assisted Conversions’ report (under Multi-Channel Funnels).

Interaction Types for GDN Ads

If you have enabled GDN impression reporting, not only you will see the ‘Interaction Type’ drop-down menu in your multi-channel funnel reports but you will also see a new set of interactions types in the ‘Top Conversion Paths’ report:

interaction types menu

display impression interactions

Following are the new interactions (touchpoints) for GDN (Google display network) ads:

#1 ‘Impression’ Interactions/Touch Points – it refers to unclicked non-text display ad impressions

#2 ‘Click’ Interactions/Touch Points – it refers to non-text display ad clicks

#3 ‘Direct’ Interactions/Touch Points – it refers to unclicked non-text display ad impressions which resulted in direct conversions on your website.

#4 ‘Rich media’ Interactions/Touch Points – it refers to the following types of interactions:

  • Click on a video ad: play, pause, and resume button.
  • Turning a video ad to full screen
  • Expanding an ad

To learn more about these interactions types, read this article: View-through conversion tracking in Google Analytics

Not All Touch Points Are Equally Valuable

The exposure to a marketing channel during the path to conversion is known as interaction, touch or touchpoint.

So if you are exposed to one marketing channel in your path to conversion then your conversion path includes only one touchpoint. Similarly, if you are exposed to 4 marketing channels in your path to conversion then your conversion path will include 4 touchpoints.

Now consider I followed this conversion path:

conversion path2

Here I am exposed to 7 different acquisition channels before I made a purchase. Since each of this exposure is considered as a touchpoint, there are 7 different touchpoints in my conversion path.

Now let us see how credit for the conversion is distributed to different touchpoints under different attribution models:

distribution for conversion credit

In the case of the first touch attribution model 100% credit for the conversion is attributed to the first touch. So according to the first touch model, my reading of the blog post gets all the credit for the conversion. But this is not true.

As you can see 6 other acquisition channels have also played an important role in my path to conversion.

In the case of last-touch attribution model 100% credit for the conversion is attributed to the last touch. So according to the last touch model, direct traffic gets all the credit for conversionsAgain this is simply not the case as there are 6 more channels in play.

In the case of the Linear attribution model, all touchpoints get equal credit for the conversion. So according to the linear model, all 7 touch points are equally important in my path to purchase. But this is also not true. 

I read product reviews and went to the product comparison website before making a purchase. These two touches were more valuable to me than the exposure to the blog post, display ad, PPC ad and organic search listing as they played a very important role in my purchase decision.

Had I not been satisfied with the product review or pricing, I wouldn’t have made the purchase in the first place.

In the case of the time decay model, the touchpoints which are closest in the time to conversions get more credit.

So according to the time decay model, my exposure to an organic search result and visiting the website directly should get more credit than to my exposure to product reviews and pricing.

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Again such type of credit distribution is not accurate as had I not been satisfied with the product review or pricing, I wouldn’t have made the purchase in the first place.

The time decay model is the modified last touch attribution model. Although it is crappy, it is less crappy than the last touch and other GA models.

One issue worth pointing out about giving more credit to last touches/interactions:

Last interactions act as a last point of contact prior to purchase. By the time a customer experience last touches to marketing channel(s), a purchase decision has already been made. So it doesn’t really matter which channel closed the sale when it comes to allocating budget.

In the case of the Last Non-Direct Click attribution model 100% credit for the conversion is attributed to the last non-direct click. According to this model, organic search gets all the credit for the conversion. So this model also gives an incorrect picture of the conversion path.

In the case of Last Adwords Click attribution model 100% credit for the conversion is attributed to the last Adwords click. So according to this model, paid search result gets all the credit for the conversion. But this is also not a true representation of my buying behavior.

In the case of the Proportional Multi-touch attribution model the credit is distributed to touches in proportion to their contribution in the conversion.

The acquisition channel which assists the most gets the maximum credit for conversion and maximum resources are allocated to it regardless of it being the first touch, last touch or middle touch.

All other touches would get credit in proportion to their contribution to the conversion path.

So according to this model, exposure to product review and product comparison websites get more credit for conversion than all other touchpoints as they played a key role in the decision-making process and my purchase journey.

Note: The proportional multi-touch attribution model is my model and GA does not directly support it. But it indirectly supports it through data driven attribution model (only available to GA premium customers at present).

The Concept of Missing Touchpoints in Conversion Paths

Google Analytics provides the best attribution modelling capabilities in the world but it has its own drawbacks.

You need to be aware of following limitations before you interpret GA multi-channel funnel and attribution reports:

#1 Google Analytics and Adwords attribution modelling is based on only ‘known’ touchpoints

#2 Both Google Analytics and Google Adwords, record and report attribution for only online touchpoints.

#3 Both Google Analytics and Google Adwords, do not record and report attribution for ‘all’ digital marketing channels

#4 Both Google Analytics and Google Adwords, do not record and report attribution across devices and browsers

#5 Adwords conversion paths in Google Analytics do not include impression interactions (unless you use GA premium)

#6 Filtered views omit certain touchpoints in conversion paths

If you wish to minimize the number of missing touchpoints in your conversion path, you need to integrate as much data as possible from different data sources.

To learn more about the concept of missing touchpoints in a conversion path, read this article: Understanding Missing Touchpoints in Attribution Modelling

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.

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