Data Driven Attribution Model in Google Ads (Adwords)

Customers do not always click on your Google ads and then make a purchase straightaway.

They generally start their purchase journey by doing multiple searches using generic search terms.

Then they refine their search queries as they get better understanding of what they are looking for.

Sometimes customers click on your ads just to return to your website.

During their purchase journey, customers may see/click several of your ads via different devices/browsers and over the course of multiple different sessions, days or even weeks.

As such, customers purchase journey is not as easy, as clicking on an Google ad and then making a purchase.

Different search terms, different ads and users experience on different devices can play an important role in shaping the purchase decision of your customer.

Then there are external factors like: weather, better price/deal offered by your competition, level and type of remarketing campaigns run by your competitors etc which can influence the purchase decision of a customer.

However such external factors are generally not taken into account by attribution models while calculating which ad interaction (ad click/ ad impression) should get more credit for conversions.

For the sake of simplicity, let us ignore these factors for now, from our attribution modelling.

By default, Google Ads use the last ad click’ attribution model.

According to this attribution model, the last ad click in a conversion path gets 100% credit for a conversion.

In other words, according to last click model, the last ad your customer clicked on, is solely responsible for generating a sale on your website.

But know you know that, this can not be true.

As long as you keep using last ad click attribution model, you will get inaccurate/incomplete insight from your Google Ads reports.

As long as you keep using last ad click attribution model, you will remain busy optimizing your Ad campaigns for last ad click and keep losing money.

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So what is the solution?

Should you use ‘last click model’ or ‘last non direct click model’ or some other attribution model offered by Google Ads?

As long as you use any rule based model (models which assign conversion credits based on some predefined rules) whether it is ‘first click’, ‘last click’, ‘time decay’ , ‘linear’ etc  you are more likely to assign conversion credits to wrong ad clicks / impression and loose money.

In order to determine the most effective ad campaigns, ad groups, ads or keywords for conversions, you need to move beyond ‘rule based models’ and use ‘algorithmic attribution models’.

The algorithmic attribution model offered by Google Ads is ‘Data Driven Attribution Model’.

This attribution model use conversion data from your Google Ads account to determine:

  • How people searched for your business and then turned into customers.
  • How each campaign, ad group, ads and keywords actually contributed, across a conversion path.
  • Which campaigns, ad groups, ads and keywords have the most influence / greatest effect on your customers’ purchase decision.
  • Which campaigns, ad groups, ads and keywords have the biggest impact on your website conversions.
  • Which campaigns, ad groups, ads and keywords are likely to generate more sales.

It is important to note that, in the context of Google Ads attribution modelling, a conversion path is just made of Google ad clicks and/or impressions.

The conversion path does not take the role of other marketing channels (like ‘organic search’, ‘social media’, ‘email’ etc) into account.

Following is an example of Google Ads conversion path:

generic impression > generic click > generic impression > brand click => Conversion

There are several types of Google Ads conversion paths:

  1. Click Paths
  2. Transition Click Paths
  3. Impression Paths
  4. Transition Impression Paths

I have explained all of these conversion paths in great detail in this article: Understanding Conversion Paths in Google Ads (Google Adwords)

The data driven attribution model uses a predictive algorithm (algorithm used to predict the users’ probability of making a purchase or completing some other conversion) to assign or reassign conversion credit to various ‘ad interactions’ in a conversion path according to the most recent conversion data.

It is a conversion probability model.

It does not distribute conversion credit just on the basis of ad interactions’ position.

In other words ‘data driven attribution model’ is not a rule/position based model.

Following are the examples of rule based attribution models available in Google Ads:

  1. Last Click
  2. First Click
  3. Linear
  4. Time Decay
  5. Position-based

Data driven attribution model is used to identify the most influential ad interactions at a particular point in time.

The most influential ad interactions are the interactions / touchpoints which lead to conversions or maximum conversions.

In order to identify the most influential ‘ad interactions’ at a particular point in time, the data driven attribution model:

#1 Compare the ‘conversion paths’ of your website users to each other. These website users can be: customers, non-customers, prospects (who abandoned the shopping cart or checkout) or customers who completed more conversion than the others.  

#2 Add or remove touch points in conversion paths to determine higher conversion probability.

For example, consider the following two Google Ads conversion paths:

Ad1 > Ad2 > Ad3     Conversion Probability 5%

Ad1 > Ad3    Conversion Probability 10%

Here the second conversion path has higher conversion probability.

What that means, if the Ad2 is not shown to users, there is a high probability of generating sales.

The data driven attribution model add or remove touchpoints algorithmically to assign/reassign conversion credit to various ad interactions and to show ad or ads combinations which have higher probability of generating sales or completing some other conversions.

Google recommends that you use an automated bidding strategy (like ‘target CPA’, ‘enhanced cost per click’) in order to benefit from a data driven attribution model. 

By default, data driven attribution model is not available in Google Ads account.

As such, a traditional MMM model is not suitable for carrying out digital marketing mix modelling aka attribution modelling.

Other Articles Related to Attribution Modelling in Google Ads

  1. Guide to Attribution Modelling in Google Ads (Google Adwords)
  2. Which Attribution Model to use in Google Ads (Google Adwords)?
  3. Cross device attribution reports in Google Ads (Google AdWords)
  4. How to change Attribution Model in Google Ads (Google Adwords)
  5. Setting up Data Driven Attribution Model in Google Ads (Google Adwords)
  6. Store visit conversion tracking in Google Ads (Google AdWords)
  7. Understanding the Analytics behind Google Ads (Google Adwords)
  8. Cross Account Conversion Tracking in Google Ads (Google Adwords)
  9. Understanding View-Through conversions In Google Ads (Google Adwords)
  10. Assisted Conversions Report in Google Ads (Google Adwords)
  11. Understanding Conversion Paths in Google Ads (Google Adwords)
  12. Attribution Modelling Reports in Google Ads (Google Adwords)
  13. How to test Attribution Model in Google Ads (Adwords)

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Himanshu Sharma

Certified web analyst and founder of

My name is Himanshu Sharma and I help businesses find and fix their Google Analytics and conversion issues. If you have any questions or comments please contact me.

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