Last Updated: May 26, 2022

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

They generally start their purchase journey by doing multiple searches using generic search terms. Then they refine their search queries as they get a 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, customer purchase journeys are not as easy as clicking on a Google ad and then making a purchase.

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Different search terms, different ads, and user’s 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 the last ad 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 the last ad click attribution model, you will get inaccurate/incomplete insight from your Google Ads reports.

As long as you keep using the last ad click attribution model, you will remain busy optimizing your ad campaigns for the 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 lose 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’.

Introduction to Data Driven attribution model in Google Ads

The data driven 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 a 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 (the 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

A 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 touchpoints 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 a higher conversion probability.

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

The data-driven attribution model adds or removes touchpoints algorithmically to assign/reassign conversion credit to various ad interactions and to show ads or ad combinations that have a 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, a data-driven attribution model is not available in a Google Ads account.

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

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