Introduction
This article aims to clarify what the Attribution Model is and what influence it has on the conversion tracking process.
If you use Digital Marketing to promote your brand, products or services, you've certainly come across this term. It's very important to know it and understand the differences between the models.
Let's go?
Conversions and Attribution Model
To understand what an attribution model is, we first need to know what conversion is.
Whenever a goal is achieved on your page - be it a sale, a newsletter subscription, a video view, etc. - you have a conversion.
The main objectives for advertised businesses are sales, in the case of e-commerce, and generating contacts, for businesses that rely on leads.
With users becoming more and more connected, it's only natural that conversion paths become increasingly complex.
Which raises the question: how do we know when the decision to buy (or register) was made? And on which platform, on which device, through which ad?
So, the attribution model is the rule that determines how conversion credit is distributed to the different customer interactions along the conversion path.
In other words, the consumer's entire buying journey is analyzed, from the first contact to completion. And finally, different weights are given to each of these stages.
What are the Assignment Models?
When it comes to Google Ads, there are 6 different possible attribution models. They are:
Last Click Model
It gives all the credit (the weight) for the conversion to the last point of contact with the customer. Ignoring the influence that previous interactions had on the purchase.
The Last Click Model is used in most of the accounts that Pareto evaluates before starting work with us.
For example, a person clicks on a Google Ads ad for a particular store, but doesn't make a purchase that day. The next day, they decide to return to the store's website by typing the site directly into their browser. On that day, they complete the purchase.
In this last click model, 100% of the conversion would be attributed to the Direct channel. Disregarding the weight of the ad that the person clicked on.
First Click Model
As opposed to the last click model, it gives all the credit to the first point of contact, disregarding the impact of the user's other interactions.
Time Reduction Model
In this model, the higher the conversion credit, the closer the interaction was to the moment of conversion. Generating a result closer to the Last Click model, but taking into account the influence of all the interactions the customer went through until reaching the conversion.
Linear Model
The Linear Model, on the other hand, distributes the conversion weight equally among all the clicks in the user's path.
For example, if a person went through a Facebook ad, then a Google ad and finally went through direct, the conversion weight would be divided by 3.
Position Based Model
This model attributes 40% of conversion to the first click and 40% to the last click, with the remaining 20% attributed to other interactions in the medium.
This is the model most recommended by Pareto for tracking conversions.
With this model, we hope to encompass and fairly attribute all types of user interaction with the site.
Data-driven model
This is Google's own model, which uses machine learning to understand customer behavior and identify a conversion credit attribution pattern. This model requires extensive data, which necessitates a larger investment.
What is the importance of the Attribution Model?
Assigning conversion weight to all the clicks that led the user to the final decision makes Pareto Quantic understand your data better. It can then make more informed adjustments to your account.
This is why Pareto does not recommend using the first two models presented: Last Click and First Click. They ignore a large part of the customer's conversion journey.
Once we've established that all clicks should be attributed a portion of the conversion, the question is how?
How to distribute the conversion weights?
It is through the first contact that the customer begins to decide whether or not to do business with your company. It therefore makes sense to give greater weight to the first impression.
On the other hand, the last click is when the user closes the deal. So it also makes sense to give greater weight to the final interface.
Thus, despite being less widely used, the Pareto Group considers that the Position-Based model is the one with the best attribution rule.
It's worth noting that, despite being entirely data-driven and using machine learning to intelligently assign credit for conversions, the Data-Driven model requires a large amount of information to work accurately.
In other words, the data-based model is not applicable to every business and can only be used by large accounts.
Attribution models change the way we look at conversion data on the Google Ads platform. Not by changing your results, but by changing your perspective on which touchpoints are generating conversions.
This type of information is essential for decision-making when allocating resources. This means that Pareto Quantic has more complete data to optimize your account and seek better performance.
Conclusion
In this article, we present what the Attribution Model is and how it is important when analyzing conversion performance.
We've seen that they change the way we see the weight of each channel the customer comes into contact with before finalizing the purchase, but they don't change the data itself.
So, you can rest assured that the overall monitoring of the business remains the same! This means that changing the attribution model doesn't change the view of your business as a whole. Nor does the data you see in Google Analytics.
This change enables Pareto's artificial intelligence to allocate your resources more efficiently, bringing better results to your business.
In another article, we showed how there is a discrepancy in conversion data between platforms. Especially when it comes to Facebook.
If you want to know more about Conversion Rate, we recommend this other article we've prepared for you.
