In the world of advertising, every click or view counts! That’s why we use attribution models to understand which ad channels really deliver results. These models help us find out where it is worth investing. So in today’s article we will look at how the attribution models in Google or Microsoft Ads help us decide which ads really work.
Tools needed for successful tracking?
To monitor and analyze the performance of campaigns in both Google and Microsoft Ads, it is necessary to use Analytics and GTM tools. They will help you to understand visitor behavior more deeply and optimize marketing campaigns based on data analysis.
Google Analytics 4 (GA4) as a key tool for tracking and analyzing website traffic, provides comprehensive data on visitor behavior, traffic sources, conversions and many other metrics. In the context of the attribution models in Google and Microsoft Ads, GA4 allows conversion paths to be tracked to see which channels and campaigns are contributing to goals. GA4 also allows you to compare different attribution models and their impact on campaign performance. In addition, it provides tools to analyze interactions between different marketing channels and identify the most effective combinations.
As a tag management tool, Google Tag Manager (GTM) enables easy deployment and management of tracking codes on your website without the need for intervention in the source code. When using GTM in conjunction with Google and Microsoft Ads, you can effectively deploy the conversion codes that are essential for tracking campaign success. GTM also allows you to create and manage remarketing audiences, improving your ad targeting capabilities. In addition, it ensures consistent and accurate tracking of events and conversions across different platforms and devices.
What is an attribution model?
An attribution model is a set of rules according to which conversions and their value are attributed to individual touchpoints (interactions with resources). They tell a lot about, for example, what your customers are looking for or what they are interested in at different decision-making stages. This information helps you, for example, figure out which parts of the buying process you should focus more money on. At the same time, they can also tell you where you should be more present in order to better engage your customers. Simply put, they determine how much recognition/credit individual ad interactions should receive. They provide a deeper understanding of the effectiveness of ad campaigns and help you improve conversion processes.
Types of attribution models
A variety of attribution models help you understand how different ad interactions contribute to conversions. The most popular models include:
Last click
The entire value of the conversion is attributed to the last ad that the customer clicked on before the conversion (e.g. the one they clicked on before purchasing the product).
Advantages: simple, easy to understand
Disadvantages: ignores previous interactions that may have contributed to the customer’s decision
Example: Customer first he clicks on a banner ad, then on a Facebook ad and finally on a Google/Microsoft ad, after which he makes a purchase. In this case, the last click attribution model attributes the entire value of the Google/Microsoft conversion to the ad.
First click
The conversion value is attributed to the first ad the customer clicks on. It can often be useful to assess which ads are most effective in driving customers back to your site.
Advantages: Identifies the channels that bring visitors to your website
Disadvantages: Ignores other interactions that may have contributed to the customer’s decision
Example: Customer clicks on a banner ad, then on Facebook advertisement and finally to Google/Microsoft advertisement, after which he makes a purchase. The first-click attribution model attributes the entire conversion value to the banner ad.
Linear
The value of the conversion is evenly distributed among all ad interactions in the customer journey.
Advantages: provides a balanced view of all interactions
Disadvantages: too simple, does not distinguish between different types of interactions
Example: A customer clicks on a banner ad, then on a Facebook ad and finally on a Google/Microsoft ad, after which he makes a purchase. The attribution linear model splits the conversion value equally between all three channels, so that each channel receives 1/3 of the conversion value.
Timing
More value is attributed to ads that the customer saw closer to the time of conversion.
Advantages: takes into account the possible importance of interactions closer to the conversion
Disadvantages: underestimates the importance of early interactions
Example: A customer clicks on a banner ad 10 days ago, on a Facebook ad 5 days ago and on Example: A customer clicks on a banner ad 10 days ago, a Facebook ad 5 days ago, and a Google/Microsoft ad today, after which they make a purchase. The time decay attribution model attributes the most conversion value to Google/Microsoft advertising, the middle value to Facebook advertising, and the smallest value to banner advertising, due to the length of time between interaction and conversion.
Location-based
Combines the first and last clicks, where both of these points receive a larger portion of the conversion value.
Advantages: combines the benefits of the first and last click models
Disadvantages: more complicated to implement and understand
Príklad: Zákazník klikne na bannerovú reklamu, potom na Facebook reklamu a nakoniec na Google/Microsoft reklamu, po ktorej uskutoční nákup. Pozíciou založený atribučný model pripíše 40% hodnoty konverzie bannerovej reklame, 40% hodnoty Google/Microsoft reklame a zvyšných 20% Facebook reklame.
Model based on data
Data-driven attribution model – DDA uses machine learning to assign value to conversions based on the analysis of available data about each conversion action.
Advantages: the most accurate model taking into account all interactions based on real data
Disadvantages: requires a large amount of data, so it can only be available for accounts with a sufficient number of conversions
Example: A customer clicks on a banner ad, then on a Facebook ad and finally on a Google/Microsoft ad, after which he makes a purchase. A data-driven attribution model will use machine learning algorithms to analyze all interactions and use the data to decide how to attribute conversion value. For example, it can determine that a banner ad contributed 20%, a Facebook ad 30% and a Google/Microsoft ad 50% to achieve a conversion, based on an analysis of historical data and customer behavior patterns.
Conclusion
Attribution models provide incredibly valuable insight into how different ad interactions affect conversions. A thorough understanding and proper use of these models will allow you not only to increase ROI, but also to better understand the journey of your customers from the initial contact to the final purchase. Constantly monitoring and updating them in line with changing trends and consumer behavior can dramatically improve your ability to attract and retain customers.
