Data attribution helps us digital marketers understand the effectiveness and performance of our Google Ads campaigns. With the growing popularity of Google Ads, it’s essential to know how data attribution works. In this post, we will delve into data attribution and explore how it can be used to optimise Google Ads campaigns for maximum ROI.
What is Data Attribution?
Data attribution helps us identify which specific ads, keywords, or channels are responsible for driving conversions. By understanding that, we can make informed decisions about budget allocation, campaign optimisation, and overall marketing strategy. Google has announced that first click, linear, time decay and position-based attribution models will be phased out from July 2023. Their replacement, which is already in play, is the data-driven attribution model.
The newest and arguably the most advanced model so far. This model has been known to increase conversions by 6%. Data-driven attribution assigns credit at every touchpoint in the customer journey, allowing us to see exactly where those conversions are coming from.
This model attributes 100% of the credit to the final click that led to the conversion. It was the default model in Google Ads and is relatively simple. However, it overlooks the influence of other touchpoints that may have contributed to the customer journey.
As the name suggests, this model attributes all the credit to the first interaction that initiated the customer journey. It can be useful for understanding the initial touchpoints that led to awareness and interest.
In this model, equal credit is given to all touchpoints involved in the customer journey. It provides a more balanced view of how each interaction contributes to the conversion process
Time Decay Attribution
This model assigns more credit to touchpoints that are closer in time to the conversion. It acknowledges that interactions that occur closer to the point of conversion often have a more significant impact.
Also known as the U-shaped model, it gives 40% credit each to the first and last interaction, while the remaining 20% is distributed across the middle touchpoints. This model aims to strike a balance between the first-click and last-click attribution models.
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