Attribution Models and Discrepancies in Conversion Data
As businesses invest more in digital marketing, they rely on data-driven insights to inform their marketing strategies. Attribution models, which track the journey a customer takes before converting, provide valuable insights into which marketing channels are driving conversions. However, relying solely on attribution models can lead to discrepancies in conversion, return on ad spend (ROAS), and revenue data. In this blog, we will discuss why attribution models can lead to discrepancies in data and how to mitigate these discrepancies.
What are Attribution Models?
Attribution models are used to track the touchpoints a customer interacts with before making a conversion. They help businesses understand which channels and campaigns are driving conversions, and which touchpoints are most valuable in the conversion process. There are several attribution models, including first-click attribution, last-click attribution, and multi-touch attribution. Each model attributes different weight to each touchpoint in the customer journey, which can impact the data collected.
Why Attribution Models Can Lead to Discrepancies in Conversion Data
Attribution models can lead to discrepancies in conversion data because they do not capture the entire customer journey. For example, a customer may see a display ad, click on a search ad, and then convert after visiting the website directly. In this case, the attribution model may only attribute the conversion to the last touchpoint, which was visiting the website directly, ignoring the contribution of the other channels. This can lead to an inaccurate representation of the impact of each channel on the customer journey and can lead to an incorrect allocation of marketing budget.
Why Attribution Models Can Lead to Discrepancies in ROAS and Revenue Data
Attribution models can also lead to discrepancies in ROAS and revenue data. ROAS measures the amount of revenue generated from a specific ad spend, while revenue data measures the total amount of revenue generated. Attribution models can impact these metrics by overvaluing or undervaluing certain channels. For example, if a customer interacts with a display ad, then clicks on a search ad, and finally converts after visiting the website directly, the attribution model may attribute the conversion to the last touchpoint, which was visiting the website directly. This may undervalue the impact of the display and search ads on the customer journey and overvalue the impact of the website. This can lead to inaccurate ROAS and revenue data.
How to Mitigate Discrepancies in Attribution Models
To mitigate discrepancies in attribution models, businesses can use a combination of attribution models and data analysis. Using multiple attribution models can provide a more accurate representation of the customer journey and help identify which channels are most valuable at each stage of the journey. Additionally, businesses can use data analysis to identify trends and patterns in the data that may indicate discrepancies. This can help identify areas where adjustments need to be made to the attribution model.
Conclusion
Attribution models are an essential tool for tracking customer journeys and identifying which marketing channels are driving conversions. However, relying solely on attribution models can lead to discrepancies in conversion, ROAS, and revenue data. To mitigate these discrepancies, businesses can use a combination of attribution models and data analysis to provide a more accurate representation of the customer journey. By doing so, businesses can make more informed decisions about their marketing strategies and allocation of marketing budget.