Understanding and interpreting data from multiple sources is critical for marketers seeking to optimize their strategies and maximize their ROI. However, when it comes to comparing metrics between Google Analytics (GA) and affiliate tracking platforms, discrepancies often arise, leaving brands puzzled and seeking clarity. To help cut some of the noise, let’s dive into some of the complexities of reconciling data from these two platforms and provide actionable insights to help you navigate the reporting maze effectively.
Why the Discrepancies?
First and foremost, it’s essential to acknowledge that differences between GA and affiliate platforms are to be expected. These disparities stem from variations in data collection methods, channel assignment rules, and attribution models employed by each platform. While GA focuses on tracking website usage metrics and measuring cross-channel marketing effectiveness, affiliate platforms like Partnerize are tailored to manage partnerships and drive revenue. Understanding the unique purposes and functionalities of each platform is crucial for interpreting and reconciling their metrics effectively.
Data Collection Dilemmas
One of the primary sources of discrepancy lies in how revenue and other metrics are collected and calculated. For example, differences in revenue calculation logic, such as inclusion/exclusion of shipping and tax, can lead to variations in reported revenue figures. Moreover, variations in data collection mechanisms, such as server-to-server (S2S) vs. pixel-based tracking, can further contribute to disparities in metric values. Even if both platforms use the same tracking mechanism, conditional logic, and implementation nuances can result in varying metric values.
Channel Assignment Challenges
Another area of contention is channel assignment, particularly in the context of last-click attribution. While affiliate platforms like Partnerize attribute every order to the ‘affiliate’ channel, GA employs hierarchical processing rules that may prioritize other channels over affiliates in certain scenarios. Additionally, anomalies in GA’s assignment logic, such as automatic assignment to ‘Organic Shopping’ for partners with specific names, can further complicate channel attribution. Discrepancies in campaign conversion windows and attribution models can also contribute to variations in reported metrics.
Making Sense of Attribution Models
Understanding the nuances of attribution models is crucial for reconciling metrics between GA and affiliate platforms. With multiple attribution models available in GA, such as first-click, last-click, and data-driven models, variations in the volume of revenue assigned to affiliates are to be expected. While GA’s proprietary attribution algorithms remain opaque, it’s essential to recognize that different models may prioritize certain channels over others, potentially influencing the distribution of credit across marketing channels.
Navigating the Reporting Maze
While disparities between GA and affiliate platform metrics are inevitable, brands can navigate the reporting maze by adopting a strategic and nuanced approach to measurement and analysis. By understanding the unique functionalities and limitations of each platform, monitoring and comparing metrics regularly, and proactively troubleshooting implementation issues, brands can gain a comprehensive understanding of their marketing performance and optimize their strategies for success in an increasingly complex digital landscape.
Partnerize users can access our tutorial on comparing GA data vs affiliate data on our KnowledgeBase found here for the Partnerize platform and here for Ascend.
