Implications for Click IDs and Their Role in Marketing Analytics

The latest in privacy and security innovations to hit marketers comes with Safari’s introduction of Link Tracking Protection in iOS 17 and macOS Sonoma. According to Apple, “Some websites add extra information to their URLs in order to track users across other websites. Now this information will be removed from the links users share in Messages and Mail, and the links will still work as expected. This information will also be removed from links in Safari Private Browsing.”

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Understanding the shift in tracking dynamics.
Safari’s Link Tracking Protection marks a significant move towards enhanced user privacy. By automatically removing tracking parameters such as Google and Facebook click IDs or other unique identifiers from URLs, the feature aims to curb cross-site tracking and protect user data. This change is automatic in Private Browsing mode and can be manually extended to all browsing sessions.Interestingly, while Safari has targeted certain identifiers, Urchin Tracking Module (UTM) parameters, a staple in marketing analytics, remain unaffected for now. This selective approach to tracking parameters presents a unique challenge for marketers relying on comprehensive data sets.
The Impact on Marketers.
The future of UTM parameters is in the air. While the change currently only impacts advertising click IDs and only in Safari private browsing, we can only assume this is just a first step to a larger phase-out of all tracking parameters, all in the name of privacy.This shift could lead to less effective campaign optimization and strategy development, as marketers might miss out on critical user interaction insights. The foundation of effective digital marketing is the ability to make data-driven decisions
The Solution.
Ultimately, marketers need to embrace change and innovation. This challenge is also an opportunity for more creative tracking and attribution approaches industry-wide. Marketers can explore novel attribution models that rely less on individual tracking parameters and more on aggregate data and machine learning algorithms to predict user behavior and campaign effectiveness.
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