Privacy-first analytics governance in the era of cookieless commerce

The shift toward a cookieless commerce scenario has fundamentally altered the way organizations think about collecting, analyzing, and governing digital data. The withdrawal of third-party cookies, together with growing concerns about regulatory scrutiny and consumer expectations regarding privacy, has created challenges for traditional analytics models, which relied on exhaustive cross-site tracking. Many organizations now rely on privacy-first analytics methods based on first-party data, consent-aware data collection, and privacy-preserving computational methods. This research review explores how privacy-first analytics can be governed by drawing on information from academic publications, industry frameworks, and regulatory recommendations. It explores the role of governance mechanisms such as organizational roles, policy structures, technical controls, and measurement oversight in enabling compliant, trustworthy analytic practices while also providing analytic utility. In addition, the review examines the primary methods of measuring analytic performance in a cookie-less environment, such as modeled attribution and clean room collaborations, and aggregated reporting. By illustrating the interrelationship between governance and technical issues, the review identifies key challenges, trade-offs, and opportunities for future research to support the development of sustainable analytic practices in privacy-centric digital ecosystems.