AppsFlyerWhat eCommerce brands get wrong about retail media measurement
Summary
The article highlights a critical issue in retail media measurement: network-reported ROAS varies by up to 63% across platforms due to methodological differences like attribution windows and conversion definitions. This creates a "fragmentation tax" for brands working with multiple networks, as each network's data is irreconcilable. The problem isn't flawed data but that closed-loop measurement is designed for network-specific reporting, not cross-channel decision-making.
Brands scaling budgets across networks without independent measurement make portfolio decisions on unreliable data. Independent measurement provides a unified layer where the same attribution logic applies across all networks, turning network reports into inputs rather than verdicts. This enables accurate cross-network comparisons and budget allocation.
The necessary signal infrastructure already exists from mobile performance marketing, which solved similar governance challenges. However, recent acquisitions (Publicis acquiring LiveRamp, WPP taking over InfoSum) introduce conflict-of-interest risks in measurement. For ad ops decision-makers, the key takeaway is to invest in independent measurement to reconcile retail media data and make portfolio-level decisions with confidence.
Analyst Note
The 63% ROAS fluctuation across retail media networks isn't a data quality issue—it's a structural gap that directly impacts how ad ops professionals allocate budgets across channels. As brands scale to an average of six networks, the fragmentation tax compounds: each network's proprietary attribution logic (varying windows, view-through weighting, incrementality definitions) makes cross-network comparisons unreliable. The key implication for UA teams is that relying on network-reported ROAS for portfolio optimization introduces systematic misallocation—a performance difference may simply be a methodology difference.
This article arrives at a critical moment: as privacy regulations and signal loss pressure all measurement frameworks, retail media's closed-loop design becomes both a strength (closed-loop purchase data) and a weakness (siloed, irreconcilable metrics). The reference to mobile measurement's governance infrastructure is instructive—the industry previously solved similar challenges with independent measurement layers. For ad ops professionals, the practical takeaway is clear: without a unified attribution layer that applies consistent logic across networks, 'optimizing' across retail media becomes guesswork.
The commentary underscores that the conflict-of-interest risk—now extending to measurement providers as they consolidate—is an operational reality that teams must account for when building their measurement stack.
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