Retail Media Measurement: Attribution Across Walled Gardens

Can you really measure retail media across walled gardens without losing accuracy—or speed?

Retail media spend is surging, but measurement hasn’t kept pace. Each network (Amazon Ads, Walmart Connect, Roundel, Instacart, etc.) limits visibility, defines conversions differently, and reports on its own cadence. The result: inconsistent ROAS, double counting, and stalled optimization.

This outline breaks down a pragmatic path: center measurement on first-party data, use privacy-safe collaboration, and combine MMM, MTA, and incrementality to compare networks fairly. Switchboard brings the data foundation—automated ingestion across retail and ad platforms, rigorous normalization, anomaly alerts, and audit-ready delivery to your warehouse—so your team can trust the numbers and act daily.

The Retail Media Boom—and the Measurement Gap

The rapid growth of retail media has opened new avenues for brands to connect with consumers directly at the point of purchase. However, this expansion has also exposed significant challenges in accurately measuring the true impact of these campaigns. As retail media networks multiply, so do the complexities in data interpretation and attribution, creating a notable measurement gap that marketers must navigate carefully.

Why walled gardens complicate truth-finding

Retail media platforms often operate as walled gardens, controlling access to their data and limiting transparency. This creates several obstacles for marketers trying to assess performance objectively:

• Fragmented taxonomies, lookback windows, and attribution models vary widely across platforms, making return on ad spend (ROAS) comparisons akin to apples-to-oranges evaluations.
• Access to granular, log-level data is typically restricted. Instead, marketers receive cohort-level reports that obscure individual user paths and audience overlaps, hindering precise analysis.
• Signals from onsite placements (within the retailer’s ecosystem) and offsite placements (external sites or apps) are often blended, complicating conversion de-duplication and inflating or deflating performance metrics inconsistently across networks.

These factors collectively make it difficult to establish a single source of truth, forcing marketers to rely on imperfect data that can misrepresent campaign effectiveness.

Dashboards aren’t enough

While dashboards provide a convenient snapshot of campaign results, they often fall short in capturing the full story behind retail media performance:

• Last-click attribution dominates many dashboards, disproportionately crediting the final touchpoint and masking the influence of upper-funnel activities and retail media assists that contribute earlier in the customer journey.
• Generative summaries or automated insights derived from raw data require a well-governed, accurately modeled data warehouse. Without this foundation, these summaries risk producing misleading conclusions that can steer decision-making off course.

In essence, dashboards are a starting point but not a comprehensive solution for understanding the nuanced impact of retail media investments.

Define “good” before you scale spend

Before increasing retail media budgets, it’s critical to establish clear, consistent definitions of success and measurement standards. This involves:

• Creating daily, normalized data tables keyed by product, store, audience segment, geography, and time to enable granular performance tracking.
• Implementing versioned metrics and dimensions alongside repeatable backfills to maintain data integrity and allow for historical comparisons.
• Setting up rigorous data quality checks and monitoring processes to catch anomalies early and ensure reliability.
• Ensuring finance-ready reconciliation that aligns sales data, media costs, and retailer-reported conversions to provide a holistic and trustworthy view of campaign ROI.

By defining what “good” looks like with robust data infrastructure and governance, marketers can confidently scale retail media spend while minimizing the risks associated with measurement inaccuracies.

Make First-Party Data the Measurement Spine

In today’s complex digital landscape, first-party data has become the cornerstone for accurate measurement and meaningful insights. Relying on your own customer data not only enhances precision but also respects privacy regulations and builds trust. To truly make first-party data the backbone of your measurement strategy, it’s essential to focus on identity management, privacy-safe collaboration, and unified data infrastructure.

Identity and consent, by design

At the heart of effective measurement lies a robust identity framework that respects user consent. Using hashed emails, loyalty IDs, and CRM keys allows brands to identify customers without exposing personally identifiable information. However, this must be paired with strict consent governance to ensure compliance with privacy laws and maintain customer trust.

Mapping brand-specific IDs to retailer IDs is another critical step. This can be achieved through secure environments like clean rooms or partner ID graphs, which facilitate data joins without compromising privacy. Documenting which joins are permissible helps maintain transparency and accountability, reducing risks associated with data misuse.

Privacy-safe collaboration with clean rooms

Clean rooms have emerged as a practical solution for brands and retailers to collaborate on data without sharing raw information. Platforms such as Amazon Marketing Cloud, Google Ads Data Hub, Snowflake Native or Powered clean rooms, and AWS Clean Rooms offer secure environments where data can be joined and analyzed safely.

These tools provide valuable cohort insights and enable secure data joins, but they come with trade-offs. They require specialized skills and time to model queries effectively, and typically only allow aggregate outputs to be shared, limiting granular data access. Despite these challenges, clean rooms represent a privacy-conscious way to unlock collaborative measurement.

Unify retail, ad, and commerce data in your warehouse

To maximize the value of first-party data, it’s crucial to bring together diverse data sources into a single, coherent system. This means ingesting data from platforms like Amazon Ads, Walmart Connect, Target Roundel, Instacart, Criteo/CitrusAd, Meta, Google, as well as your own ecommerce and point-of-sale systems.

Once ingested, data must be normalized—aligning schemas, calendars, currencies, and product hierarchies—to ensure consistency and comparability. Automated tools like Switchboard can simplify this process by providing connectors, handling backfills, performing quality assurance, and delivering clean data to your cloud warehouse.

By unifying these data streams, brands gain a comprehensive view of customer journeys and campaign performance, enabling more informed decisions and precise measurement anchored in their own first-party data.

Cross-Network Attribution that Survives Walled Gardens

In today’s fragmented advertising landscape, marketers face the challenge of accurately attributing performance across multiple platforms—many of which operate as walled gardens. These closed ecosystems limit data sharing, making it difficult to get a unified view of how campaigns perform across channels. To navigate this complexity, it’s essential to apply the right measurement approaches, establish clear reconciliation rules, and leverage operational tools that streamline data integration and analysis.

Right tool for the question: MTA, MMM, incrementality

Choosing the appropriate attribution method depends on the specific business question and data availability. Here’s how the main approaches differ and complement each other:

• Multi-Touch Attribution (MTA): Best suited for optimizing channels and audience segments where detailed user-level or cohort-path data exists. MTA tracks individual user journeys across touchpoints, providing detailed insights into which interactions contribute to conversions.
• Marketing Mix Modeling (MMM): Ideal for budget allocation decisions across networks, especially when offline channels and seasonal effects play a role. MMM uses aggregated data to estimate the impact of various marketing activities on sales, helping to balance spend across platforms.
• Incrementality Testing: Employs geo holdouts, ghost bids, or PSA (Platform-Specific Attribution) tests to measure the true lift generated by campaigns beyond what would have happened organically. This approach is critical for calibrating platform-reported performance metrics and avoiding over-attribution.

Each method has strengths and limitations, so combining them often yields the most reliable insights. For example, MMM can guide high-level budget decisions, while MTA and incrementality tests refine channel-level tactics.

De-duplication and reconciliation rules

When aggregating data from multiple sources, it’s crucial to standardize how conversions are counted and attributed to avoid double counting or misalignment. Key practices include:

• Defining consistent lookback windows and conversion hierarchies to determine which touchpoint claims credit for a sale.
• Establishing a primary source of truth for sales attribution, often by reconciling retailer-reported sales with brand point-of-sale (POS) or e-commerce data.
• Documenting any overrides or manual tie-outs to explain discrepancies and maintain transparency.
• Using metrics like TACoS (Total Advertising Cost of Sale) and contribution margin to compare network performance on a level playing field, accounting for differences in cost structures and sales impact.

These rules ensure that attribution data is clean, consistent, and actionable across all marketing channels.

Operationalize measurement with Switchboard

To manage the complexity of cross-network attribution at scale, automation and centralized workflows are essential. Platforms like Switchboard help by:

• Automating data ingestion and normalization from retail media, paid social, and search channels, reducing manual effort and errors.
• Delivering audit-ready datasets directly to your data warehouse, with daily anomaly alerts that flag unusual CPM or conversion rate swings for timely investigation.
• Providing pre-modeled KPIs and dashboards focused on pacing, return on ad spend (ROAS), and incrementality inputs, enabling faster decision-making.
• Offering success engineer support to help interpret results and optimize campaigns based on the integrated data.

By operationalizing measurement in this way, marketers can maintain a clear, consistent view of performance across walled gardens and open channels alike, making attribution a practical tool rather than a theoretical exercise.

Summary and next step

Retail media can drive growth, but only with a measurement spine rooted in first-party data, privacy-safe collaboration, and a blended toolkit (MMM + MTA + incrementality). Build on a governed, query-ready warehouse and set clear rules for reconciliation and de-duplication.

Switchboard provides the operational backbone—connectors to major retail media networks, dependable normalization, monitoring, and warehouse delivery—so marketing and RevOps teams can compare networks, adjust budgets, and report with confidence. Ready to see it in action? Schedule a personalized demo today!