Attribution in Retail Media: Why It’s More Complex Than It Looks!

When Did Attribution Become a Strategic Question?

To put it straight as an arrow, attribution in advertising tries to answer a deceptively simple question: which ad interaction actually drove the sale? 

But in retail media, that answer is rarely straightforward. Across retailers, agencies, and ad tech platforms, attribution logic varies wildly. Some platforms count only clicks. Others include views. Some stop counting after seven days, others after thirty. Some credit only the exact product advertised, while others include halo sales across SKUs or even categories!

For retailers operating retail media networks, this inconsistency isn’t just confusing for brands but directly affects revenue credibility, budget allocation, and long-term trust. As brands push harder for proof of incrementality, attribution has shifted from a technical metric to a strategic differentiator, especially as retail media platforms like Osmos push for more transparent, retailer-controlled measurement frameworks.

Attribution Is Many Decisions Stacked Together

When a shopper engages with multiple ads across search, display, and video; often over several days or devices, deciding which interaction deserves credit becomes complicated fast. This is where retail media attribution stops being a simple rule and becomes a system of choices. Every attribution framework is shaped by what you count, how you count it, and when you decide that influence expires.

a) Click vs. View: What Counts as Influence?

The first fork in the road is deciding whether attribution should be click-based, view-based, or a mix of both…

Click-through attribution is the most conservative approach. Credit is assigned only when a shopper actively clicks an ad and later completes a purchase. It’s clean, defensible, and easy to explain, which is why many retailers default to it.

But retail media isn’t only about last-click conversions. View-through attribution expands the lens by acknowledging that seeing an ad, even without clicking, can influence future buying behavior. This is especially relevant for upper- and mid-funnel formats like display and video, where visibility matters more than immediate action.

Many retail media platforms now allow configurable combinations, such as one- or seven-day view-through windows alongside longer click-through windows of up to thirty days. This flexibility helps retailers better reflect real shopping behavior, rather than forcing every campaign into a performance-only box. From a retailer’s perspective, offering both options isn’t about generosity; it’s about accuracy.

b) Attribution Windows: How Long Does Influence Last?

Attribution has an expiration date, whether platforms admit it or not. A one-day attribution window captures impulse-driven purchases. A thirty-day window reflects longer research cycles. Neither is inherently better, but both can distort results if applied blindly.

Retailers must strictly align attribution windows with category dynamics because fast-moving consumer goods often justify shorter windows, typically between one and seven days, while high-consideration purchases, like electronics or furniture, require longer windows that reflect real decision-making timelines…

Set the window too short, and retail media attribution underreports impact. Set it too long, and marketing attribution starts absorbing sales that would have happened organically anyway. The challenge isn’t picking a number, but defending why that number makes sense for your marketplace… especially in verticals like fashion and beauty marketplaces and grocery retailers adopting retail media where browsing behavior and purchase latency vary widely.

c) Single-Touch vs. Multi-Touch Attribution Models

Most retail media networks today rely on last-touch attribution. The final ad interaction before purchase gets full credit. It’s simple, operationally efficient, and easy to scale. But this simplicity often has its limits.

As retailers expand across onsite, offsite, and even in-store media, multi-touch attribution is gaining relevance. These models distribute credit across multiple interactions, acknowledging that awareness, consideration, and intent build over time, not in a single click.

Some models emphasize the first interaction to measure discovery. Others split credit evenly across touchpoints. More advanced approaches weight interactions closer to conversion more heavily. Each method adds nuance, but also complexity.

In practice, attribution models typically fall into a few well-understood approaches:

1. Last-touch attribution assigns 100% credit to the final ad interaction before purchase. For example, if a shopper sees three ads, clicks one, and then converts, the last ad interaction receives full credit.
2. First-touch attribution does the opposite, assigning 100% credit to the first interaction in the journey. This model is often used to understand awareness and discovery impact.
3. Linear multi-touch attribution distributes credit equally across all interactions. If a shopper is exposed to four ads before converting, each touchpoint receives 25% credit.
4. Time-decay multi-touch attribution weighs interactions closer to conversion more heavily. For instance, the final ad may receive 50% credit, the previous interaction 30%, and earlier exposures progressively less.

Multi-touch attribution demands cleaner data, stronger identity resolution, and deeper integration across ad servers, DSPs, and commerce systems. For many retailers, this level of marketing attribution is only achievable with the right ad tech foundation in place.

The Halo Effect Makes Attribution Messier Than It Sounds

Even with perfect tracking of clicks and views, attribution in retail media runs into another problem: products don’t exist in isolation.

Imagine a shopper clicks an ad for a specific beverage SKU and ends up buying a different flavour, a variant, or even a complementary product. Did the ad work? And if so, what exactly should it get credit for?

Retailers face a spectrum of attribution choices:

• Direct credit for the exact product advertised
  
  
• Credit for the same product in a different size or variant
  
  
• Credit across SKUs within the same category
  
  
• Broader halo credit across related brands or categories

Each option changes reported performance. More importantly, each option changes how brands perceive the value of a retailer’s media network. This is why standardization in retail media attribution is nearly impossible!

Product taxonomies, category structures, and on-site experiences differ across retailers. What counts as a meaningful halo in one marketplace may be irrelevant in another. For retailers, the real challenge isn’t choosing the “right” halo definition, it’s clearly communicating how attribution logic works, and why it aligns with real shopper behavior.

To see how leading retailers are already navigating attribution complexity, halo effects, and incrementality in real-world environments, explore how Osmos powered modern retail media teams are putting these frameworks into action through our retail media success stories.

Why Retail Media Attribution Is Harder Than Other Channels

In channels like paid search or social media, attribution typically lives within a single platform. Ads are served, clicks are tracked, and conversions are measured in one ecosystem. Well, retail media doesn’t have that luxury.

Here, ad delivery often happens through an ad server or supply-side platform. The purchase occurs in a commerce engine. Reporting lives in a separate analytics layer. Stitching these together requires consistent identity resolution, SKU-level accuracy, and cross-surface data alignment, areas where many retailers are still maturing. This fragmentation is why marketing attribution in retail media feels harder than in other channels. It’s not just a measurement problem, but also an infrastructure problem.

Retailers that partner with ad tech platforms capable of architecting customizable attribution logic gain a meaningful advantage. They can support click and view attribution, define halo boundaries, and compare onsite, offsite, and in-store performance within a single framework. More importantly, they create a foundation for what comes next.

That next step is incrementality.

When Attribution Meets Incrementality, Measurement Gets Real

Attribution will always have limits. It can show correlation, but it can’t prove causation. This is where incrementality changes the conversation.

Instead of asking which ad deserves credit, incrementality asks a tougher question: did the ad drive any additional sales at all? Incrementality analysis relies on controlled experiments; such as A/B tests or geo-based test-and-control setups to isolate true lift.

For retailers, this shift matters. Incrementality helps prevent attribution inflation, reduces overlap between campaigns, and gives brands confidence that retail media budgets are driving net-new value. Incrementality doesn’t replace retail media attribution, it validates it. Attribution explains the journey. Incrementality proves impact.

As incrementality analysis becomes more common, following the path already set by platforms like Google and Meta, retailers will be able to compete not just on reported ROAS, but on provable performance!

What This Means for Retailers Building Media Networks

Retail media is no longer a side initiative. It’s a core revenue engine, and attribution is the accounting system behind it. Retailers that treat attribution in advertising as a static rule set will struggle to earn long-term brand trust. Those that invest in flexible marketing attribution frameworks, transparent logic, and a roadmap toward incrementality will win larger budgets and longer partnerships.

So, the goal isn’t perfect attribution. That doesn’t exist. The goal is credible measurement that reflects real shopper behavior, supports strategic decision-making, and evolves toward incrementality over time. Because in retail media, attribution tells one side of the story, but incrementality proves the truth…

Curious how this looks in practice? Get a quick demo with Osmos to see how retailers are operationalising attribution and incrementality!