For advertisers in retail media, the era of easy “Return on Ad Spend” (ROAS) has ended. Thus, understanding how to use retail media analytics effectively is critical to achieving real success in a campaign. We have entered an era in which the primary metric of success is no longer “conversion” but “incrementality,” measuring the true lift of a campaign.
Retail media now commands nearly 30% of all US digital ad spend, up from 15% just four years ago, so getting this measurement wrong does not just skew a report; it misallocates real budget
This article will teach you how to use retail media analytics metrics across the funnel to optimize your campaign performance.
Key Highlights
- Beyond standard ROAS, successful brands focus on iROAS and new-to-brand rates to distinguish genuine growth and market share expansion from organic sales.
- Retail media analytics must be tailored to each stage, using reach and brand lift for top-of-funnel awareness, then shifting to conversion and real-time inventory data at the point of purchase.
- A robust measurement framework requires normalizing definitions across retailers to ensure accurate comparisons of campaign performance.
- Using AI-driven insights, such as Perion’s Outmax, bridges the gap between data and action.
What Is Retail Media Analytics?
Retail media analytics is the practice of measuring, analyzing, and optimizing advertising performance within retail media networks (RMNs) using first-party data, closed-loop attribution, and cross-channel signals.
Unlike traditional digital analytics, retail media analytics connects ad exposure directly to actual sales outcomes, both online and in-store, allowing advertisers to understand not just what converted, but what truly drove incremental revenue. By leveraging deep data insights, retailers and CPG brands can move away from fragmented strategies towards a unified media ecosystem through platforms like Perion One.
The focus of retail media analytics relies on four main aspects:
- Attribution: Linking media spend to purchases across digital and physical environments
- Incrementality: Determining which sales would not have occurred without advertising
- Performance measurement: Evaluating metrics like ROAS, iROAS, and new-to-brand acquisition
- Optimization: Using real-time data signals to adjust bids, creative, and audience targeting.
As retail media evolves, analytics is no longer just about reporting; it’s about enabling continuous, data-driven decision-making across the entire commerce funnel.
Learn what retail media is, its basics, and its benefits.
The Retail Media Metrics That Actually Matter for Analytics
In any retail media campaign, you use an array of metrics to measure success. However, to measure incrementality and impact, you need to look past vanity metrics and focus on the following:
ROAS
Return on ad spend serves as the baseline health indicator for any retail media investment, measuring the gross revenue generated per dollar spent on advertising. While it is a foundational metric, it is most effective for comparing the performance of different products or categories within a single retail ecosystem. A high ROAS indicates that your creative and targeting are resonating with shoppers. Still, you need to add other metrics as they do not account for the cost of customer acquisition, for example.
Incremental Return on Ad Spend (iROAS)
To truly understand the lift your marketing provides, you must look at incremental return on ad spend. This metric is designed to isolate the sales that occurred specifically because of an advertisement. It filters out organic purchases that would have been made regardless of the ad. Advertisers use iROAS to identify where their media spend is driving new revenue rather than feeding existing demand. This is harder than it sounds: 71% of marketers name advancements in analytics, attribution, and measurement models as their single greatest opportunity in retail media, precisely because so few feel they have cracked it.
New-to-Brand (NTB) Rate
The New-to-Brand (NTB) rate is the most vital metric for long-term growth, as it identifies the percentage of customers who have not purchased from your brand within a specific look-back window (usually 12 months). High NTB rates suggest that your retail media strategy is successfully acting as an acquisition engine, pulling shoppers away from competitors or bringing entirely new users into the category. For brand managers, this data is the key to justifying top-of-funnel investments that expand market share rather than just maintaining the status quo.
Share of Voice Within the Retail Environment
Share of voice measures your brand’s digital shelf space by tracking how often your products appear in search results and category pages compared to your competitors. In a crowded retail environment, maintaining a dominant SOV is critical to staying top of mind during the decision-making process. Brands can identify blind spots where competitors are outbidding them for premium visibility and adjust their programmatic strategies in real time to reclaim their position.
Customer Lifetime Value (CLV)
Customer lifetime value shifts the focus from a single transaction to the total revenue a shopper is expected to generate over their entire relationship with your brand. In retail media analytics, CLV helps determine how much you can afford to spend to acquire a new customer today based on their predicted future behavior.
Retail Media Analytics Across the Full Funnel
What’s the role of retail media analytics at every stage of the full marketing funnel? Unlike traditional silos, where brand awareness and performance marketing operate separately, using retail media analytics helps track a user from the first exposure to an ad to the final checkout.
Bottom of the Funnel
At the final stage of the buyer journey, analytics focus almost exclusively on conversion and immediate sales attribution. The goal here is to capitalize on high-intent shoppers who are already searching for specific products or navigating category aisles. Thus, we track conversions, cost-per-acquisition, and ROAS. Retail media analytics at the bottom of the funnel measure the direct effectiveness of sponsored search, for instance. Integrating retail and commerce data helps ensure every dollar spent is optimized to clear the digital shelf and maximize daily sales volume.
Middle of the Funnel
In the middle of the funnel, the focus shifts toward engagement and building preference. Here, we use metrics such as click-through rate, add-to-cart rate, ad frequency, and branded search volume lift. We are measuring how successfully our messaging moves a consumer from passive interest to active consideration. At this stage, advertisers usually leverage display advertising to retarget shoppers who engaged with the brand but didn’t complete the purchase.
Top of the Funnel
At the top of the funnel, the goal is to cast a wide net and introduce the brand to qualified new audiences. The metrics that matter in this phase are reach, frequency, brand lift, and share of voice. We measure the scale of the brand’s presence across high-impact environments like Connected TV and social platforms.
How to Set Up a Retail Media Measurement Framework
To use retail media analytics effectively, you need a standardized roadmap. Here is how to do it step by step.
Step 1: Define your commercial objective. A brand-building campaign will focus on reach, while a performance-driven campaign will look more closely at iROAS. Using a unified platform like Perion One allows stakeholders to align these objectives with a single point of execution.
Step 2: Standardize your definitions across networks. One of the greatest challenges in retail media is the lack of uniformity between different retail networks. Each retailer may have different attribution windows and different ways of counting new-to-brand customers. On average, brands already work with around six retail media networks in a given year, each with its own attribution rules, which is exactly why standardized definitions matter. Standardize these definitions to enable accurate cross-channel comparison.
Step 3: Build your measurement tools stack. You need a reliable platform to get actionable analytics. This involves selecting tools that can ingest first-party data from retailers and combine it with programmatic performance data. Incorporating high-impact creative tracking alongside standard performance metrics ensures you measure both the how and the where of your ad delivery.
Step 4: Establish your baseline. You cannot measure growth or incrementality without establishing a baseline. To do that, first analyze your organic sales velocity. The historical data will provide the context you need to calculate true lift and incrementality.
Step 5: Create a reporting structure. The final step is to build a reporting structure that delivers the right information so you can act at the right time. Automated dashboards that highlight key performance indicators and allow for quick adjustments. For instance, leveraging AI-powered insights from Outmax will allow your brand to move beyond static reports.
How to Act on the Data in Real Time
The gap between insights and action has long been a bottleneck for retail marketers. In the past, by the time a report was generated and analyzed, the opportunity to optimize the campaign had passed. Current programmatic tools make it possible to identify the data signals that actually matter, and instantly adjust the campaign.
When you add the power of AI to retail media analytics, you get precision and efficiency. Perion’s Outmax, our AI agent execution platform, detects untapped audiences and data patterns, increasing performance opportunities.
Retail Media Analytics for Different Stakeholders
Different stakeholders require different views of the same data set.
- For brand managers, for example, the priority is brand health and market positioning; they would like to see how retail media spend is impacting long-term perception and NTB rates.
- Media buyers and agencies want to measure executional efficiency. They require granular data on cost-per-acquisition and channel performance to ensure they are meeting the campaign’s tactical goals.
- E-commerce and category managers look at the data through the lens of the digital shelf. They need to know how media spend correlates with inventory turnover and how it affects their relationship with the retailer.
- CFOs and finance teams care primarily about the bottom line. For them, the conversation is centered on ROAS and the direct contribution of advertising spend to the company’s overall profitability. A unified analytics platform must be able to cater to all these needs simultaneously.
Why Retail Media Analytics Is Different From Every Other Channel You’ve Measured
Retail media analytics is unique because it closes the loop between an ad impression and a verified transaction. Unlike social media or general web advertising, where conversion might mean a lead form or a page view, retail media provides a direct window into the checkout basket. This level of transparency creates a higher standard of accountability. It also introduces complexities, such as the need to account for in-store versus online sales and the influence of loyalty programs on purchase behavior.
The Future of Retail Media Analytics
The future of the industry lies in the proliferation of AI agents that replace manual reporting entirely. These agents will not only gather data but will also provide conversational insights, allowing marketers to ask complex questions and receive immediate, data-backed answers. This shift is already underway: 42% of marketers report using AI in their retail media campaigns today.
As retail creatives continue to extend into high-impact areas like high-impact creative and cross-device environments, the analytics of tomorrow will be truly omnichannel, providing a 360-degree view of the customer that was previously impossible.
FAQS
What is the difference between ROAS and iROAS?
While ROAS measures total revenue relative to ad spend, iROAS (Incremental Return on Ad Spend) focuses specifically on sales that would not have occurred without the advertisement. This is crucial for understanding the true effectiveness of a campaign in driving new growth rather than just claiming credit for organic purchases.
How does retail media analytics account for offline sales?
Modern retail media bridges the gap between digital impressions and physical store visits. Using anonymized loyalty card data and geolocation signals, analytics platforms can track when a consumer is exposed to a Digital Out-of-Home (DOOH) or mobile ad and subsequently makes a purchase at a physical checkout. This “store lift” measurement is essential for CPG brands that sell the majority of their inventory in-store, as it provides a comprehensive view of how digital spend influences real-world consumer behavior and total omnichannel revenue.
Why is first-party data so important in retail media?
As third-party cookies are phased out, first-party data from retailers (such as purchase history and loyalty stats) enables highly accurate personalization and measurement, as it is based on actual consumer behavior within a closed-loop ecosystem.
