First-Party Data in Retail Media: The Complete Targeting Guide (2026)

Last updated: April 8, 2026

First-party data is the foundation of modern retail media targeting, giving retailers a deterministic, privacy-compliant data asset that no walled garden or third-party intermediary can replicate. According to IAB Europe's 2025 Attitudes to Retail Media Report, 85% of buy-side stakeholders now cite access to retailer first-party data as the primary opportunity driving their retail media investment. With US retail media spending projected to reach $71.09 billion in 2026 (eMarketer, 2026) and global spend surpassing $166 billion in 2025 (Path to Purchase Institute), the retailers who own, govern, and activate their first-party data through purpose-built targeting infrastructure will capture the majority of this growth.

This guide covers everything retailers, marketplace operators, and ad tech decision-makers need to build a first-party data targeting strategy in 2026 -- from data types and audience segmentation models to clean rooms, privacy governance, measurement, and the platform infrastructure that makes it all executable.

What Is First-Party Data in Retail Media? (And Why It Now Defines the Industry)

First-party data in retail media is shopper data that a retailer collects directly through its own properties and customer relationships -- including purchase transactions, website browsing behavior, loyalty program activity, app interactions, and in-store point-of-sale signals. Unlike third-party data sourced from external aggregators or cookie-based tracking, first-party data is owned by the retailer, collected with explicit or implied consent, and reflects actual shopper intent.

This distinction matters because first-party data carries a structural advantage that no third-party signal can match: it is deterministic, not probabilistic. When a retailer knows that a specific customer purchased organic baby food three times in the last 30 days, that signal is fundamentally more reliable for targeting than a cookie-based inference that someone "appears to be a parent." As Joe Frick, VP of Business Development at G-Comm / Goodway Group, put it: "What was once a promising add-on to broader media strategies has become a core driver of marketing performance" (Path to Purchase Institute, 2025).

The scale of the opportunity is difficult to overstate. According to eMarketer, close to 90% of US retail media investment is concentrated with Amazon and Walmart (eMarketer, 2026), meaning every other retailer -- from mid-market Indian marketplaces to Southeast Asian grocery chains to European fashion platforms -- must build competitive first-party data capabilities if they want to capture their share of a market growing at 33% year-over-year in recent quarters, according to Skai's Q4 2025 Quarterly Trends Report (Skai, 2026).

The benefits of activating first-party data in retail media extend across the entire value chain. For retailers, it transforms existing shopper relationships into a monetizable media asset. For brands, it provides targeting precision that third-party alternatives cannot achieve -- 43% of B2C marketers worldwide cite improved targeting accuracy as the primary benefit of first-party data collection (eMarketer, 2025). For shoppers, it powers personalized ad experiences rooted in genuine purchase behavior rather than invasive cross-site tracking.

Retail media network (RMN) is the advertising business a retailer builds on top of its owned media inventory (website, app, email, in-store screens) and first-party data. An RMN lets brands pay to reach the retailer's shoppers using targeting signals the retailer owns.

The Post-Cookie Era: Why First-Party Data Is Retail Media's Structural Moat

The post-cookie era is not a future scenario -- it is the current operating reality for retail media. While Google reversed its plan to fully deprecate third-party cookies in Chrome in 2024, the shift away from cookie-based targeting has become irreversible for practical and regulatory reasons. According to eMarketer, 38% of US consumers now accept cookies less frequently than three years ago (eMarketer, 2025), and Safari and Firefox have blocked third-party cookies entirely for years. The regulatory trajectory -- GDPR enforcement in Europe, CCPA/CPRA in California, and emerging privacy legislation in India, Brazil, and Southeast Asia -- continues to erode the viability of any targeting strategy that depends on cross-site tracking.

Cookieless targeting refers to ad targeting methods that do not rely on third-party cookies for audience identification. In retail media, this includes first-party data targeting (using the retailer's own shopper data), contextual targeting (matching ads to page content), and privacy-safe identity solutions like clean rooms.

For retailers, this structural shift is not a threat but a competitive moat. Retailers already own the most valuable targeting signal in commerce: demonstrated purchase intent. A shopper who searches for "running shoes under $150" on a retailer's website has declared intent that no cookie-based lookalike model can replicate. As AdExchanger observed in April 2026, first-party data is structurally necessary for the emerging AI and agentic advertising era, where machine learning models require deterministic, high-quality training data to drive real-time ad decisions (AdExchanger, 2026).

The strategic implication is clear: retailers who invest in first-party data activation now will compound their advantage as third-party signals continue to degrade. The share of brands working with 4-6 retail media networks more than doubled from 10% to 24% between 2024 and 2025 (IAB Europe, 2025), indicating that brands are actively diversifying their retail media spend beyond Amazon and Walmart toward retailers with strong first-party data offerings.

This also creates a targeting effectiveness gap between retailers who activate their data and those who do not. European retail media spend reached EUR 13.7 billion in 2024 with 21.1% year-on-year growth (IAB Europe / ppc.land, 2025), and off-site investment by high-spending buyers jumped from 30% to 46% between 2024 and 2025 -- a clear signal that brands are looking for retailers who can extend first-party targeting beyond owned properties.

For a deeper dive into how the contextual and behavioral targeting approaches work in practice within a cookieless framework, see our guide to contextual vs behavioral targeting in retail media.

Types of First-Party Data in Retail Media (The Data Asset Taxonomy)

Not all first-party data is created equal. Retailers typically collect five distinct categories, each with unique targeting value.

Transactional Data

Transactional data is the backbone of retail media targeting: purchase history, order frequency, average basket size, category affinity, and product-level purchase patterns. This data powers the most reliable targeting signals because it reflects actual purchase behavior rather than inferred intent. A beauty retailer that knows a shopper buys SPF moisturizer every 45 days can serve a sunscreen brand's ad at precisely the right moment in the repurchase cycle.

Behavioral Data

Behavioral data captures how shoppers interact with the retailer's digital properties before they buy: browse paths, search queries, product detail page views, dwell time, add-to-cart events, and abandonment signals. These signals reveal in-session intent and enable real-time targeting. A shopper browsing three different laptop models in the electronics category is signaling active consideration -- an opportunity for a computing brand to serve a targeted ad before the session ends.

Loyalty Program Data

Loyalty data is structured customer data collected through a retailer's membership or rewards program -- including enrollment details, tier status, point accrual and redemption patterns, purchase frequency, and lifetime value metrics. Loyalty programs are the single most powerful first-party data collection mechanism because members actively opt in to data sharing in exchange for rewards. According to Novus Loyalty, 60% of retail consumers initiate purchases on digital platforms through loyalty ecosystems (Novus Loyalty, 2026). As the Novus research team noted: "Loyalty programmes provide a comprehensive view of the customer by combining online and offline behavior" (Novus Loyalty, 2026).

In-Store Data

In-store data extends first-party data collection to physical retail environments: foot traffic patterns, aisle dwell time, point-of-sale transaction records, digital kiosk interactions, and QR code scans. This data is increasingly critical as in-store retail media grows. Off-line first-party signals enable retailers to close the loop between digital ad exposure and physical purchase behavior. Platforms like Osmos Instore Ads use aisle-level targeting and digital screen placements to activate these signals in real time.

Zero-Party Data

Zero-party data is information shoppers deliberately share with the retailer: declared preferences, quiz responses, wishlists, product review content, and communication preferences. Unlike behavioral data (which is observed), zero-party data is volunteered. This makes it the highest-consent tier of first-party data -- particularly valuable in markets with strict data protection regulations.

How Retailers Collect and Activate First-Party Data (From Signal to Segment)

Collecting first-party data is necessary but not sufficient. The competitive advantage lies in activating that data -- converting raw signals into targetable audience segments that brands can buy against.

Data Collection Touchpoints

Retailers generate first-party data across every shopper interaction: e-commerce websites, mobile apps, loyalty program sign-ups, in-store point-of-sale systems, customer service interactions, email engagement, and digital kiosk usage. The most sophisticated retailers unify these touchpoints into a single customer view using customer data platforms (CDPs), enabling cross-channel targeting that connects a shopper's online browse session to their in-store purchase.

Data Activation Architecture

Data activation is the process of transforming raw first-party data into targetable audience segments and making those segments available for ad campaign targeting. A typical activation architecture includes four layers:

1. Data ingestion -- Collecting and normalizing shopper signals from all touchpoints into a unified data layer
2. Identity resolution -- Matching anonymous signals to known customer profiles across devices and channels
3. Segment creation -- Building audience segments based on purchase behavior, browsing patterns, loyalty status, and predictive models
4. Ad delivery -- Making segments available for targeting through the ad serving platform, whether for onsite, offsite, or in-store campaigns

For retailers building their own retail media network, the platform layer matters enormously. A full-stack retail media operating system like Adscape by Osmos handles the entire activation pipeline -- from first-party data ingestion to segment-based ad delivery across product ads, display, video, and in-store formats -- without requiring retailers to stitch together multiple point solutions.

Data Enrichment

Raw first-party data can be strengthened by appending supplementary signals: demographic overlays, geographic data, weather-based triggers, and third-party audience enrichment where consent permits. The key principle is that enrichment supplements the first-party foundation rather than replacing it. Marc Fanelli, SVP of Digital Audiences and Operations at Dun & Bradstreet, emphasized this: "Differentiation will not come from claiming strong first-party data but from effectively addressing the gaps in that data" (eMarketer, 2026). For a detailed implementation framework, see our guide to how retailers enrich first-party data for smarter ad targeting.

First-Party Data Blind Spots

It is important to acknowledge that even robust first-party datasets have inherent constraints. As eMarketer noted, first-party data "reflect only shoppers who have engaged" -- meaning irregular buyers, emerging customer segments, and in-market prospects who have not yet transacted are invisible to first-party targeting (eMarketer, 2026). This is where data enrichment strategies and clean room collaborations become essential for closing the gaps.

Audience Segmentation Models: From Broad to Hyper-Targeted

The quality of first-party data targeting depends on the segmentation models retailers use to organize shoppers into addressable audiences. Four models dominate retail media in 2026.

RFM Segmentation (Recency, Frequency, Monetary)

RFM segmentation is a method of categorizing customers by how recently they purchased (Recency), how often they purchase (Frequency), and how much they spend (Monetary value). RFM remains the foundational segmentation model for retail media because it directly reflects purchase behavior. A "high-value loyalist" (recent purchase, high frequency, high spend) warrants different ad treatment than a "lapsed high-spender" (low recency, historically high frequency and spend). As the Kevel RFM Guide advises: "Don't sacrifice granularity, but don't make segments you're not going to use, either" (Kevel, 2026). For a deeper dive into RFM mechanics and behavioral targeting implementation, see our guide to first-party data targeting in retail media.

Behavioral and Interest Segmentation

Behavioral segmentation groups shoppers by their observed actions: category browsing patterns, search queries, content engagement, and add-to-cart behavior. This model captures real-time intent signals that transactional data misses. A shopper who has browsed protein powder three times this week but not purchased is a high-intent prospect for sports nutrition brands -- even if their purchase history shows no prior category activity.

Lifecycle Segmentation

Lifecycle segmentation maps customers to their stage in the relationship with the retailer: new acquirers, active repeaters, at-risk churn candidates, lapsed customers eligible for win-back campaigns, and high-value advocates. This model is particularly valuable for loyalty program integration, where tier status and engagement recency provide natural lifecycle markers. Retailers integrating loyalty data into their retail media platforms can offer brands the ability to target specific lifecycle stages, such as serving a "welcome offer" ad to new loyalty members or a "reactivation" promotion to lapsed members who have not purchased in 90+ days.

Lookalike and Predictive Modeling

Lookalike modeling extends first-party audiences by identifying net-new shoppers who share behavioral and demographic attributes with a retailer's existing high-value customers. Machine learning models trained on first-party purchase data can score prospective audiences for purchase propensity, category affinity, or brand switching likelihood. According to Skai's 2026 State of Retail Media report, 63% of marketers are now using generative AI in retail media (Skai, 2026), with predictive targeting and lookalike expansion emerging as key use cases beyond creative generation. For more on how AI and ML drive real-time segment updates and personalization, see our guide to AI-driven personalization in retail media.

Platforms like Osmos Adscape use ML-powered product ads to automate segment-to-campaign matching -- activating first-party purchase signals for 1-click campaign setup without requiring manual audience configuration.

Clean Rooms: How Retailers Enable Privacy-Safe Data Collaboration

Data clean rooms are secure, privacy-compliant environments where two or more parties (typically a retailer and a brand) can match and analyze overlapping audience data without either party exposing raw customer records to the other. Clean rooms enable advertisers to measure campaign performance, build matched audiences, and validate targeting effectiveness while maintaining data sovereignty.

Clean Room Adoption and Use Cases

According to eMarketer, 66% of organizations now use clean rooms in some capacity, but only 48% of US retail media networks currently offer clean room capabilities (eMarketer, 2026) -- indicating significant differentiation potential for retailers who implement them. The primary use cases in retail media include:

• Audience matching -- Brands upload their CRM data into the clean room; the retailer matches it against their shopper base to build targetable segments of known brand customers
• Campaign measurement -- Closed-loop attribution connecting ad exposure to purchase, without sharing individual-level data
• Overlap analysis -- Understanding the intersection of brand and retailer audiences to identify net-new reach opportunities
• Incrementality validation -- Comparing exposed and unexposed matched groups to measure true campaign lift

The CDP-Clean Room Ecosystem

Clean rooms do not operate in isolation. Over 80% of clean room users also utilize CDPs (Customer Data Platforms) and DMPs (Data Management Platforms) as complementary technologies (eMarketer, 2026). The typical data stack includes a CDP for first-party data unification, a clean room for privacy-safe collaboration, and an ad server for segment activation. Major clean room providers include AWS Clean Rooms, Google Ads Data Hub, LiveRamp Safe Haven, and Snowflake Data Clean Rooms.

Clean Room Challenges

Adoption is not without friction. According to eMarketer, 39% of organizations struggle to extract actionable insights from clean room data, and 48% of marketers cite budget constraints as barriers to adoption (eMarketer, 2026). For mid-market retailers, the cost and complexity of standalone clean room implementations can be prohibitive -- which is why integrated retail media platforms that include clean room-adjacent privacy controls within the targeting workflow are gaining traction.

DTC Brand and Retailer Data Partnerships

Clean rooms are particularly valuable for DTC (direct-to-consumer) brand and retailer first-party data partnerships. DTC brands often have rich CRM data but limited retail distribution insight. Retailers have purchase behavior data but limited brand-level customer knowledge. As Decentriq noted in their analysis of retail data partnerships: "Retailers and brands achieve greater success when they collaborate than when they keep insights locked away" (Decentriq, 2025). The clean room model enables this collaboration without either party surrendering their data asset -- and the brands building these partnerships early are establishing a structural advantage. As Joe Frick noted, "The brands that will win won't be those chasing every new format or algorithm tweak. They'll be forging deeper partnerships with retailers, with data partners and across their own organizations" (Path to Purchase Institute, 2025).

Targeting Methods in Retail Media: From Contextual to Behavioral to Predictive

Retail media targeting operates as a layered stack where multiple methods combine for precision. Here is a brief overview of the primary approaches.

Contextual Targeting

Contextual targeting matches ads to the content of the page a shopper is currently viewing -- serving a breakfast cereal ad on a grocery category page, or a running shoe ad on an athletic footwear product detail page. This method requires no personal data and is inherently privacy-safe, making it the most cookieless-ready targeting approach. For a detailed comparison of when to use contextual versus behavioral approaches, see our guide to contextual vs behavioral targeting in retail media.

Behavioral Targeting

Behavioral targeting uses cross-session shopper data -- purchase history, browsing patterns, search queries -- to build audience profiles and target ads based on demonstrated behavior. This is where first-party data delivers its highest-precision targeting advantage. Rather than relying on probabilistic third-party segments, behavioral targeting on first-party data uses deterministic signals from the retailer's own ecosystem. For implementation mechanics and the distinction between keyword-driven and AI-powered approaches, see our guide to AI-powered ad serving and behavioral targeting.

Predictive and ML-Powered Targeting

Predictive targeting uses machine learning models trained on first-party data to forecast shopper behavior: purchase propensity, brand switching likelihood, category exploration probability, and lifetime value trajectory. These models operate in real time, continuously updating audience scores as new behavioral signals arrive. According to AdTech Today, "The biggest driver of India's retail media surge is the convergence of first-party shopping signals with full-funnel advertising capabilities," as noted by Priyanka Khaneja Gandhi of Amazon Ads India (AdTech Today, 2026).

Real-Time Bidding and Budget Allocation

In programmatic retail media environments, first-party data powers real-time bidding decisions. The ad server evaluates each impression opportunity against available audience segments, brand budgets, and campaign objectives to determine which ad to serve. Budget allocation algorithms optimize spend distribution across audience segments, ad formats, and placements based on first-party performance signals -- adjusting bids in real time to maximize return on ad spend.

Cross-Channel Targeting Continuity

The most advanced retail media networks extend first-party targeting across onsite (website and app), offsite (social, search, display via Meta, Google Shopping, DV360), and in-store (digital screens, aisle targeting, POS) environments. This cross-channel continuity is where integrated platforms create the most value. Intent-driven display ads use first-party browse signals for geo-targeted campaigns, while offsite retail media activation extends retailer audiences to Meta, Google Shopping, and DV360 for multi-channel reach beyond owned inventory.

Privacy, Consent, and Data Governance in First-Party Targeting

First-party data carries inherent privacy advantages over third-party alternatives, but retailers must still implement robust governance frameworks to maintain compliance and shopper trust.

Governance Principles

The foundation of first-party data governance is consent clarity: shoppers must understand what data is being collected, how it will be used for advertising, and how they can opt out. GDPR in Europe requires explicit consent for data processing, while CCPA/CPRA in California mandates opt-out rights and data deletion capabilities. Emerging regulations in India (DPDPA), Brazil (LGPD), and Southeast Asian markets are converging toward similar consent-based frameworks.

Data governance in retail media is the set of policies, processes, and technical controls that determine how a retailer collects, stores, activates, and shares shopper data for advertising purposes -- including consent management, data retention rules, access controls, and audit trails.

Data Ownership Rights

A critical distinction in retail media: the retailer owns the first-party data, and brands access it through the platform. Brands do not get raw data exports -- they get access to targetable audience segments within the retailer's ad platform. This architecture inherently protects shopper privacy while giving brands the targeting precision they need. According to Suzanna Stevens, Brand Sales Director at Adstra: "The days of relying on intermediaries are waning as CPGs opt for in-house identity building to reclaim control" (Digiday / Nielsen, 2024). This trend reinforces the value of retailers owning and controlling their data layer rather than outsourcing it to third-party identity vendors.

Building Shopper Trust

Transparency is essential for maintaining shopper consent rates and long-term data quality. Retailers who clearly communicate their data practices, provide easy opt-out mechanisms, and demonstrate that ads improve the shopping experience (rather than feeling intrusive) maintain higher consent rates and richer data assets over time. For a deep dive into consumer trust strategies, see our guide to building shopper trust through transparent ad targeting. For the broader framework on navigating the privacy-personalization tradeoff, see our guide to privacy and personalization in retail media.

How Osmos Adscape Powers First-Party Data Targeting Infrastructure

For retailers looking to activate their first-party data without the complexity and cost of enterprise ad tech stacks, the platform choice is decisive. Adscape by Osmos is a full-stack retail media operating system that handles first-party data activation across every ad format -- onsite, offsite, and in-store -- under a single platform.

What Adscape Delivers

Adscape provides the entire targeting infrastructure retailers need:

• Full ad format stack -- Product ads, display ads, video ads, carousel ads, story ads, email ads, gamified ads, and in-store digital screen ads, all activated on the retailer's own first-party data
• ML-powered targeting -- Machine learning models that match campaigns to audience segments using purchase signals, browse behavior, and contextual relevance without third-party cookie dependency
• In-store + digital continuity -- Adscape integrates with Osmos Instore Ads for aisle-level targeting, digital screen placements, QR tracking, and offline analytics, extending first-party data from digital to physical
• Offsite activation -- Retailer first-party audiences extended to Meta, Google Shopping, and DV360 through offsite retail media activation
• Rapid deployment -- Turnkey retail media launch in as few as 4 weeks, or API Hub integration (ad server API, campaigns API, events API, reporting API) for custom implementations

Client Results

Osmos Adscape has driven measurable results for retailers across markets: 200% revenue growth in 2 months for Apollo 24x7 through PLA optimization, 112% increase in ad revenue in 2 months for Konvy Thailand (beauty retail), 100% QoQ ad revenue growth for India's leading online pharmacies, and in-store retail media scaled across 1,300+ stores for Southeast Asia's largest multi-brand retail group.

How Adscape Compares to Alternatives

The following comparison highlights how Adscape's full-stack approach differs from single-capability competitors on the dimensions that matter most for first-party data targeting:

Each platform has genuine strengths. Amazon DSP offers unmatched intent signal volume at 9.7 billion monthly shopping queries (ATTN Agency, 2026). Walmart Connect delivers unique omnichannel data bridging online ads to in-store purchases across 10,500+ stores. Criteo brings strong contextual targeting capabilities with commerce signal fusion. And Skai provides powerful cross-network campaign management.

But for retailers building their own media network -- rather than advertising within someone else's -- Adscape is the platform that gives them Amazon-equivalent targeting infrastructure on their own first-party data, without ceding control to a walled garden or paying identity graph fees.

For full product details, visit Adscape by Osmos. For an in-depth exploration of the full product suite, see our guide to Osmos Adscape.

Measuring First-Party Data Targeting Performance

First-party data targeting is only as valuable as the retailer's ability to measure its impact. Four metrics anchor retail media measurement in 2026.

ROAS (Return on Ad Spend)

ROAS remains the most in-demand retail media metric, cited by 88% of buyers (IAB Europe, 2025). First-party data targeting typically delivers higher ROAS than non-targeted or third-party-targeted campaigns because of the precision of deterministic audience signals. According to ATTN Agency's 2026 comparison, Amazon DSP ROAS benchmarks range from 3.6x (Home & Garden) to 5.1x (Electronics), while Walmart Connect delivers 3.4x (Electronics) to 4.1x (Home & Garden) (ATTN Agency, 2026).

However, ROAS alone is increasingly insufficient. Only 6% of advertisers fully trust retailers' self-reported media metrics (Bain & Company research, cited by Dataslayer, 2025), which is why incrementality testing has risen to prominence.

Incrementality Testing

Incrementality testing measures the true causal effect of an ad campaign by comparing the behavior of an exposed group against a matched holdout group that was not exposed. This isolates the incremental lift attributable to the ad spend from sales that would have occurred organically.

According to an Association of National Advertisers survey reported by Dataslayer, 71% of advertisers now rank incrementality as their top retail media KPI (Dataslayer / ANA, 2025). Incremental ROAS (iROAS) performance has varied dramatically across advertisers, ranging from 253% to 1,609% (Dataslayer, 2025). Over 52% of US brand and agency marketers already employ incrementality testing, with 36.2% planning to increase investment further (eMarketer, 2025).

Cross-Channel Attribution

First-party data enables retailers to close the attribution loop across channels in ways that third-party data cannot. When a retailer owns the data from onsite ad exposure, offsite media delivery (via Meta or Google Shopping), and in-store purchase confirmation, it can connect the full journey from ad impression to transaction. This closed-loop attribution is one of retail media's strongest value propositions for brands and a key reason 63% of buy-side stakeholders are now in retailer partnerships lasting 1 year or more (IAB Europe, 2025). For a comprehensive framework on ad serving, targeting, and attribution working together, see our guide to ad serving, targeting, and attribution in retail media.

Media Mix Modeling (MMM)

Media Mix Modeling is regaining prominence as a privacy-safe measurement methodology for retail media. According to eMarketer, 46.9% of US marketers plan to increase MMM investment, and 27.6% of brand and agency marketers identified MMM as the most reliable measurement methodology (eMarketer, 2025). MMM models aggregate-level performance data to quantify the contribution of each media channel -- including retail media -- to overall sales. The 50% increase in RMNs offering Media Mix Modeling access between Q1 and Q3 2024 (Dataslayer, 2025) signals that retailers are recognizing MMM as a necessary capability for attracting brand investment.

Budget Allocation and Optimization

First-party data enables smarter budget allocation across audience segments, ad formats, and placements. Retailers with integrated analytics can identify which segments deliver the highest marginal return on incremental spend and shift budgets accordingly -- a capability that is increasingly automated through ML-driven optimization engines. According to Skai's 2026 State of Retail Media report, 85% of CPG brands now allocate spend across four or more retail media networks (Skai, 2026), making cross-network budget optimization a critical capability for both brands and the platforms serving them.

The Global Landscape: First-Party Data Targeting by Region

First-party data targeting maturity varies significantly by market, and the opportunities differ for retailers in each region.

United States

The US remains the largest retail media market ($71.09 billion projected for 2026) but is heavily concentrated: approximately 90% of investment flows to Amazon and Walmart (eMarketer, 2026). For mid-market US retailers (grocery, pharmacy, specialty), the opportunity is to build first-party data capabilities that offer brands a differentiated alternative to the Amazon/Walmart duopoly.

India

India is experiencing a retail media inflection point. According to a YouGov Consumer Study cited by AdTech Today, 88% of Indian consumers prefer retail media to discover new brands or products due to ease of discovery and trust (AdTech Today / YouGov, 2026). As Priyanka Khaneja Gandhi of Amazon Ads India observed, "The biggest driver of India's retail media surge is the convergence of first-party shopping signals with full-funnel advertising capabilities" (AdTech Today, 2026). Osmos has driven results for major Indian retailers including Apollo 24x7 (200% revenue growth) and BigBasket (8% revenue growth through keyword expansion and personalized recommendations).

Europe

European retail media spending reached EUR 13.7 billion in 2024 with 21.1% growth, with network fragmentation (51%) and lack of standardization (53%) remaining the most significant barriers (IAB Europe, 2025). Retail media is projected to contribute 20% of total UK ad spend by 2027 (Novus Loyalty, 2026). European retailers face additional complexity due to GDPR's stringent consent requirements, making first-party data strategies -- which are inherently consent-based -- particularly well-suited to this market.

Southeast Asia and Australia

Southeast Asia represents one of the fastest-growing retail media markets globally. Osmos has already scaled in-store retail media across 1,300+ stores for Southeast Asia's largest multi-brand retail group and delivered 112% ad revenue growth for Konvy Thailand. In these markets, third-party cookie alternatives are particularly underdeveloped, giving first-party data strategies an even greater competitive advantage.

FAQ

What Are the Biggest Challenges of First-Party Data in Retail Media?

The biggest challenges include data accuracy (a 2024 Nielsen study found 41% of marketers cite accuracy as problematic in their first-party data programs), first-party data blind spots where only engaged shoppers are captured, network fragmentation making cross-network standardization difficult, and the organizational capability gap where teams need data literacy, ML expertise, and AI-led decisioning skills. Clean room adoption also presents challenges, with 39% of organizations struggling to extract actionable insights (eMarketer, 2026).

Who Owns the First-Party Data in a Retail Media Network?

The retailer owns the first-party data. Brands and advertisers access targetable audience segments through the retailer's ad platform but do not receive raw data exports. This ownership model protects shopper privacy while enabling brands to benefit from the targeting precision of first-party signals. The retailer controls data governance policies, consent management, retention rules, and access permissions.

How Do Retailers Balance Personalization and Privacy in Retail Media?

Retailers balance personalization and privacy by collecting data with clear consent, using privacy-safe targeting methods (contextual targeting, aggregated segments rather than individual-level tracking), offering transparent opt-out mechanisms, and implementing data governance frameworks that comply with GDPR, CCPA/CPRA, and emerging regional regulations. First-party data is inherently more privacy-safe than third-party alternatives because it is collected within the retailer's own ecosystem with the shopper's knowledge. For a full framework, see our guide to privacy and personalization in retail media.

How Does Walmart Connect First-Party Data Compare to Amazon DSP?

Amazon DSP reaches 310 million+ monthly active users with 18+ years of purchase history on 300 million+ customers and processes 9.7 billion shopping queries monthly. Walmart Connect reaches 240 million+ customers weekly across 10,500+ stores, with 90% of Americans living within 10 miles of a Walmart. Amazon's strength is intent signal volume and online purchase depth; Walmart's strength is omnichannel offline-to-online measurement and mass-market reach. Amazon DSP ROAS benchmarks by category: Beauty 4.2x, Electronics 5.1x, Home & Garden 3.6x. Walmart Connect ROAS: Beauty 3.8x, Home & Garden 4.1x, Electronics 3.4x (ATTN Agency, 2026). Target Roundel operates as a smaller but growing third platform, leveraging Target's loyalty program (Target Circle) and in-store data. For retailers outside these walled gardens, platforms like Adscape by Osmos provide equivalent first-party targeting infrastructure on the retailer's own data.

What Skills Does a Retailer Need to Activate First-Party Data for Retail Media?

Retailers need a combination of data engineering (to build ingestion and identity resolution pipelines), data science (for segmentation models and predictive targeting), ad operations (for campaign management and yield optimization), and privacy/compliance expertise (for consent management and regulatory adherence). Increasingly, AI and ML literacy is required as targeting models become more automated. Platforms like Osmos Adscape reduce the organizational capability requirement by providing turnkey targeting infrastructure that handles the technical complexity -- enabling retailers to launch with smaller teams and scale capability over time.

What Are the Common Pricing Models for First-Party Data Targeting in Retail Media?

The most common pricing models are CPM (cost per thousand impressions) for display and video formats, CPC (cost per click) for sponsored product and search ads, and CPA (cost per acquisition) or percentage-of-sale models for performance-based campaigns. Some platforms offer hybrid models combining a base CPM with performance bonuses. Enterprise platforms like Criteo and Amazon DSP typically operate on CPM/CPC self-serve or managed-service models. Mid-market platforms like Osmos Adscape offer white-label SaaS pricing without identity graph fees, making them accessible to retailers who do not have enterprise ad tech budgets.

How Can Retailers Integrate Loyalty Programs with Retail Media Platforms?

Loyalty programs integrate with retail media platforms through data feeds that pass member tier status, purchase frequency, points balance, and redemption patterns into the ad targeting layer. This enables brands to target specific loyalty segments -- such as new members, top-tier loyalists, or lapsed members -- with tailored messaging and offers. As Novus Loyalty's research observed: "Retail media without data is just advertising. Retail media with loyalty data becomes precision marketing" (Novus Loyalty, 2026). The most effective integrations make loyalty data available as audience segments that brands can select during campaign setup, creating a flywheel where better targeting drives higher conversion, which drives more loyalty enrollment, which generates richer data.