The Role of a Composable Data Layer in Ad Targeting

This fundamental mismatch means that even with sophisticated tools, efforts to improve customer engagement and build detailed customer profiles fall short. True success requires a shift in how you approach data management from the ground up.

Key Highlights

Here are the key takeaways on building a composable data layer for ad targeting:

• Retail media often fails because its data layer is built for analytics, not for real-time ad activation.

• A retail data fabric is an operating model, not just a tool, that unifies data for activation across your entire tech stack.

• Traditional CDPs can create bottlenecks due to black-box logic and schemas not designed for advertising.

• Effective data management and identity resolution are foundational for creating trusted, high-value audience segments.

• Strong data governance is not optional; it is essential for building advertiser trust and monetizing first-party data.

• A composable architecture enables closed-loop measurement, linking ad exposure directly to purchase behavior to improve marketing campaigns.

Rethinking Retail Ad Targeting: Why Data Architecture Drives Success

Your approach to data architecture is the most critical factor in the success of your retail media and ad targeting efforts. Without a solid foundation, even the best marketing strategies will fail to scale. The ability to understand customer behavior and deliver a relevant customer experience depends entirely on how you collect, unify, and activate information from disparate data sources.

A successful architecture prioritizes activation, ensuring data is not just collected but is structured for immediate use in advertising and personalization. This requires a strategic approach to data management that moves beyond simple reporting. Let's explore the specific challenges that a modern data architecture solves.

From Analytics to Activation: The Real Retail Media Challenge

The core challenge for many retailers is that their data infrastructure was built to answer questions about the past, not to influence the present. These systems excel at generating reports and providing customer insights for analysis, but they are ill-equipped for the real-time demands of ad activation. This is the critical gap between an analytics-first and an activation-first mindset.

An activation-first model is designed to power marketing campaigns and personalized experiences on the fly. It turns customer data management into a dynamic process in which data flows to the systems that need it when they need it. For example, instead of just reporting that a user segment browsed a product category, an activation-ready system immediately pushes that segment to an ad platform for targeting.

This shift is essential for modern digital commerce. It allows you to move from passive analysis to active engagement, using data to trigger real-time interactions across web, mobile, and in-store channels. Use cases like retargeting abandoned carts or personalizing offers based on recent behavior become seamless.

Fragmented Data Sources and the Impact on Monetization

Retailers today collect a massive amount of data, but it is often trapped in silos. This fragmentation undermines your ability to build a complete customer view, directly undermining monetization efforts. When your data flows are disconnected, you cannot create the premium, high-trust audience segments that advertisers are willing to pay for.

Consider the typical data sources in a retail environment:

• Point-of-Sale (POS) systems

• E-commerce platforms and mobile apps

• Customer Relationship Management (CRM) tools

• Web analytics and loyalty programs

Each system holds a piece of the puzzle. Without a unified layer connecting them, you get an incomplete picture of customer behavior. This leads to inconsistent targeting, poor reporting credibility, and a loss of advertiser trust, ultimately limiting your revenue potential. Effective monetization is impossible when your foundational data is fragmented.

Event Tracking Versus Actionable Ad Data

Many organizations believe that implementing event tracking is equivalent to having usable advertising data. This is a critical misunderstanding. Basic event tracking, like the kind used for web analytics, captures user interactions but doesn't inherently structure them for ad activation. An "add to cart" event is just a data point until it's enriched and placed in the context of a specific user's journey.

Actionable ad data is different. It involves taking raw first-party data from event tracking and transforming it into attributes that define an audience segment. This means resolving retail shoppers' identities across devices, enriching their profiles with transactional history, and ensuring the data is formatted for the destination ad platform. It's the difference between knowing an event happened and knowing who performed the event and why it matters for targeting.

Without this transformation layer, your data analytics may look good, but your advertising capabilities remain weak. You end up with a high volume of customer data that you can't effectively use to target specific cohorts, rendering your retail media network less valuable to potential advertisers.

Defining a Retail Data Fabric for Modern Retail Media

So, what is the solution to these challenges? It's a retail data fabric. This is not another off-the-shelf tool but a composable data layer that unifies, governs, and activates first-party data from all your data sources. It serves as an operating model for your data, coordinating the flow of information across your systems.

This approach allows you to build rich customer profiles ready for activation. By focusing on flexible data integration and robust data management, a data fabric provides the architectural clarity and scalability needed to support a thriving retail media business.

What Makes a Composable Data Layer Unique?

A composable data layer, or composable data and analytics framework, is unique because it treats your data capabilities as a set of interchangeable building blocks, much like Legos. Instead of a single, monolithic platform, it uses a microservices-based architecture that lets you select and combine the specific components you need. This approach provides unparalleled flexibility.

Several key characteristics define this uniqueness:

• Modularity: The system is partitioned into managed components, allowing you to change or update one part without breaking the entire system.

• Autonomy: Each component is independent, maintaining data-flow integrity.

• Orchestration: It focuses on prescribing and negotiating interactions between components through an API-first approach.

For ad targeting, this means you can assemble a best-of-breed solution. You can use one service for identity resolution, another for consent management, and another to activate data into your Customer Relationship Management system. This composable data model empowers you to build a system that perfectly fits your needs, rather than forcing you to adapt to a rigid, pre-packaged solution.

Comparing Retail Data Fabric to Traditional CDP Architecture

A common point of confusion is the difference between a retail data fabric and a traditional Customer Data Platform (CDP). While both aim to unify customer data, their architectural philosophies are fundamentally different. A traditional CDP is often a single software system—a tool that centralizes data into its own data store.

A retail data fabric, in contrast, is an operating model. It coordinates data across your existing systems, including your data warehouse, rather than requiring you to move all data into a new platform. This makes it more flexible and less disruptive for a retail business to implement. The fabric acts as a connectivity layer, while the CDP often acts as a new central destination.

Here is a simple comparison of the two approaches:

This table clarifies why simply "buying a CDP" often fails to solve the deeper architectural problems in a retail business.

Common Misconceptions About Retail Data Fabrics

As with any architectural concept, several misconceptions have emerged around retail data fabrics. Clarifying these is crucial for marketing teams and engineers to align on strategy.

Here are a few common myths that need debunking:

• It is "another CDP." A data fabric is not a replacement for data management platforms but a way of organizing them. It's an architectural approach that can leverage a CDP as one of its components, but it is not the tool itself.

• It replaces your data warehouse. The opposite is true. A data fabric works with your existing data warehouse, treating it as a core component for storage and processing. It enhances your warehouse by making the data within it more accessible for activation.

• It is a massive, "big bang" project. A composable fabric can be adopted evolutionarily. You can start by unifying a few critical data flows and expand over time, minimizing risk and delivering value faster.

Understanding these points helps frame the data fabric as an enabler, not another system to manage.

Retail CDP Architecture vs Data Fabric: Key Differences

The distinction between a standard customer data platform architecture and a data fabric is more than semantic—it has significant implications for scalability, control, and cost. While a CDP is typically sold as a product, a data fabric should be thought of as a strategic design pattern for your entire data ecosystem.

This difference impacts everything from how you manage data sources to how you integrate with CRM systems. A fabric-based approach gives you more control and avoids the vendor lock-in that often comes with monolithic platforms, making it a superior choice for building long-term, scalable data infrastructure.

CDP as a Tool Versus Data Fabric as an Operating Model

A traditional CDP is fundamentally a software system. It is a product you buy to perform a specific set of functions: collect data, build profiles in its own data store, and send segments to other tools. While useful, this "tool" mindset often leads to it becoming another silo in your organization.

A data fabric, on the other hand, is an operating model for your data. It is an architectural philosophy focused on creating seamless data flows between the systems you already have. It doesn't seek to replace your data warehouse; it aims to make it more powerful by connecting it to your activation channels through a governed, API-first framework.

This distinction is critical. With a composable data fabric, your architecture dictates the tools you use, not the other way around. This gives you the freedom to choose best-in-class components for identity, consent, and activation, creating a more resilient and future-proof customer data platform architecture.

Identity Resolution and Data Activation Capabilities

Identity resolution is the process of merging fragmented data points into a single, unified view of each customer. While most CDPs offer this, a data fabric approach provides greater transparency and control over the process. Instead of relying on a vendor's black-box logic, your teams can define the rules for matching identifiers.

A data fabric enhances identity and activation in several ways:

• Controlled Logic: You can use deterministic matching (e.g., based on email addresses) or plug in your own advanced AI and machine learning models for probabilistic matching.

• Holistic Profiles: It unifies data from a broader range of sources—including seller data and in-store transactions—to create more complete customer profiles.

• Real-Time Activation: By leveraging an event-driven architecture, a fabric can trigger activation the moment a customer's behavior changes, enabling immediate and relevant customer engagement.

This level of control ensures you have an accurate, holistic view of all retail shoppers, the foundation for building the valuable audience segments that drive retail media revenue.

Why Standard CDPs Rarely Deliver in Retail Media

Despite their promises, standard, off-the-shelf CDPs often fail to meet the specific demands of retail media. The primary reason is a structural mismatch. These platforms were built as general-purpose marketing tools, not as the high-performance revenue infrastructure that retail press requires.

One major issue is that they can become bottlenecks. When all data management and segmentation logic is locked within a single platform that engineering teams cannot control or inspect, it slows down innovation. Marketing strategies that require new data integrations or custom segmentation rules get stuck in a queue waiting for vendor support or a new software release.

Furthermore, many CDPs are designed with a one-size-fits-all approach that doesn't cater to the complex customer needs of a large retail operation. They lack the flexibility to handle unique data types, such as seller inventory or complex household-level identity structures. This is why a more flexible, architectural approach is needed.

Why Traditional CDPs and GA4 Alternatives Struggle with Ad Targeting

Traditional data management platforms, including many CDPs and web analytics tools like GA4, were not designed with ad activation as their primary goal. Their core purpose is measurement and reporting. This foundational difference creates significant challenges for digital marketing teams trying to build sophisticated ad targeting campaigns.

These tools often rely on client-side tracking, have rigid reporting schemas, and offer limited control over the underlying logic. This makes them poor choices for the backbone of a retail media network, which demands reliable, transparent, and flexible customer data management.

Limitations of Client-Side Tracking and Reporting Schemas

Many data platforms rely heavily on client-side data tracking, where code runs in the user's browser to collect data. This approach has severe limitations for building a reliable ad targeting system.

Key limitations include:

• Ad Blockers: Client-side scripts are often blocked by ad blockers and privacy tools, leading to incomplete data.

• Performance Issues: Adding multiple third-party scripts to your website or mobile app can slow down the user experience.

• Lack of Control: Data processing occurs on the client's device, giving you less control over data quality and enrichment before it reaches your systems.

Moreover, the reporting schemas in these tools are designed for analytics, not activation. They may tell you how many users visited a page, but lack the rich attributes needed to build a segment of "high-intent users likely to purchase in the next 48 hours." This makes effective customer data management for ads nearly impossible.

Activation-First Data Versus Measurement-First Structures

The difference between activation-first and measurement-first data structures is critical. A measurement-first structure, typical of web analytics tools, is designed to collect data, process it in batches, and present it in dashboards for analysis. The primary goal is historical reporting on customer behavior.

In contrast, an activation-first structure is built for speed and actionability. Its primary purpose is to make data available to other systems for immediate use in marketing strategies. This means data is processed in real time, enriched with relevant attributes, and pushed to destinations like ad platforms, email tools, and personalization engines. Data activation is the priority.

For retail media, a measurement-first approach is simply too slow and inflexible. You cannot build a dynamic, high-yield advertising business on a data architecture that was designed for after-the-fact data analytics. You need a system that is engineered to act on data the moment it is created.

Control and Bottlenecks in Black-Box CDPs

Many traditional CDPs operate as "black boxes." Their internal logic for identity resolution, segmentation, and data processing is proprietary and hidden from your engineering teams. This lack of transparency and control is a primary source of frustration and a significant risk for your software development lifecycle.

When you can't inspect or modify the logic, you become entirely dependent on the vendor for any changes. Need to integrate a new data source or create a complex audience segment? You have to submit a request and wait. This creates a bottleneck that stifles innovation and prevents your teams from responding quickly to market opportunities.

For a robust customer data platform architecture, control is paramount. Your engineers need the ability to manage data flows, customize logic, and integrate with both new and legacy systems without being constrained by a vendor's rigid platform. A composable data fabric provides this control, whereas black-box CDPs often remove it.

Core Components of Advanced Retail Data Fabric Architecture

An advanced retail data fabric is built from several core architectural components working in concert. These layers handle everything from data collection and integration to governance and activation. Together, they form a cohesive system for turning raw customer data into a monetizable asset.

Understanding these components provides a clear mental model for what you need to build. The key layers include event collection, data integration, identity unification, and governance. This structure allows for the use of machine learning models and ensures robust data management across the enterprise.

Event Collection: Moving Beyond Basic Tracking

The foundation of any data fabric is its event collection layer. This goes far beyond basic pageview tracking. It involves implementing a robust event-driven architecture (EDA) to capture a rich stream of real-time data on customer behavior. An EDA is a software design pattern in which systems detect, process, and react to events as they occur.

In this model, every meaningful action—a product view, an "add to cart" click, an in-store purchase—is captured as an event. These events are published to a central stream, like Apache Kafka, where various systems can consume them. This approach is far more potent than traditional batch processing because it enables immediate reactions to customer behavior.

By moving to a server-side event tracking strategy, you gain more control over data quality and can capture a more complete customer view, unaffected by client-side blockers. This rich, real-time data stream is the fuel for your entire activation and personalization strategy.

Integrating Transactional, Seller, and Behavioral Data

A key strength of a data fabric is its ability to integrate data from different information types. To build a truly valuable retail media offering, you must unify more than just online behavioral data.

Three critical data types must be integrated:

• Transactional Data: This includes purchase history from both online and offline (POS) systems, providing a clear picture of what customers are actually buying.

• Behavioral Data: This covers all digital interactions, including clicks, searches, and content views, across your website and mobile app.

• Seller Data: For marketplaces, this includes information about third-party sellers, their inventory, and their performance.

By integrating these disparate sources, you create a rich, multi-dimensional view of your ecosystem. This enables advanced use cases, such as targeting ads for a specific seller's products to users who have previously purchased in that category.

Identity and Unification Across Systems

Once data is collected, the next critical layer is identity and unification. The goal of identity resolution is to link data points from various data sources to a single, persistent customer profile. A data fabric orchestrates this process across your entire tech stack, not just within a single data store.

This involves combining deterministic matching (e.g., linking records by a common identifier such as an email address or customer ID) with more advanced probabilistic methods. You can plug in your own AI and machine learning models to improve matching accuracy, giving you complete control over how customer profiles are constructed.

The result is a unified view of the customer that is consistent across all your systems. Whether a user is interacting with your mobile app, your website, or your customer service team, their profile is updated in real time. This unified profile is the cornerstone of effective personalization.

Consent, Governance, and Policy Management

A data fabric is incomplete without a robust layer for consent, governance, and policy management. This is not an afterthought; it is a core architectural component that ensures trustworthy and compliant customer data management. Advertiser trust is built on a foundation of well-governed data.

This layer is responsible for several critical functions:

• Centralized Consent Management: It captures and stores user consent preferences, ensuring you only use data for purposes the user has agreed to.

• Regulatory Compliance: It helps you comply with regulations such as GDPR and the California Consumer Privacy Act (CCPA) by providing audit trails and enforcing data access rules.

• Policy Enforcement: It allows you to define and enforce policies about who can access which data and for what purpose, mitigating security risks.

By embedding data governance and policy management directly into your architecture, you create a system that is compliant by design. This is one of the most essential best practices for building a sustainable and trustworthy retail media business.

Retail Data Governance: The Foundation for Trustworthy Ad Monetization

You cannot sell premium retail media on ungoverned data. Data governance is the missing layer in many retail media strategies, but it is the absolute foundation for building advertiser trust and achieving successful data monetization. It establishes clear rules for data ownership, consent, and quality, ensuring consistency across all data flows.

When advertisers know your data is accurate, compliant, and auditable, they are more willing to invest. This commitment to governance transforms your first-party data from a chaotic asset into a reliable, revenue-generating product that also improves customer satisfaction.

Defining Data Ownership Across Product, User, and Seller Contexts

A critical part of data governance is establishing clear data ownership. This means deciding which team is responsible for defining, ensuring quality, and maintaining different data domains. In a retail context, this typically breaks down into three key areas: product data, customer data, and seller data.

The product team might own the definitions for all product data, ensuring that attributes such as category, size, and color are consistent across the product. The marketing or CRM team might own the definitions for customer data, standardizing how user attributes are captured and stored. For marketplaces, a partner team might own the definitions for seller data.

This straightforward assignment of ownership is enforced through policy management within the data fabric. It eliminates ambiguity and ensures that when an advertiser targets a segment based on "women's running shoes," the definition of that segment is consistent and trustworthy across the entire organization.

Ensuring Consent and Regional Compliance in Data Activation

Data activation for advertising must be tied directly to consent and regional compliance. It is not enough to simply collect user consent; your architecture must enforce it at the point of activation. A data fabric with a strong policy management layer makes this possible.

This involves several key steps in computer vision applications transforming retail:

• Capturing Granular Consent: The system must record exactly what a user has consented to (e.g., personalization, advertising, analytics).

• Enforcing Consent at Activation: Before a user's data is sent to an ad platform, the system checks their consent status. If consent is not present for advertising, the data is not sent.

• Managing Regional Rules: The system must also be aware of regional compliance laws such as GDPR and the California Consumer Privacy Act (CCPA) and apply the appropriate rules based on the user's location.

This automated enforcement ensures that your data activation practices are always compliant, protecting your business from legal risk and building trust with your customers.

Enabling Consistent, Auditable Data Flows

Advertisers and auditors demand proof that your data is reliable. A governed data fabric ensures that all data flows are consistent and auditable. This means you can trace the journey of any data point from its source to its activation, with a clear record of any transformations or enrichment that occurred along the way.

This level of data tracking builds immense trust. When an advertiser questions the size or composition of an audience segment, you can provide a detailed audit trail showing exactly how it was built. This transparency is a powerful differentiator in the competitive retail media landscape.

Ultimately, consistent and auditable data flows lead to a better customer experience. When you can trust your data, you can deliver more relevant and personalized interactions, confident that you are acting on accurate information and respecting user preferences. Strong data governance is not just a technical requirement; it's a business imperative.

The Role of Integrations: Segment Alternatives and Warehouse-First Models

The integrations you choose are critical to the success of your data fabric. Instead of being locked into a single vendor's ecosystem, a composable approach allows you to select the best tools for each job. This includes considering Segment alternatives and adopting warehouse-first models for customer data management.

This strategy focuses on patterns, not specific brands. By prioritizing a flexible data integration strategy, you can build a scalable, future-proof analytics platform that seamlessly connects to your data warehouse and avoids costly vendor dependencies.

Event Collection Strategies: Client-Side vs Server-Side Approaches

When it comes to event tracking and data collection, you have two main architectural choices: client-side and server-side. Each has its own set of trade-offs.

• Client-Side: In this approach, the tracking code (such as a JavaScript snippet) runs directly in the user's browser. It's easy to implement, but it is susceptible to ad blockers and can impact site performance.

• Server-Side: Here, data is sent from your web server to your data store or other destinations. This approach is more reliable, secure, and gives you greater control over data quality.

For building a serious retail media business, a server-side approach is strongly recommended. First-party data collected this way is more accurate and complete, making it far more valuable for ad targeting. While it requires more engineering effort upfront, the long-term benefits in data quality and reliability are well worth the investment.

Reducing Vendor Dependency for Scalability

One of the most significant benefits of a composable data layer is the reduction of vendor dependency. Traditional data management platforms often lock you into their ecosystem, making it difficult and expensive to switch tools or integrate with systems outside their walled garden. This lack of flexibility hinders scalability.

A composable, API-first architecture puts you in control. You can choose the best identity resolution provider, consent management tool, and activation platform for your specific needs. If a better solution comes along, you can swap out a single component without having to rebuild your entire stack.

This modularity is essential for any retail business that wants to innovate quickly. It allows your engineering and product teams to focus on building differentiated experiences rather than spending time on complex, costly data migration projects. It provides the architectural freedom needed to scale your operations effectively.

Why Server-Side Pipelines Matter for Retail Ad Yield

The connection between server-side data pipelines and ad monetization is direct and consequential. Ad yield—the revenue you generate from your ad inventory—is heavily dependent on the quality of your audience targeting. Better data leads to better targeting, which allows you to charge higher prices for your ad space.

Server-side data flows provide a stream of high-fidelity, reliable data that is unaffected by browser-based privacy tools or ad blockers. This means you can build more accurate and comprehensive user profiles. You can confidently create valuable segments like "users who have purchased twice in the last 30 days" because your data is complete.

When you can prove to advertisers that your audience segments are built on this kind of high-quality data, they are willing to pay a premium. Investing in a robust server-side data pipeline is, therefore, a direct investment in your ad yield and the overall success of your retail media network.

Closed-Loop Measurement and Attribution in Retail Media

Advertisers don't just want to reach audiences; they want to see results. This is why closed-loop measurement and attribution are non-negotiable components of a successful retail media offering. It means connecting ad exposure directly to purchasing outcomes, proving the value of your platform.

A data fabric is uniquely positioned to enable this. By unifying customer insights from advertising platforms with transactional data from your own systems, you can close the loop between seeing an ad and making a purchase. This provides the credible, data-backed attribution that advertisers expect.

Linking Ad Exposure to Purchase Behavior

The ultimate goal of closed-loop measurement is to answer a simple question: did the person who saw the ad end up buying the product? A unified data fabric enables the connection of the data points needed to answer this question accurately.

The process works like this:

• An exposure event is logged when a user from your customer base sees an ad. This event includes a user identifier.

• A purchase event is logged when a user buys a product, either online or in-store. This event also includes a user identifier.

• The data fabric's identity resolution capabilities link these two events to the same user profile.

By analyzing these linked events over time, you can directly attribute a portion of sales to ad exposure. This moves beyond simple click-based attribution and provides a much more accurate measure of an ad campaign's impact on actual purchase behavior.

First-Party Data in On-Site to Off-Site Attribution

A robust capability unlocked by a data fabric is on-site-to-off-site attribution using your rich first-party data. This means connecting a user's behavior on your own properties (such as your website or app) to the ads they see on other platforms.

For example, a user might browse a specific product category on your website. Your customer data management system captures this intent signal. Later, that same user is shown a sponsored ad for a product in that category on a social media platform. If they click that ad and make a purchase, your data fabric can connect all three events: the initial on-site browse, the off-site ad exposure, and the final conversion.

This holistic view of the customer journey, which includes everything from previous purchases to customer service interactions, allows you to build much more sophisticated attribution models. You can finally move beyond last-click attribution and understand the actual influence of each touchpoint.

Creating Feedback Loops to Improve Ad Targeting

Measurement shouldn't end with a report. The most valuable outcome of closed-loop attribution is the creation of feedback loops that continuously improve your ad targeting. The insights you gain from measuring campaign performance should be fed back into your system to make your targeting smarter over time, similar to how matchmaking companies leverage AI algorithms to refine connections and outcomes in various sectors.

For example, your analysis might reveal that users who view at least three products in a category before seeing an ad are twice as likely to convert. This insight can be used to refine your audience segments. The definition of your "high-intent" audience can be updated to include this behavior, making future campaigns more effective.

This is where predictive modeling comes in. The performance data from your feedback loops can be used to train machine learning models that predict which users are most likely to convert. This transforms your ad targeting from a reactive process to a proactive one, constantly optimizing for better customer engagement and a superior customer experience.

Activation Use Cases Enabled by Composable Retail Data Fabric

A composable retail data fabric is not just an architectural concept; it is a powerful engine for enabling concrete activation use cases that drive revenue and improve the customer experience. The flexible nature of composable data, combined with rich customer insights, allows for sophisticated data management and predictive analytics.

These capabilities translate directly into tangible marketing and personalization activities. Let's explore a few specific examples of what this architecture makes possible, including the development of artificial intelligence solutions for your digital commerce platform.

Building High-Intent Audience Segments for Sponsored Listings

One of the most valuable use cases for a retail data fabric is creating high-intent audience segments for sponsored listings. Because the fabric provides a complete customer view, you can build segments based on real-time behaviors that signal strong purchase intent.

Examples of high-intent segments include:

• Users who abandoned their cart in the last 24 hours.

• Shoppers who have viewed a specific product more than three times in a single week.

• Customers who previously purchased from a category and are now browsing it again.

These precise audience segments, built from unified customer profiles, are extremely valuable to advertisers. They can be activated in real time to power marketing campaigns for sponsored listings, ensuring that ads are shown to the shoppers most likely to convert. This is a direct way to use first-party data to increase ad effectiveness.

Personalization Engines Leveraging Unified Data

A retail data fabric is the ideal foundation for a powerful personalization engine. By providing a single, unified data source updated in real time, you can deliver a consistent, highly relevant customer experience across all your channels.

This unified data, drawn from complete customer profiles, can power a wide range of personalization tactics. You can deliver tailored product recommendations based on a user's complete purchase and browsing history, not just their last session. You can customize website content to show different banners or offers to different user segments.

Because the data fabric orchestrates data flow to all your tools, the personalization is consistent everywhere. A customer receives the same relevant experience whether they are on your website, using your mobile app, or reading an email. This is how a data platform directly helps retailers improve and personalize the customer experience.

Seller-Funded Ads Targeting In-Market Users

For retail marketplaces, a key benefit of a composable data layer is the ability to enable sophisticated seller-funded ad campaigns. Your third-party sellers want to reach in-market users actively shopping for their products, and a data fabric provides the tools to do so effectively.

Imagine a seller who sells high-end running shoes. Using the data fabric, you can enable them to target a campaign to users who have recently searched for "marathon shoes," browsed competitor brands, or previously purchased running apparel. This level of precision is impossible without a unified view of user behavior.

This capability transforms your relationship with sellers. Instead of just providing a platform for them to list products, you become a vital digital marketing partner. This creates a powerful new revenue stream and gives your marketing teams a compelling offering for seller engagement. The flexibility of a composable layer is essential for building these custom, high-value advertising products, which are central to modern mobile app and web development.

Conclusion

In conclusion, a composable data layer serves as a transformative foundation for effective ad targeting in retail media. By addressing the limitations of traditional data architectures and enabling seamless integration of first-party data, it enhances monetization strategies and fosters advertiser trust.

With a focus on unified identity resolution, governance, and actionable insights, retailers can leverage this advanced framework to streamline their ad operations and achieve better targeting outcomes.

The transition from disparate data sources to a cohesive data fabric is essential, ensuring that every piece of information works towards maximizing revenue and improving user experiences. For those looking to deepen their understanding and application of this concept, consider reaching out to our experts for a free consultation.