Retail Media with Data Streaming: The Future of Personalized Advertising in Commerce

Retail media is transforming advertising by leveraging first-party data to deliver highly targeted, real-time promotions across digital and physical channels. As traditional ad models decline, retailers are monetizing their data through retail media networks, creating additional revenue streams and improving customer engagement. However, success depends on real-time data streaming—enabling instant ad personalization, dynamic bidding, and seamless attribution. Data Streaming with Apache Kafka and Apache Flink provide the foundation for this shift, allowing retailers like Albertsons to optimize advertising strategies and drive measurable results. In this post, I explore how real-time streaming is shaping the future of retail media.

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What is Retail Media?

Retail media is transforming how brands advertise by leveraging first-party data from retailers to create highly targeted ads within their ecosystems. Instead of relying solely on third-party data from traditional digital advertising platforms, retail media allows companies to reach consumers at the point of purchase—whether online, in-store, or via mobile apps.

Retail media is one of the fastest-growing and most strategic revenue streams for retailers today. It has transformed from a niche digital advertising concept into a multi-billion-dollar industry, changing how retailers monetize their data and engage with brands. Below are the key reasons retail media is crucial for retailers.

Retailers like Amazon, Walmart, and Albertsons are leading the way in monetizing their digital real estate, offering brands access to sponsored product placements, banner ads, video ads, and personalized promotions based on shopping behavior. This shift has made retail media one of the fastest-growing sectors in digital advertising, expected to exceed $100 billion globally in the coming years.

The Digitalization of Retail Media

Retail media has grown from traditional in-store promotions to a fully digitized, data-driven advertising ecosystem. The rise of e-commerce, mobile apps, and connected devices has enabled retailers to:

• Collect granular consumer behavior data in real time
• Offer personalized promotions to drive higher conversion rates
• Provide advertisers with measurable ROI and closed-loop attribution
• Leverage AI and machine learning for dynamic ad targeting

By integrating digital advertising with real-time customer data and real-time inventory, retailers can provide contextually relevant promotions across multiple touchpoints. The key to success lies in seamlessly connecting online and offline shopping experiences—a challenge that data streaming with Apache Kafka and Flink helps solve.

Online, Brick-and-Mortar, and Hybrid Retail Media

Retail media strategies vary depending on whether a retailer operates online, in-store, or in a hybrid model:

• Online-Only Retail Media: Retail giants like Amazon and eBay leverage vast amounts of digital consumer data to offer programmatic ads, sponsored products, and personalized recommendations directly on their websites and apps.
• Brick-and-Mortar Retail Media: Traditional retailers like Target and Albertsons are integrating digital signage, in-store Wi-Fi promotions, and AI-powered shelf displays to engage customers while shopping in physical stores.
• Hybrid Retail Media: Retailers like Walmart and Kroger are bridging the gap between digital and physical shopping experiences with omnichannel marketing strategies, personalized mobile app promotions, and AI-powered customer insights that drive both online and in-store purchases.

Omnichannel vs. Unified Commerce in Retail Media

Retailers are moving beyond omnichannel marketing, where customer interactions happen across multiple channels, to unified commerce, where all customer data, inventory, and marketing campaigns are synchronized in real time.

• Omnichannel: Offers a seamless shopping experience across different platforms but often lacks real-time data integration.
• Unified Commerce: Uses real-time data streaming to unify customer behavior, inventory management, and personalized advertising for a more cohesive experience.

For example, a unified commerce strategy allows a retailer to:

• Track customer interactions in real time across e-commerce, mobile, and in-store purchases
• Deliver personalized product recommendations based on live shopping behavior
• Optimize ad placements and promotions based on real-time demand and inventory availability

This level of integration is only possible with real-time data streaming using technologies such as Apache Kafka and Apache Flink.

How Data Streaming with Apache Kafka and Flink Powers Retail Media

Retail media networks require real-time data processing at scale to manage millions of customer interactions across online and offline touchpoints. Kafka and Flink provide the foundation for a scalable, event-driven infrastructure that enables retailers to:

• Process customer behavior in real time: Tracking clicks, searches, and in-store activity instantly
• Deliver hyper-personalized ads and promotions: AI-driven dynamic ad targeting
• Optimize inventory and pricing: Aligning promotions with real-time stock levels
• Measure campaign performance instantly: Providing brands with real-time attribution and insights

With Apache Kafka as the backbone for data streaming and Apache Flink for real-time analytics, retailers can ingest, analyze, and act on consumer data within milliseconds.

Here are a few examples of input data sources, stream processing applications, and outputs for other systems:

Input Data Sources for Retail Media

1. Customer transaction data (e.g., point-of-sale purchases, online orders)
2. Website and app interactions (e.g., product views, searches, cart additions)
3. Loyalty program data (e.g., customer preferences, purchase frequency)
4. Third-party ad networks (e.g., campaign performance data, audience segments)
5. In-store sensor and IoT data (e.g., foot traffic, digital shelf interactions)

Stream Processing Applications for Retail Media

1. Real-time advertisement personalization engine (customizes promotions based on live behavior)
2. Dynamic pricing optimization (adjusts ad bids and discounts in real-time)
3. Customer segmentation & targeting (creates audience groups based on behavioral signals)
4. Fraud detection & clickstream analysis (identifies bot traffic and fraudulent ad clicks)
5. Omnichannel attribution modeling (correlates ads with online and offline purchases)

Output Systems for Retail Media

1. Retail media network platforms (e.g., sponsored product listings, display ads)
2. Programmatic ad exchanges (e.g., Google Ads, The Trade Desk, Amazon DSP)
3. CRM & marketing automation tools (e.g., Salesforce, Adobe Experience Cloud)
4. Business intelligence dashboards (e.g., Looker, Power BI, Tableau)
5. In-store digital signage & kiosks (personalized promotions for physical shoppers)

Key Retail Media Use Cases for Data Streaming with Kafka & Flink

Real-time data streaming with Kafka and Flink enables critical retail media use cases by processing vast amounts of data from customer interactions, inventory updates and advertising platforms. The ability to analyze and act on data instantly allows retailers to optimize ad placements, enhance personalization, and measure the effectiveness of marketing campaigns with unprecedented accuracy. Below are some of the most impactful retail media applications powered by event-driven architectures.

Personalized In-Store Promotions

Retailers can use real-time customer location data, combined with purchase history and preferences, to deliver highly personalized promotions through mobile apps or digital signage. By incorporating location-based services (LBS), the system detects when a shopper enters a specific section of a store and triggers a targeted discount or special offer. For example, a customer browsing the beverage aisle might receive a notification offering 10% off their favorite soda, increasing the likelihood of an impulse purchase.

Dynamic Ad Placement & Bidding

Kafka and Flink power real-time programmatic advertising, enabling retailers to dynamically adjust ad placements and bids based on customer activity and shopping trends. This allows advertisers to serve the most relevant ads at the optimal time, maximizing engagement and conversions. For instance, Walmart Connect continuously analyzes in-store and online behavior to adjust which ads appear on product pages or search results, ensuring brands reach the right shoppers at the right moment.

Inventory-Aware Ad Targeting

Real-time inventory tracking ensures that advertisers only bid on ads for products that are in stock and ready for fulfillment, reducing wasted ad spend and improving customer satisfaction. This integration between retail media networks and inventory systems prevents scenarios where customers click on an ad only to find the item unavailable. For example, if a popular TV model is running low in a specific store, the system can prioritize ads for a similar in-stock product, ensuring a seamless shopping experience.

Fraud Detection & Brand Safety

Retailers must protect their media platforms from click fraud, fake engagement, and suspicious transactions, which can distort performance metrics and drain marketing budgets.

Kafka and Flink enable real-time fraud detection by analyzing patterns in ad clicks, user behavior, and IP addresses to identify bots or fraudulent activity. For example, if an unusual spike in ad impressions originates from a single source, the system can immediately block the traffic, safeguarding advertisers’ investments.

Real-Time Attribution & Measurement

Retail media networks must provide advertisers with instant insights into ad performance by linking online interactions to in-store purchases.

Kafka enables event-driven attribution models, allowing brands to measure how digital ads drive real-world sales. For example, if a customer clicks on an ad for running shoes, visits a store, and buys them later, the platform tracks the conversion in real time, ensuring brands understand the full impact of their campaigns. Solutions like Segment (built on Kafka) provide robust customer data platforms (CDPs) that help retailers unify and analyze customer journeys.

Retail Media as an Advertising Channel for Third-Party Brands

Retailers are increasingly leveraging third-party data sources to bridge the gap between retail media networks and adjacent industries, such as quick-service restaurants (QSRs).

Kafka enables seamless data exchange between grocery stores, delivery apps, and restaurant chains, optimizing cross-industry advertising. For example, a burger chain could dynamically adjust digital menu promotions based on real-time data from a retail partner—if a grocery store’s sales data shows a surge in plant-based meat purchases, the restaurant could prioritize ads for its new vegan burger, ensuring more relevant and effective marketing.

Albertsons’ New Retail Media Strategy Leveraging Data Streaming

One of the most innovative retail media success stories comes from Albertsons. Albertsons is one of the largest supermarket chains in the United States, operating over 2,200 stores under various banners, including Safeway, Vons, and Jewel-Osco, and providing groceries, pharmacy services, and household essentials.

I explored Albertsons in another article about its revamped loyalty platform to retain customers for life. Data streaming is essential and a key strategic part of Albertsons’ enterprise architecture:

When I hosted a webinar with Albertsons around two years ago on their data streaming strategy, retail media was one of the bullet points. But I didn’t yet realize until now how crucial it would become for retailers:

• Retail Media Network Expansion: Albertsons has launched its own retail media network, leveraging first-party data to create highly targeted advertising campaigns.
• Real-Time Personalization: With real-time data streaming, Albertsons can provide personalized promotions based on customer purchase history, in-store behavior, and digital engagement.
• AI-Powered Insights: Albertsons uses AI and machine learning on top of streaming data pipelines to optimize ad placements, campaign effectiveness, and dynamic pricing strategies.
• Data Monetization: By offering data-driven advertising solutions, Albertsons is monetizing its shopper data while enhancing the customer experience with relevant, timely promotions.

Business Value of Real-Time Retail Media

Retailers that adopt data streaming with Kafka and Flink for retail media strategies to unlock massive business value:

• New Revenue Streams: Retail media monetization drives ad sales growth
• Higher Conversion Rates: Real-time targeting improves customer engagement
• Better Customer Insights: Streaming analytics enables deep behavioral insights
• Competitive Advantage: Retailers with real-time personalization outperform rivals
• Better Customer Experience: Retail media reduces friction and enhances the shopping journey through personalized promotions

The Future of Retail Media is Real-Time and Context-Specific Data Streaming

Retail media is no longer just about placing ads on retailer websites—it’s about delivering real-time, data-driven advertising experiences across every consumer touchpoint.

With Kafka and Flink powering real-time data streaming, retailers can:

• Unify online and offline shopping experiences
• Enhance personalization with AI-driven insights
• Maximize ad revenue with real-time campaign optimization

As retailers like Albertsons, Walmart, and Amazon continue to innovate, the future of retail media will be hyper-personalized, data-driven, and real-time.

How is your organization using real-time data for retail media? Stay ahead of the curve in retail innovation! Subscribe to my newsletter for insights into data streaming and connect with me on LinkedIn to continue the conversation. And download my free book about data streaming use cases and success stories in the retail industry.