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Decoding the future of Retail Media Networks

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The ever-evolving retail landscape amid the upsurge of transformative technologies such as cloud computing, Generative AI, Big Data, and the Internet of Things (IoT) has spurred enterprises to reimagine their marketing and advertising tactics. Moreover, shifting channel preferences as consumers move online has led to an explosion in retail media networks (RMNs). This presents an exciting opportunity for retailers and brands eager to expand their reach and drive sales.

RMNs allow brands to promote their products and services through purchased ad spaces owned by them in closed data loops. Since coming to the forefront in the last few years, RMNs have exploded, with the successes of Amazon, Walmart, and Target (Roundel) being touted. Today, as we count dozens of new retailers embarking on this almost every month, the provider space is probably helpful to review some operating principles of what retailers can focus on to build and control closely.

The big RMN explosion: An exciting opportunity

Today, Retail Media Networks (RMNs) have emerged as the fastest-growing spending area in advertising, exhibiting an accelerated growth of $55 billion in expenditures by 2024, eventually expanding to $106.12 billion by 2027.

It’s anticipated that a noteworthy 1 in 8 dollars of ad spending will be directed towards RMNs this year, mirroring the proportion of digital media spending compared to traditional media spending in 2016.


Unpacking the causes: Converging pressures

The surge of Retail Media Networks (RMNs) can be attributed to the recent disruptions in the advertising industry, where ‘downward pressures’ have become a defining force steering the trajectory of marketing strategies. These include:

  • Cookie depreciation across mainstream browsers: The gradual phasing out and doubling down on third-party cookies make it difficult for advertisers to track users.
  • Platform changes limiting mobile tracking: Most devices today have specialized settings and apps to prevent advertisers from tracking users through interfaces and apps.
  • Regulatory changes limiting tracking options: New privacy laws and guidelines could dramatically alter how advertisers and tech companies serve ads to their target consumer base.

Mounting downward pressures like the above have given rise to increased acquisition costs and limited targeting options. Adding to the complexities, Google is cutting down on its ‘free’ real estate, thus reducing organic content visibility for brands. Therefore, in an ecosystem where sponsored content gets priority over screen real estate, marketers must incorporate paid tactics into every organic strategy to thrive. Moreover, major social media channels have also reached saturation due to an influx of top competitor brands. What does this indicate for RMNs that promise high-margin revenue for low cost?

Thanks to its closed-loop system, retail media can be the way forward for businesses, making tracking and attribution measurement easier and resulting in effective brand messaging. Besides, advertising on e-commerce platforms is a high-margin, low-risk, and low-cost revenue option compared to other digital channels.

The missing pieces of the RMN puzzle

Organizations today are in favor of the commodification of AdTech platforms. However, to sustain an ascending position in expanding RMN prospects, it is essential to identify and resolve unmet capability gaps such as:

  • Integrating AI and data services engines can be a significant advantage for RMN ad partners like CPGs. Enterprises are searching for enhanced AI-driven data collection and aggregation capabilities to help improve audience lifecycle management – where the advertiser can directly take charge of personalization. Other areas include but are not limited to dynamic omni-channel journey, product cross-selling/up-selling, and the following best action recommendations.
  • Reporting analytics can help marketers experiment with platforms offering superior analytical capabilities to help measure various customer parameters or attributes, create data rooms for better audience development, optimize pricing and trade, and generate higher ROAS.


A well-planned Retail Media Network (RMN) with these capabilities can help end users derive contextual insights by aggregating in-store, online, and trading data—enabling them to improve operations, optimize costs, augment retail journeys, and elevate ROAS.


In today’s market, GenAI is reshaping frictionless and high-value customer experiences. When incorporated with RMN, GenAI can help value chain participants – firms, advertising leads, spend managers, media operations providers, and ad-tech providers to nurture a seamless retail ecosystem.

  • Improved searchability and discoverability: GenAI can help advertisers automate processes like meta-tagging and semantic search, thereby enhancing search results with relevant information like product descriptions, context-based search query analysis, and videos. Advertisers can also use GenAI to categorize products based on size, color, or features and optimize content according to keywords and phrases that best align with their products and services.
  • Elevated efficiency and ROI: GenAI can provide agencies with many advantages, from productivity improvements to transformative initiatives. Leveraging GenAI, strategists can analyze extensive data from diverse sources, crafting intricate customer profiles and building predictive models to forecast future consumer trends and behaviors. Asset managers can utilize GenAI to optimize trade executions through automated reporting based on outcomes and risk while cutting overall input costs.
  • Dynamized user journeys: GenAI can empower retailers to address customers’ aspirations and pain points. For instance, democratized media buy-ins fuelled by GenAI capabilities can help sellers create captivating product listings based on large-language-based (LLM) models that use enriched enterprise data. Inventory managers can utilize GenAI for enhanced data analysis by screening sales, customer search, and purchase history to optimize brokerage and stockpiling for peak seasons to prevent stockouts.

Future-proofing retail – The final block in the last mile

Futuristic retail leaders need enhanced data-driven decision-making to maintain the transformative momentum sparked by the intersection of retail marketing and cutting-edge technologies. But with more and more non-pandemic brands joining the RMN circuit, there is a need for a purpose-driven roadmap complemented by design thinking, business goal alignment, and privacy-compliant approaches. For instance, our AI-powered platform, MAGE, has an Ad-Recommender to make campaign journey planning data-driven.


HTCNXT’s plug-and-play platform, MAGE, interoperates with existing RMN systems without displacing them and empowers retailers with 360° visibility of their audience lifecycle management, revenue, pricing cycles, and ROAS. Our AI-based attribution service performs 16% better than attribution algorithms that are currently in use. The Insights Serving layer provides batch and streaming APIs and enhanced visualizations, helping brands gain more control of their RMN data securely and anonymously. Additionally, our Data Clean rooms empower CPGs and retailers to develop more refined audiences, amplifying the impact of their marketing efforts.

Besides, such solution-suites can also help retailers position themselves as ‘publishers’ or dynamic entities, always in touch with the pulse of their audience and, by extension, long-term growth.

Are you looking to supercharge your RMN journey? Explore how MAGE works now!


Shammo Ghosh

Senior Vice President, SME Retail










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