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From Context to Clean Rooms: How Brands Can Adapt to the AI-Driven Evolution of Programmatic Advertising

SUMMARY

Discover how brands can adapt to the future of programmatic advertising with AI-driven targeting, privacy-first strategies, and smarter measurement in a cookieless world.

Picture of Rachel Horner

Rachel Horner

Rachel Horner serves as a Content Marketing Writer for Feedvisor. She has extensive experience in writing for diverse B2B brands, particularly in the tech industry, and is dedicated to fostering meaningful brand-audience connections.

What started as a way to automate ad buying is now a data-driven battleground. Originally designed to streamline media buying through real-time bidding (RTB), programmatic advertising has evolved over the years since its inception in the late 2000s, now incorporating audience targeting, dynamic creatives, and AI-driven optimizations.

Fast-forward to 2025 and the landscape is poised to shift again. Transparency concerns, the rise of private marketplace (PMP) deals, and the growing influence of artificial intelligence (AI) are reshaping how ads are bought, delivered, and measured. At the same time, the industry-wide move toward a cookieless future is forcing brands to rethink audience targeting and attribution.

As we enter this next phase, programmatic advertising will no longer just be about targeting; it will leverage AI-driven insights, context, and first-party data to deliver ads that truly resonate with consumers. For e-commerce brands, this shift presents both challenges and opportunities, redefining how ads are bought, delivered, and measured in ways that drive better engagement and higher returns. Below, we share how these changes can impact your advertising strategy and how to adapt.

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Contextual Targeting Makes a Comeback

As cookies fade, AI-driven contextual advertising is making a powerful comeback, with recent advancements unlocking new and exciting opportunities for advertisers.

Source: Amazon Ads

Contextual targeting was once limited by rigid standards and broad categorizations, designed to simplify targeting by grouping users into predefined segments. While this reduced complexity, it often failed to capture niche audiences, resulting in ads being placed alongside irrelevant content that didn’t resonate or drive engagement.

AI-driven contextual targeting has since evolved, using advanced machine learning to analyze vast datasets and identify nuanced consumer contexts across diverse media. Advertisers shifting from “on the viewer” to “on the page” now leverage AI-driven signals to reach high-propensity users and optimize KPIs. This hybrid approach combines contextual relevance with predictive accuracy, making programmatic advertising more effective in a privacy-first era.

As digital advertising evolves, contextual targeting is poised to shape the future of ad placements in a post-cookie world, striking a balance between relevance, engagement, and ethical advertising practices.

Clean Rooms Become Key in the Privacy-First Era

The advertising industry is still adjusting to the challenges of privacy-first advertising.  The evolving legal and regulatory requirements and the ongoing impact of Apple’s and Google’s opt-out policies are reshaping what compliance, data ownership and measurement look like for digital advertising without the help of third-party cookies. 

A clean room is a secure, privacy-compliant environment where multiple parties can share and analyze aggregated data without exposing individual consumer information. It allows advertisers, retailers, and publishers to collaborate on insights while maintaining data privacy and security.

In response to privacy laws and the growing loss of tracking signals, nearly two-thirds of data and advertising professionals in the U.S. have embraced privacy-first data clean rooms. These tools are becoming essential for integrating fragmented retail media datasets and moving beyond historical behaviors. By partnering with publishers and suppliers, retailers can combine valuable data assets within a privacy-conscious framework. Through clean rooms, they can expand their managed services, while effectively demonstrating the impact of retail media investments.

These environments securely unify first-party data with predictive audience insights, unlocking omnichannel visibility and enabling more strategic decision-making. As Amazon and Walmart expand into upper-funnel channels like Prime Video and Vizio, advertisers must evolve beyond return on ad spend (RoAS) to measure incrementality and brand impact.

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Mapping the Full Customer Journey: The New Measurement Playbook for Brands

Future-proof measurement strategies will combine traditional techniques—such as media mix modeling and A/B testing—with advanced metrics, including lifetime value (LTV), purchase path analysis, and multi-touch attribution.

Amazon’s launch of its Multi-Touch Attribution feature is a great example. MTA provides more detailed insights into how different touchpoints influence a consumer’s decision-making process. With MTA, a brand can see, for example, that an ad thought to be ineffective is actually contributing significantly to shopper engagement. This level of insight wouldn’t be available with last-touch attribution or traditional MMM, which tends to oversimplify the customer journey by focusing on the final point of interaction.

In tandem, A/B testing helps optimize specific campaign elements (e.g., creative, copy, targeting) for immediate performance. However, when integrated with LTV and purchase path analysis, A/B testing provides even deeper insights. While A/B testing measures short-term effects (like immediate conversions), LTV enables you to assess the long-term value of a customer acquired through specific campaigns. This is particularly powerful for evaluating the success of campaigns over time and determining which strategies foster not just immediate purchases, but sustained brand loyalty and higher customer retention.

Lastly, purchase path analysis provides the full story of how a consumer interacts with a brand, from the first touchpoint to the final purchase. Traditional methods like A/B testing or MMM are typically focused on broader audience segments and may not account for the multi-device, multi-touchpoint nature of today’s consumer journey. 

By mapping the entire customer journey, however, purchase path analysis reveals how each interaction across channels contributes to a sale. When combined with traditional techniques, this analysis enables brands to optimize their strategies more precisely across different stages of the funnel, rather than focusing only on the end conversion.

Private Marketplaces (PMPs) Are Evolving: From Ad Waste to Quality-Driven Deals

PMPs are also being redefined. Originally seen as a way for publishers and buyers to bypass the complexities of the programmatic supply chain, private marketplace deals are meant to offer a more direct, premium experience. 

However, data in recent years has shown that these private deals often feature the same low-quality media found in the open internet — a report from the Association of National Advertisers revealed that 14% of spend in PMPs went to “made-for-advertising” sites, which exist solely for ad revenue, not to serve readers.

But this is changing. Where buyers once faced constraints due to fraud and limited premium inventory, publishers are now restructuring supply chains to prioritize performance-based quality. Audience targeting will align with contextual relevance to breathe new life into PMPs, reducing inefficiencies and creating more seamless connections between brands and consumers.

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How Can Brands Take Advantage of An AI-First Programmatic World? 

To stay competitive in an AI-first programmatic landscape, brands must rethink their data strategies, measurement frameworks, and ad placements. Here are four key ways to adapt and maximize success.

1. Invest in Privacy-First Data Strategies

Implement privacy-first clean rooms to unify retail media and customer data across platforms. Partner with technology providers to integrate predictive modeling and AI-driven audience segmentation for more precise targeting.

2. Optimize Upper-Funnel Measurement Tactics

Move beyond RoAS. Develop a holistic media mix strategy incorporating incrementality testing, LTV, and purchase path analysis. Experiment with new KPIs to measure long-term brand impact in addition to short-term sales.

3. Leverage Amazon’s Retail Ad Service

For third-party retailers, explore Amazon’s Retail Ad Service to monetize website traffic with contextually relevant sponsored products. Advertisers should look for “Advertise products across retailers” in the Amazon Ads Console and optimize performance using familiar reporting tools and APIs.

4. Reshape Your Programmatic Supply Chain

Prioritize PMPs that guarantee contextual relevance and performance-based inventory. Align with publishers offering AI-enhanced audience targeting to reduce inefficiencies and improve quality outcomes.

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Final Thoughts

As programmatic advertising continues to evolve, it’s clear that adapting to these changes will be essential for e-commerce brands looking to stay competitive. From leveraging AI and first-party data to navigating the shift toward a cookieless future, the landscape is rapidly transforming. 

To help you stay ahead of the curve, we dive into these shifts—and other key e-commerce trends—in our E-Commerce Trends and Predictions Report for 2025. Download it now for expert insights and strategies you need to keep your brand at the forefront of e-commerce.

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