The Day After: Post-Cookie Apocalypse Inventory Value Enhancement for Second-Tier Video Publishers
As third-party cookies disappear, video publishers face a stark reality. Without precise targeting and retargeting, their ad inventory risks becoming commoditized. For midsize players like FAST channels and smaller ad-supported streaming platforms, this challenge is especially acute. Unlike industry giants with vast first-party data reserves and advanced ad tech ecosystems, these publishers often lack the resources to replace the functionality cookies once provided.
The result will be a shift toward generic ads aimed at generic audiences, driving down CPMs and diminishing advertiser interest. To avoid this fate, publishers must adopt innovative, privacy-compliant strategies to maintain—and even grow—the value of their ad inventory.
This article explores three actionable approaches that publishers can use to navigate the post-cookie landscape:
• Strategies that use protected data
• Strategies that don’t use protected data
• Operational tactics to maximize revenue and retain value
Let’s dive into how these approaches can help midsize publishers preserve inventory value and stay competitive in a rapidly evolving advertising ecosystem.
Strategies That Use Protected Data
Even without third-party cookies, many high-value targeting methods rely on protected data—user information that’s subject to privacy regulations. These strategies require user consent, robust data protection measures, and careful compliance with laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)/California Privacy Rights Act (CPRA).
Expand First-Party Data Capabilities
The loss of third-party cookies has made first-party data a critical asset for video publishers, particularly those operating in OTT and FAST environments. First-party data refers to information collected directly from your audience—such as viewing habits, preferences, and interactions with your platform—and is considered both privacy-compliant and highly actionable under regulations like GDPR and CCPA.
For midsize publisher, the first step is maximizing the first-party data already available through existing analytics platforms. Many OTT systems, such as those from Amagi or the recently sold Brightcove (go2sm.com/spoons), automatically gather foundational metrics like viewership trends, content performance, and device usage.
Publishers can use these metrics to create audience profiles and better understand which content drives engagement, helping them align their inventory with advertiser needs. For example, identifying the most-watched genres or peak viewing times can help prioritize premium ad slots that justify higher CPMs. Publishers must also ensure they have robust consent mechanisms in place, such as those provided by a consent management platform (CMP; go2sm.com/cmp), to meet GDPR and CCPA requirements.
Publishers can also package audience insights into actionable reports for advertisers. Rather than simply presenting raw data, publishers should focus on creating advertiser-specific narratives. For instance, a report might highlight engagement metrics for a family-focused content category, demonstrating its value to advertisers that are targeting parents.
Audience segmentation is another effective way to leverage first-party data. Publishers can offer highly targeted inventory by grouping viewers based on genres, demographics, or engagement behaviors (see Figure 1). For example, viewers who frequently watch documentaries about sustainability might be an ideal audience for eco-conscious brands. These segments provide value to advertisers and help publishers differentiate their inventory in an increasingly competitive market.
Figure 1. According to MNTN, studies show that retargeting campaigns using first-party data outperform upper-funnel efforts.
First-party data also enables publishers to understand and cater to underrepresented or niche audiences, which is a significant advantage over traditional measurement systems. Stephen Paez, EVP of cultural investment and innovation at Publicis Media, explains, “Traditional measurement companies have always underrepresented diverse audiences. … With FAST channels, you’re no longer dependent on a third party to tell you your audience size.”
By transparently presenting audience engagement and reach data, publishers can demonstrate value to advertisers targeting specific cultural or niche demographics. Custom KPIs, such as awareness metrics or time spent watching, can further strengthen these narratives, helping advertisers measure success beyond standard conversions.
This segmentation can be used internally to optimize content strategy or shared with advertisers through mechanisms like private marketplace (PMP) deals or direct sales. In these scenarios, publishers have complete control over how their audience segments are marketed, which can work effectively for niche advertisers or high-value inventory.
However, leveraging first-party data in this way has its limitations. While it allows for precision in targeting and direct control, it doesn’t scale easily across programmatic channels. Sharing audience insights with multiple supply-side platforms (SSPs) or demand-side platforms (DSPs) without exposing raw user data can be complex and often requires additional technology to ensure privacy compliance. I will discuss these technologies in the next section.
Unified IDs
Unified IDs (UIDs), based on encrypted and hashed data like email addresses or phone numbers, offer a scalable, privacy-compliant way to enhance ad monetization while maintaining compliance with GDPR and CCPA. By integrating UIDs into their ad tech stacks, OTT and FAST publishers can deliver cross-platform targeting, manage ad frequency, and command higher CPMs.
UIDs begin with first-party data collected by publishers (e.g., user accounts, viewing habits) and advertisers (e.g., loyalty programs, CRM records). This data is hashed and anonymized into unique UIDs, enabling publishers and advertisers to securely match user profiles without exposing personally identifiable information (PII). However, UIDs only function effectively within a shared ecosystem where both publishers and advertisers use the same UID framework (see Figure 2).
Figure 2. How Unified ID 2.0 works
Within this ecosystem, three things happen:
- First-Party Data Conversion: Each participant converts their first-party data into standardized hashed UIDs, ensuring privacy and interoperability across platforms.
- Secure Data Matching: Clean room environments allow publishers and advertisers to match their hashed UIDs to identify overlapping users. For instance, a publisher’s UID might reveal viewing habits, while an advertiser’s UID adds purchase history, creating richer profiles for better targeting.
- Third-Party Data Collaboration: Multi-party clean rooms enable participants to share hashed UIDs with other ecosystem members (e.g., ad tech providers or additional publishers). This allows access to aggregated third-party behavioral and demographic data, enhancing audience segmentation while maintaining privacy compliance.
Once created, UIDs are used throughout the programmatic ecosystem. Participating SSPs pass anonymized UIDs with ad requests to DSPs, where advertisers match them against their first-party data or audience profiles stored in data management platforms (DMPs). This interoperability ensures that advertisers can target enriched audience segments, such as eco-conscious viewers, while publishers expand their pool of potential buyers.
For OTT and FAST publishers, choosing the right UID provider is critical. Compatibility with the OTT ecosystem, including platforms like Roku, Apple TV, and Samsung Smart TVs, is a must. Providers like Unified ID 2.0 or LiveRamp’s RampID offer cross-platform integration, ensuring seamless recognition. Scalability also matters; solutions like ID5, which combine deterministic and probabilistic methods, allow publishers to monetize unauthenticated traffic while maximizing the value of logged-in users.
Privacy compliance remains at the heart of UID adoption. Tools like Prebid Stack provide a framework for seamless integration to ease the transition to UID-based solutions. Prebid Stack supports multiple UID providers and ensures interoperability across SSPs and DSPs. For publishers, adopting such tools ensures a smoother implementation process and maximizes the benefits of UIDs in a cookie-less world. By leveraging their own first-party data while collaborating within the UID ecosystem, OTT and FAST publishers can scale their reach, deliver personalized campaigns, and remain competitive in the evolving digital advertising landscape.
Data Clean Rooms
Data clean rooms (see Figure 3) offer a secure, privacy-compliant environment where publishers and advertisers can match and analyze first-party data without exposing raw PII. These tools are used for a variety of collaborative purposes, such as enabling publishers and advertisers to share data via UIDs or other privacy-compliant methods.
Figure 3. A data clean room is a secure environment where organizations can collect data from multiple sources and combine it with their first-party data.
Clean rooms facilitate key activities for small publishers, such as generating audience insights, enabling targeted ad activation, and improving measurement and reporting. For example, an advertiser might upload its campaign data while a FAST channel shares its audience data. By matching these datasets, the clean room can reveal overlaps, allowing small publishers to offer more precise targeting and deliver measurable campaign results.
However, implementing a data clean room is not without challenges. A significant hurdle is the need for scale. Clean rooms are most effective when working with very large datapoints. Small FAST or OTT channels may struggle to meet this threshold independently, making partnerships with larger advertisers or networks essential to achieving meaningful insights. Additionally, clean rooms require technical expertise to ensure that data is properly encrypted, uploaded, and analyzed. Publishers may need to invest in training or hire specialists to manage these processes.
Another important consideration is cost. While clean room providers like InfoSum, Snowflake, and Habu (recently acquired by LiveRamp) offer scalable solutions, implementing and maintaining a clean room can be expensive, especially for smaller publishers. Costs include licensing fees, data preparation expenses, and ongoing technical support. Publishers must weigh these expenses against the potential revenue gains from attracting premium advertisers and offering advanced measurement capabilities.
For publishers considering clean rooms, the best first step is to choose a provider and prepare their data in a structured format. Promoting the capability to advertisers is equally crucial, as it signals a commitment to advanced, privacy-safe data collaboration. With the right strategy, clean rooms can help OTT and FAST platforms unlock premium advertising opportunities and thrive in a data-driven, privacy-first landscape.
A Brief Update on the Privacy Sandbox
Google’s Privacy Sandbox is a suite of technologies designed to provide privacy-preserving alternatives to third-party cookies. Among its key initiatives are the Topics API and FLEDGE, which aim to balance targeted advertising with user privacy.
The Topics API groups users into broad interest categories (e.g., Sports, Travel), enabling publishers and advertisers to serve interest-based ads without exposing personal data. Meanwhile, FLEDGE allows retargeting within a platform or publisher's ecosystem, ensuring that users who interacted with specific content can be targeted with follow-up ads without sharing identifiable information externally.
However, these initiatives are still under development and require widespread industry adoption to become truly effective. Their current state of progress leaves much uncertainty, particularly for OTT platforms, which often operate outside the browser-based environments for which these tools were initially designed.
Adding to the uncertainty is Google’s decision to delay the deprecation of third-party cookies in Chrome, which has reduced the urgency for the Privacy Sandbox initiatives. As a result, many publishers may view these tools as back-burnered options, potentially delaying their integration into OTT workflows. For now, the Privacy Sandbox remains more relevant for browser-based platforms than for app-centric environments like OTT and FAST.
How do all of these initiatives fit together? UIDs, data clean rooms, and the Privacy Sandbox each address post-cookie challenges but with distinct purposes that together form a cohesive strategy for publishers. UIDs enable cross-platform audience recognition using deterministic data, serving as the backbone for targeting and personalization. Data clean rooms enhance this by allowing secure collaboration with advertisers to match and analyze data for actionable insights without exposing PII. Meanwhile, Google’s Privacy Sandbox, still in development, aims to provide browser-based tools like Topics API and FLEDGE for cohort targeting and retargeting. While the Privacy Sandbox has limited relevance for OTT and FAST publishers now, UIDs and data clean rooms offer immediate, complementary solutions for privacy-compliant advertising strategies.
Strategies That Don’t Use Protected Data
Unlike data-driven approaches, strategies that eschew protected data rely on aggregated or contextual insights, sidestepping the need for user consent or regulatory safeguards. These approaches are ideal for publishers seeking simpler, regulation-free solutions.
Strategy 1: Enhance Contextual Targeting Capabilities
Regardless of your progress with other strategies, it pays to enhance the ability of potential advertisers to target your content contextually. This means categorizing your video library in ways that align with advertisers’ needs, such as identifying genres, themes, and specific moments that offer optimal ad placement opportunities. Contextual targeting ensures that ads are served in the most relevant and brand-safe environments, making your inventory more valuable to advertisers.
If you’re a relatively small publisher, you can start enhancing contextual targeting manually within your digital asset management or CMS. To ensure that your efforts align with industry standards, consider referencing the Interactive Advertising Bureau’s (IAB) Content Taxonomy (now on version 3). This widely recognized framework helps structure metadata in ways that resonate with advertisers and programmatic platforms.
For example, tagging a video under Travel > Adventure Travel ensures it aligns with campaigns targeting that specific niche. Using standardized categories not only makes your content easier to discover but also simplifies future integration with automated tools that rely on IAB-compliant metadata. Additionally, adding transcripts, chapter markers, and scene-level descriptions provides even more context, enhancing the discoverability of your videos and helping advertisers identify the right spots for their campaigns.
For larger publishers or those with extensive video libraries, manually categorizing content is impractical. In this case, choose among several advanced solutions to enhance your video inventory's targeting capabilities. Tools like Peer39 enable midsize video publishers to categorize video content into custom taxonomies, making it easy for advertisers to target highly relevant, brand-safe environments. Peer39 also allows publishers to unify inventory across multiple properties, presenting a cohesive offering even if their content spans various genres or niches.
Contextual targeting can also leverage metadata to align ads with specific content themes. Alan Wolk, co-founder of TVREV, explains, “If there’s sports content on, these sorts of brands seem to go well with it, and that makes sense. If there’s a certain actor or performer that’s on there, we have data that says people who like them also like these types of brands. So, it’s become much more based on the content and fitting in.” This alignment, Wolk notes, ensures that ads are relevant to the programming, avoiding the jarring disconnects that have often plagued older targeting methods.
Anoki’s ContextIQ (see Figure 4) takes contextual targeting a step further by offering scene-level metadata. This means it can identify specific moments within a video, such as a romantic dinner scene, which could trigger ads for engagement rings or dating apps. According to Anoki co-founder Raghu Kodige, this level of targeting offers a significant opportunity for publishers to boost their revenue without relying on personal viewer data.
Figure 4. Anoki’s ContextIQ leverages the company’s advanced AI models to analyze video frames and identify a wide range of scene components.
Finally, Valossa Ad Scout employs multimodal AI to create time-coded ad markers within videos. The tool analyzes speech, visuals, and sentiment, identifying the best ad opportunities. It also scores these opportunities for suitability and brand safety, ensuring compliance with the IAB taxonomy and Global Alliance for Responsible Media brand safety guidelines. For example, Valossa may identify a cheerful family scene as a prime opportunity for family-friendly product ads while avoiding scenes with sensitive content that could harm a brand.
Contextual targeting makes your inventory more attractive to advertisers and increases CPMs. By using tools like Peer39, ContextIQ, or Valossa Ad Scout, publishers can align their content with advertiser needs in ways that are both privacy-compliant and deeply effective. As Wolk emphasizes, contextual targeting is increasingly seen as the solution to privacy concerns, ensuring advertisers can reach relevant audiences without the need for personal data.
Create Seller-Defined Audiences (SDAs)
To truly maximize the value of first-party data in a programmatic environment, publishers should explore Seller-Defined Audiences (SDAs; see Figure 5), which are designed to scale first-party audience segments across OpenRTB bid streams while maintaining user privacy. SDAs allow publishers to integrate their first-party data seamlessly into the programmatic ecosystem, enabling targeted bidding and higher CPMs.
Figure 5. SDAs allow publishers to create unique audience segments based on their proprietary first-party data.
SDAs are a solution introduced by the IAB Tech Lab as part of Project Rearc. They allow publishers to use first-party data to create audience cohorts based on user behavior, demographics, or interests without exposing individual identities. These cohorts are passed programmatically in OpenRTB bid requests to DSPs for ad-targeting decisions.
For OTT and FAST channels, SDAs provide a way to monetize viewer data across platforms while ensuring compliance with privacy regulations like GDPR and CCPA. They are particularly beneficial for publishers with niche or diverse audiences looking to offer advertisers precise, contextually relevant targeting options.
The benefits of SDAs for OTT and FAST channels are significant. First, SDAs enable granular targeting, allowing publishers to define specific segments such as Viewers > Family Programming > Educational Shows. This precision makes inventory more attractive to advertisers that value highly relevant targeting.
Second, SDAs prioritize privacy, avoiding reliance on third-party cookies or probabilistic models. Instead, they operate using anonymous metadata within the programmatic bid stream.
Additionally, SDAs support cross-platform reach, ensuring consistency in audience targeting across devices, such as connected TVs and streaming apps. Importantly, SDAs give publishers complete control over their data, eliminating the need to share sensitive information with external platforms.
Implementing SDAs involves several steps. To start, publishers must analyze their first-party data, typically with the help of a DMP, to create audience segments aligned with the IAB’s taxonomy of well more than 1,000 categories, such as Interest > Sports > Basketball. These segment IDs are then incorporated into bid requests using OpenRTB 2.6.
Header bidding solutions like Prebid can simplify this process by automating the inclusion of SDA values. DSPs evaluate the bid requests with the associated metadata and decide whether to bid based on the advertiser’s targeting goals. Winning bids result in ads served to the corresponding audience segment on the publisher’s platform.
Publishers should also communicate the value of their SDA cohorts to advertisers, emphasizing how these audience segments align with targeting objectives while maintaining privacy. Continuous optimization is critical, as campaign performance data can help refine audience definitions and improve outcomes.
For OTT and FAST channels, SDAs represent a powerful way to monetize first-party data programmatically while retaining control and prioritizing privacy. By offering highly targeted ad inventory, publishers can enhance their appeal to advertisers, increase CPMs, and remain competitive in the evolving advertising landscape.
Operational Tools and Tactics for Monetization
The loss of third-party cookies forces publishers to consider strategies and tools that maximize the value of their first-party data. This section explores key operational tools that OTT and FAST publishers should consider, offering actionable insights into how these technologies can elevate ad sales and improve overall profitability. These tools enhance the effectiveness of both data-driven and privacy-compliant approaches, ensuring publishers maximize revenue across all inventories.
Deploy Header Bidding
Header bidding, introduced around 2014, is a programmatic advertising method that allows publishers to invite multiple demand sources to bid on their ad inventory simultaneously. Unlike the traditional “waterfall” approach, which engages demand sources sequentially based on preset priorities, header bidding enables a unified auction that fosters greater competition among advertisers. This often leads to higher revenue potential for publishers, as every bid is considered without reliance on outdated priority rules.
You can see this in Figure 6. In the waterfall, the second bid of $2.25 beats the floor and wins the bid, even though Bid 3 was substantially higher. In the Header auction, all bits are considered, so Bid 3 wins at a substantially higher $3.
Figure 6. Header bidding increases CPMs by selecting the best/highest bid request, rather than the first bid that meets all of your parameters.
While client-side header bidding (using JavaScript tags) is common in web environments, server-side header bidding is often preferred for OTT and FAST platforms. This approach moves the auction process to the server, significantly reducing latency and improving the viewer experience—an essential factor for video content. Video-specific header bidding solutions, such as Prebid for Video, further cater to the unique needs of streaming environments by supporting server-side ad insertion, ad podding, and seamless compatibility across various devices.
To implement header bidding, publishers can either adopt open source solutions like Prebid or partner with specialized providers like Snigel. While open source tools offer flexibility, they require substantial management of demand partner relationships. A header bidding partner simplifies the process while ensuring privacy-compliant, first-party data targeting. By leveraging server-side header bidding and video-specific solutions, OTT and FAST publishers can optimize ad delivery, enhance viewer experiences, and maximize revenue from their video inventory.
Consider a Self-Serve Ad Program
The final option worth considering is a self-serve ad platform. These platforms allow advertisers to directly access audience segments within the publisher’s ecosystem, bypassing reliance on third-party cookies while maintaining user privacy. By integrating audience targeting and campaign management into a self-contained system, publishers can offer unique value to advertisers while retaining full control over their data and adhering to privacy regulations like GDPR and CCPA. Advertisers also benefit from direct access to relevant audiences without the uncertainty of broader programmatic ecosystems.
However, scalability remains a challenge. Unlike participating in larger DSP/SSP ecosystems, self-serve platforms may limit reach. To implement self-serve capabilities, publishers should start small by testing with trusted advertisers, invest in modular solutions that integrate seamlessly with existing ad stacks, and ensure a user-friendly interface for advertisers. Platforms like Roku Ads Manager and Spotify have demonstrated potential for success by offering targeted ad opportunities within their ecosystems, at least for companies of their respective sizes.
Summary
The shift to a post-cookie advertising ecosystem is challenging. But it also presents opportunities for OTT and FAST publishers to innovate and take greater control over their data and monetization strategies. By embracing privacy-compliant tools like UIDs, data clean rooms, and SDAs, along with enhancing first-party data capabilities and leveraging operational tools like header bidding and self-serve platforms, publishers can adapt and thrive. Success lies in balancing audience insights with privacy and offering advertisers valuable, targeted inventory while building sustainable revenue streams in a rapidly evolving landscape.
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