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Many discussions about privacy in programmatic advertising focus on mobile and web. But advertisers and publishers alike also need to pay attention to how connected television (CTV) is shifting in an increasingly privacy-centric world. In a recent Q&A with AdvertisingWeek, we explored the evolution of privacy in CTV, IAB’s Transparency Consent Framework, IP addresses, ID-less targeting, and much more. TV has traditionally been seen as privacy-safe. How has that changed as the connected TV (CTV) ecosystem has evolved? Fragmented development across digital and linear channels has made TV planning, activation and measurement more complex. In the advanced or addressable TV space, a key element of that has been transferring online data technologies and practices so that media owners can offer cutting-edge targeting and effective performance analysis for advertisers. This includes moving away from traditional limited (and opted-in) panels and shifting toward large-scale individual tracking using personally identifiable information (PII). Unsurprisingly, this has raised concerns around user privacy and questions about what should be done to improve data protection. For instance, a recent CTV report from the American Association of Advertising Agencies (4A’s) has encouraged deeper consideration of every privacy-related decision in line with several factors, including a company’s market position, how data usage balances business value versus privacy cost and compliance with regional regulations. But while these steps seem theoretically relevant, they are also likely to be too convoluted for CTV. In practice, enabling low-friction and future-proof growth will call for versatile and privacy-safe identity management approaches that can easily adjust to the needs and specifications of multiple media environments. When will the Transparency Consent Framework (TCF) be applied to CTV? While third-party cookies, device identifiers, and cross-industry IDs continue to play a major role in tailored ad delivery for web and mobile, their use is increasingly subject to consent-based provisions laid out by data legislation and tech providers. It therefore makes sense that the TCF — built to help keep advertising in line with regulations such as the GDPR and CCPA — has already gained significant ground in these areas. Despite attempts from leaders such as the Internet Advertising Bureau (IAB) to introduce opt-in mechanisms via its guidelines on using over-the-top platforms (OTT) advertising identifiers, the emerging CTV market has so far resisted standardization. Yet amid greater reliance on AVOD and hybrid monetization models, alongside tighter data handling rules from key tech players, avoidance is becoming less of an option. Google, for example, has recently announced plans to roll out its European consent policy to CTV, meaning publishers and developers using Google products must integrate with the TCF and implement a Google-certified consent management platform (CMP). Originally set to go into effect last month, the due date is now deferred to July 2025 but still very much on the horizon. What is the long-term value of identifiers based on first-party data (such as IP addresses)? Although seen as a “must-buy” for several years now, and especially important in branding campaigns, CTV’s addressability and measurement issues have posed persistent barriers to performance assessment that often put its ROI in doubt. Approaches that rely on first-party data such as IP addresses to identify specific households can help address this problem by supporting more precise ad delivery and evaluation. At the same time, however, they also have their difficulties. Topping the list of downsides is the fact that many global data laws consider IP addresses to be PII — including the EU and UK GDPR. Gaining consent to use IP addresses for ad purposes in CTV environments isn’t easy in these cases. Added to this is the strong likelihood that browser-based efforts to mask or disguise IP addresses, such as Apple’s Intelligent Tracking Prevention and Google’s Gnatcatcher, will expand to CTV apps and platforms sooner or later. In the future, we can expect heavy ongoing IP address use wherever possible across the CTV ecosystem. But as consent requirements incrementally chip away at data access, advertisers looking to maximize scalability will need to begin exploring a wider range of future-proof options that enable data sharing in a standardized and privacy-first way. What are the ID-less alternatives for CTV advertising? After years of innovation to facilitate cookie-free operations, the answer here is almost limitless. Take contextual advertising as an obvious example. Building on the historic method of marrying ads with basic TV programming categories, machine learning progress has created opportunities to achieve refined content analysis that enables granular classification by subject matter and sentiment, which ensures effective and ID-less matches. Cohort-based methods also provide ways to use device data to drive smart segmentation informed by anonymized interests or characteristics, without exposing user-level information. While tools rooted in third-party data such as Google’s Topics API are the typical example, seller-defined audiences aren’t dissimilar: media owners leverage user-supplied first-party data from sites, apps, and CTV platforms to create targetable audience groups. From a measurement perspective, brand uplift and conversion studies, panel-based ad ratings, statistical sampling and media mix modeling are the most privacy-conscious CTV choices. Why is attention becoming a more important metric? Aren’t ads always 100% viewable? It’s well-recognized that the high viewability of TV ads doesn’t always mean that they are seen by intended viewers, especially with second screening a regular global habit. In around 2015, researchers and vendors began working on tools to measure how audiences engage with ads. As you might expect, results showed that interaction was variable and campaigns that captured more in-the-moment attention drove better conversions. Subsequent studies proved that longer durations of attention for individual impressions closely correlated to increased impact throughout the marketing lifecycle — a finding that has cemented attention measurement as an essential performance metric that’s frequently more illuminating than simply tracking impression numbers and viewability. What do you see as the biggest future impacts for privacy on CTV? Will the rising volume of programmatically-traded inventory play a part? To a great extent, the general TV landscape will keep advancing in the same digital direction. Legacy linear practices of programming ad slots according to pre-determined schedules are rapidly becoming relics of the past while CTV takes a continually rising share of budgets; with European…

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Chrome has dominated the third-party cookie story since Google declared its deprecation intentions. Following an official halt to default blocking, advertising attention is now fixed on the new choice-centric browser experience and how web-based Privacy Sandbox adoption will pan out. However, changes set to be just as impactful for mobile are forging ahead unchanged and largely unnoticed. A Privacy Sandbox initiative for Android has been in the works since 2022. While there is no defined timeline, tools moving into beta last year suggest development is advancing, but many industry players are yet to explore these technologies and their potential wide-ranging impact.  Given Android’s 70% share of the global mobile market, failing to understand and proactively prepare for these proposals is a major oversight that could create sizable challenges for app publishers and advertisers.  Not all sandboxes are the same Firstly, it’s vital to recognize that the web and Android Privacy Sandbox initiatives are separate entities. Both share the mission to protect user privacy and allow businesses to maintain a thriving digital media ecosystem. While these branches share a unified vision and core components, they don’t work in the same way. This is largely due to the focus on usually cloud-centric activity for Chrome Sandbox solutions, while Android tools are designed for app software that users download and engage with via their devices.  Looking closer at the details, there are several key variations across the five main Android APIs — Topics, Protected Audience, Protected App Signals, Attribution Reporting, and SDK Runtime. Cohorts in, IDs out The familiar Topics API concept involves collecting on-device data to determine which topics users are interested in and putting them in cohorts that can be applied for targeting, such as ‘fitness lovers’ and ‘sci-fi fans’.  High on the list of pros for this tool is its capacity to seamlessly slot in with current trading. Topics categories can be added as another signal sent to demand-side platforms (DSPs) along existing programmatic pipes, allowing buyers to place real-time bids on app inventory that will reach relevant audiences, even if they don’t know who exactly will see their ads. Zero reliance on identifiers is also a big plus given their diminishing scope. Google maintains an emphasis on providing options that can work without IDs, because doing so has become increasingly crucial. In the immediate aftermath of Apple’s decision to make its iOS ad ID opt-in only, many users seized the opportunity to improve their mobile privacy, with consent rates teetering at 25% at that time. Opening the box of APIs  The Privacy-Preserving APIs provide another suite of solutions that is distinct from Topics but also intended to support ID-less operations. Within this collection are multiple building blocks designed to support use cases throughout the ad lifecycle, the main elements being Protected Audience, Protected App Signals and Attribution Reporting.  Protected Auction has two strains: Custom Audience for user segmentation in a re-marketing use case and Protected App Signals for app install (aka user acquisition) use cases without the reliance on cross-app identifiers. Crafting custom audiences is about creating groups based on interests or intentions, such as users who have abandoned an e-commerce app purchase over the last month. Local data storage that can’t be transferred between apps helps to maintain user privacy here. Ad Selection in a protected auction follows a structure for managing bidding activity that upends standard programmatic procedure. Normally, app publishers send audience data with bid requests, allowing demand-side vendors to produce profiles that are used for refined bidding according to key advertiser targeting preferences. Protected auctions turn the tables and means that buyers have to bring their requirements and data. This is then used in an auction inside a trusted execution environment and the only information that leaves the auction room is who has won.  Safe in the knowledge that data won’t leave the auction and users cannot be identified in the absence of cross-app identifiers, participants could work with more device-level data to boost ad relevance and impact, as long as there are robust enough provisions to prevent data leakage. In effect, auction spaces could follow the principles of data clean rooms.  Finally, Attribution Reporting functions like Apple’s SKAdNetwork (now App AdAttributionKit). Ad tech vendors register attribution sources (such as clicks and ad views) and triggers (in-app conversions), and the API links the two together through event-level and aggregated summary reports. Measurement is subject to several limitations to ensure advertisers can see a clear line from campaigns to conversion actions, but not individual users.  The great unbundling  SDKs aren’t just a bridge from platforms to apps. Deep integration practices have historically weaved them so tightly into the fabric of apps that it’s difficult to tell where the dividing lines lie. This close connection has facilitated smooth interaction with external tools and services, such as monetization SDKs that enable publishers to fuel crucial revenue with ads and mobile measurement partners (MMP). Yet the same free flow also allows SDKs to inherit privileges and data permissions, which raises concerns about privacy and the risk of unpermitted data collection.  Google hopes to tackle this with its enclosed SDK Runtime environment. By providing an isolation process that can run strains of code independently, the solution will unbundle third-party SDKs from apps. Additionally, there are further plans to cap the data SDKs can access, with initial designs suggesting a default list of four elements: internet connectivity, network information, phone state (such as network type), Google’s Privacy-Preserving APIs, and an ad ID — but only if users have granted the host app consent. These proposals have sparked anxiety about the challenges of complying with differing data requirements, especially for omni-channel campaigns. The reality is that SDK Runtime will likely mean less convolution. Just as Google can issue operating system updates to devices without disrupting apps, third-party vendors will be able to build new SDK iterations and fixes that publishers can implement without having to invest considerable time and resources in re-distributing their apps.  Why should the industry care?  It’s hard to predict precisely how Android tools will evolve or impact the industry. However, we can be sure of the need to…