The AdTech industry is buzzing with discussions about cookie deprecation and effective strategies to tackle it. One of the commonly suggested solutions is the utilization of clean rooms alongside responsibly sourced first-party data.
Above all else, the industry recognizes the importance of respecting consumer data and complying with all privacy laws. Additionally, the industry acknowledges the need for a change in our historical practices. This shift benefits everyone involved, as consumer data is more secure than ever. Tremendous investments have been made to ensure the utmost security of consumer information.
Clean rooms are one of the tools that enable companies to use data securely, ensuring the content that you see is as relevant as possible.
Two ways the AdTech industry is addressing cookie deprecation
The days of sending data directly to partners for usage or for using only third-party data for marketing efforts are gone. Now, the emphasis is on responsibly collecting first-party data and using clean rooms to enrich first-party data to enhance marketing efforts.
First-party data
The industry is starting to lean into first-party data gained through transparent means. This valuable information provides organizations with deeper insights into their customers, allowing for more personalized and effective interactions. By embracing the power of first-party data, either on its own or enriched via partner collaboration, you can cultivate stronger relationships, build trust, and deliver tailored experiences that resonate with your customers on a deeper level.
Clean rooms
Many data lakes and warehouses offer this service, ensuring their clients can not only store their data with them but can connect it with other partners in a secure environment and extract more information through the combined data sets versus their data on its own.
Brands and their partners recognize that they need to work together, and a clean room provides a secure environment to share their first-party data without exposing their sensitive data to their partner.
So, while we’re losing third-party cookies, brands and partners can still get value from first-party data by using a clean room to generate audience insights, segmentation strategies, personalized experiences and offers, media plans, and measurement and attribution.
Three ways data clean rooms can improve
Data clean rooms are a great way to facilitate data collaboration while ensuring sensitive data is not exposed.
Data clean rooms are not yet easy to use nor are they inexpensive. They require investment, both financially and resource allocation-wise, and you are not guaranteed to yield great match results. Let’s dive into three areas for data clean room improvement.
High cost
According to the IAB’s State of Data 2023, nearly two-thirds of data clean room users spent at least $200K on the technology in 2022. In addition, one-third of data clean room users expect the price of data clean rooms to rise in 2023. The high cost of this solution can make it inaccessible to smaller companies in the advertising space.
Resource intensive
Nearly half of the companies using data clean rooms have a team of six or more dedicated to the technology, according to the IAB’s State of Data 2023, while nearly a third of companies using data clean rooms have 11 or more employees focused on the technology. Data clean rooms are not turnkey solutions.
Inefficient matching
Even if companies are using clean rooms does not mean that they are automatically going to achieve great success. Identity fragmentation, data hygiene, and differing identifiers can suppress client match rates in clean rooms, leading to significant investment and a lackluster output.
How to get the most return on your clean room investment
The finish line for data collaboration in clean rooms is not just having a relationship with a clean room. Instead, you should incorporate an identity resolution solution in your clean room. By adding an identity solution to your clean room, you can:
- Resolve and match all your identity data, regardless of the identity data that you or your partner have, giving you a larger data foundation to analyze.
- Generate more valuable insights and information, leading to a better experience for your customers.
- Join data sets to create smarter activation and targeting strategies and produce more holistic measurement.
Experian can help you get started with identity resolution and data clean rooms
If you are investing in data clean rooms, that means you are committed to the best in data practices. Experian recommends going the extra step and that you also invest in finding an identity resolution solution. By doing this, you can see better match rates.
Experian offers this capability and has existing relationships with three clean room partners, Amazon Web Services, InfoSum, and Snowflake. In addition to collaborating in clean rooms, we offer collaboration in two other secure environments.
Contact us today to discuss how we enable identity resolution in clean rooms or to chat about our other collaboration capabilities.
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In our Ask the Expert series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Samantha Zhang, Senior Data Scientist, and Jim Meyer, General Manager of the DASH TV Universe Study at the Advertising Research Foundation (ARF). DASH is an annual tracking study conducted by the ARF to define and better understand TV audience behavior and household dynamics. What does DASH measure, and how does it help the industry understand TV consumption today? By capturing hundreds of individual- and household-level data points from each respondent in a rigorous and nationally projectable sample, DASH creates a comprehensive picture of U.S. consumer TV “infrastructure” – how America watches. Core elements in DASHElements that create context in DASHTV setsLocation | brand | smartness | service modes | sources DemographicsConnected devices Game consoles |video players | streaming devicesYesterday viewing Daypart | TV/device genre | Out-of-home viewingMobile devicesOwners | sharing usersShoppingOnline and in-store | Exposure to major RMNsInternet serviceModes | ISPs | connectivity by device Streaming audio Streaming TVSVOD/AVOD tiers and sharing | FAST Email accounts and apps Live TV Modes of access | including casting from devices Social media For example, DASH gathers: Data on every TV set, including brand, room location, age, “smartness,” and connection devices and modes Household connectivity and video service data, even in homes with no TV set Internet Service Providers (ISP) and TV service usage, including Multichannel Video Programming Distributors (MVPDs), virtual vMVPDs, streamers (ad-supported and premium), and Free Ad-Supported Television (FAST) channels Person-level ownership and usage of video-capable mobile devices, including smartphones, tablets, and laptops Measures of viewing and co-viewing across dayparts, devices, and services Additional modules covering shopping and retail media networks, streaming audio, social media, email, and apps Broad coverage and granularity make DASH a uniquely robust source of truth for practitioners across the industry, including measurement experts and ad programming strategists. DASH also reports regularly (and publicly) on key industry dynamics. DASH identified a growing segment of device-only viewers – now nearly 9 million households that watch TV, but do not own a TV set – and highlighted the implications of that trend for traditional ratings systems based only on households with TV sets. Households (HHs – million)2025 HHs (M) U.S. penetrationChange vs. 2024 (M)Total US134.8100%+2.7Connected TV (CTV)114.685%+2.1TV (Set)124.292.2%+1.1Device-only8.86.6%+1.6TV-Accessible133.198.7%+2.7 DASH called out the rise in app-based pay TV and proposed a new connection framework that better represents the modern TV world, in which linear and streaming overlap. DASH also defines the universes of households reachable with advertising. This graphic, for example, shows how all ad-supported linear and streaming properties in aggregate define the true scale of TV advertising. While 35 million households (and growing) are reachable only with streaming ads and 13 million (and falling) only with linear ads, most households are reachable with both, underscoring the importance of understanding the “overlap.” Who uses DASH data, and what decisions does it help inform? There are three primary users of DASH, each with its own use cases: Measurement providers, including Nielsen, use DASH to calibrate viewership data, turn household data into persons data (and vice versa) and estimate potential reached audiences–what the providers call media-related universe estimate (MRUEs)–for the calculation of ratings. Not surprisingly, measurement companies were the first to see the value that an independent TV universe study could provide. Media companies, including major broadcasters and streamers, use DASH to add context and color to their ad sales presentations – and to track the measurement providers, whose ratings play a major role in valuing ad inventory. AdTech companies, including Experian, use DASH to create high-value audience segments for activation. The recent accreditation of DASH by the Media Rating Council (MRC) and adoption by Nielsen as an input to its TV ratings have generated interest from a broad range of companies. We are actively pursuing new licensees and partners to make DASH more useful within, and even outside, the TV ecosystem. What does MRC accreditation signify, and why is it meaningful for DASH? MRC accreditation means DASH passed a rigorous audit conducted by Ernst & Young over many months, which validated our methodology, controls, and data quality. MRC accreditation establishes that DASH is an industry-standard dataset. While the service provider normally announces its own accreditation, the MRC took the unusual step of issuing its own release on DASH, announcing the accreditation of DASH for TV universe estimation and endorsing the study for broader, cross-media use. How does Experian use DASH data to build audiences? The segments combine specific TV usage habits and behaviors from DASH with Experian data on demographics, spending, and other contextual inputs to create a fuller view of consumer viewing behavior. They are designed to be valuable to advertisers in many categories and planning contexts – and to be customizable to fit advertisers’ media targets. The segments can be used to: Apply or suppress audiences to improve target coverage across a campaign Better align media and creative Reach elusive but high-value viewers, such as Ad Avoiders Drive valuable consumer behavior Achieve specific advertising objectives What are some practical use cases for DASH-based audiences? Here are some practical use cases for four different kinds of DASH segments in five different advertiser categories. Travel Co-WatchersA couples-only resort uses TV Co-Watching Households without Children to strengthen target reach and ad memory recallA big theme park destination uses TV Co-Watching Households with Children to reach families in moments of togetherness Home Entertainment TV Owners and Brand LoyalistsA premium TV manufacturer uses the overlap of Multi Brand TV Owners and Single Brand TV Loyalist Households to market its newest TV model to its most loyal consumers. Fast Food Screen Size ViewersA fast food chain with a high-impact new brand campaign uses Large Screen TV Viewers to better align the media and creativeThat same fast food chain uses Small-Screen TV Viewers to drive store traffic by increasing exposure of its retail campaign among on-the-go viewers Financial Services Cord Cutters A personal cost management app and a cash-back credit card target Streaming-First Cord Cutter Households to reach young, tech-savvy, cost-conscious consumers Thanks for the interview. Where can readers learn more about DASH? We started work on DASH seven years ago, and it’s been fun to watch it “grow up.” Our partnership with Experian is a big step toward putting DASH to work for advertisers and agencies. To learn more, visit our site at https://theARF.org/DASH or contact us at DASH@theARF.org. Contact us About our experts Samantha Zhang, Senior Data Scientist at ARF Samantha Zhang is a Senior Data Scientist at the Advertising Research Foundation working on the DASH TV Universe Study, with additional research spanning areas including attention measurement, digital privacy, and artificial intelligence. Jim Meyer, General Manager, DASH, at ARF Jim Meyer is general manager and co-founder of the ARF DASH TV Universe Study and managing partner of Golden Square, LLC, which advises media and research technology companies on growth strategy and development. Latest posts
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