At A Glance
Reaching the right audience is becoming more challenging as digital advertising grows increasingly fragmented, and privacy regulations change. 33Across addresses this by providing real-time intent signals and contextual insights, helping marketers connect with consumers across devices like CTV, mobile, and desktop. Integration into Experian's marketplace makes these solutions more accessible, helping advertisers achieve measurable results.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 Allison Dewey (Director, Data & Curation) at 33Across.
Navigating complexity in digital advertising
Digital advertising is more fragmented and privacy-constrained than ever. How is 33Across helping marketers cut through that complexity to drive real outcomes, and what makes your approach distinct?
Reaching audiences without compromising scale or performance is one of the toughest challenges for marketers. Users consume content across multiple devices and channels, making it difficult for marketers to identify and accurately target them with the right message. 33Across segments leverage AI-powered contextual and behavioral signals across privacy-safe environments to help marketers accurately identify audiences, whether they’re streaming content on their connected TV or researching products on their mobile device. What sets us apart is that we don’t just identify valuable audiences; we help marketers also target cookieless inventory and power it with real-time signals.
Marketplace integration milestones
What excites you most about bringing 33Across audiences into Experian’s data marketplace?
We’re excited to bring 33Across audiences into Experian’s data marketplace because it connects our unique segments with a powerful data ecosystem that marketers already trust. Buyers looking to activate audiences that are both privacy-safe and performant continue to tap into the Experian data marketplace for high-quality, high-performing data. We offer a wide range of audience verticals, including B2B, demographic, retail purchase data, interest and intent, and political data. In addition, we offer the ability to create custom segments across verticals.
Our intent-based audiences, built from contextual and engagement signals, help buyers reach consumers on CTV, desktop, or mobile devices with scale. Being part of Experian’s data marketplace accelerates access to these audiences, drives better ROI, and helps brands future-proof their strategies today.
Retail demand signals
Retail brands are racing toward privacy-safe, first-party data. Which 33Across retail datasets or segments are experiencing the highest demand, and what makes them a must-have?
Retail marketers are leaning into contextual and behavioral intent signals to complement their first-party data strategies. At 33Across, we’re seeing high demand for segments tied to shopping intent, including in-market consumers browsing for categories like fashion, home goods, electronics, and health & wellness.
What makes these segments essential is their real-time nature – they can capture consumer interest as it happens. For retail brands looking to expand their reach while respecting privacy, our segments offer scalable, actionable intent that drives results.
B2B without cookies
Reaching real B2B decision-makers at scale is tough with or without signals. How does 33Across deliver both precision and reach in this environment?
B2B marketing often struggles with balancing scale and specificity. 33Across addresses this by combining contextual precision with AI-modeled behavioral signals; this segment approach reaches professionals actively engaging with relevant content and topics, even in environments where IDs are unavailable. Marketers gain access to more signals and, in turn, better reach from 33Across’ unique publisher integrations and audience curation built from machine learning and AI.
We surface intent through content consumption patterns and contextual engagement, unlocking valuable, privacy-safe signals at scale. Allowing B2B marketers to reach real decision-makers in a signal-sparse world.
Use cases
With retail, B2B, and beyond, can you share an example of how brands in these verticals are utilizing your audiences?
Top brands that have a user-focused approach use 33Across audiences to drive scale; performance. These brands enable our segments to precisely reach the right users across devices and increase conversion rates; brand awareness. By reaching the right users, brands have higher conversion rates and increase campaign efficiency.
Supply path innovation
As identifiers disappear, advertisers are looking for scalable, privacy-safe ways to reach real people. How is 33Across helping unlock more addressable inventory and drive performance?
By combining contextual, semantic, and engagement-based signals, we deliver intent-based targeting that performs across CTV, display and video. Higher addressability helps marketers not only extend their reach but also deliver personalized messaging across digital channels in a privacy-compliant way.
Contact us
About our expert
Allison Dewey
Director of Data and Curation, 33Across
Allison Dewey is the Director of Data & Curation at 33Across, where she oversees data partnerships, integrations, and supply-side curation. With a deep expertise in audience targeting and signal optimization, Allison plays a key role in connecting data into the programmatic world. Allison holds a Bachelor’s degree in Psychology from Bates College.
About 33Across
Rooted in over 15 years of data expertise, 33Across harnesses signals to enrich and expand marketers’ audiences and reach them wherever they consume content. Built from over 300 billion proprietary data signals, we apply machine learning and AI to create over 1,500 B2C and B2B segments using privacy-first principles to reach audiences.
Cookieless targeting FAQs
By using contextual and engagement-based signals, advertisers can target consumers across CTV, mobile, and desktop in a privacy compliant way, even as identifiers become less available.
Segments tied to shopping intent, such as consumers browsing fashion, electronics, or health products, are highly sought after because they capture real time interest and drive results.
Combining content engagement patterns with machine learning allows marketers to reach professionals actively engaging with relevant topics, even in environments where IDs are unavailable.
Privacy safe targeting uses real time contextual and behavioral signals to deliver relevant messaging across devices and channels without compromising consumer trust.
Real time intent signals allow advertisers to capture consumer interest as it happens, helping demand side platforms and brands deliver timely, relevant ads that increase engagement and drive conversions across devices like CTV, mobile, and desktop.
Latest posts
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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