Key takeaways
- Commerce media networks (CMNs) hit a growth ceiling when on-site inventory caps out and first-party data only reaches known shoppers.
- Building identity infrastructure in-house can take a year or more before a network has a new audience product to sell.
- Three tests reveal real neutrality in a data partner: revenue source, representation on both sides of a deal, and identifier portability.
- Experian’s neutral infrastructure layer connects identity, activation, and measurement for commerce media networks without competing for advertiser spend.
Most conversations I have with commerce media network leaders open the same way. The advertiser demand is there, the first-party data is there, and a shortlist of three vendors all describe what they do in language that sounds interchangeable. Choosing between them is harder than it should be.
A constraint sits under that shortlist. On-site inventory runs out, first-party data reaches known shoppers and stops there, and advertisers keep asking for proof of incremental outcomes. Growing past all three takes capabilities the network doesn’t own, which makes the partner decision one of the more consequential calls these teams will make this year.
Most networks get sold a construction project
The pitches converge on roughly the same ideas. A network licenses an identity graph, adopts a platform, or takes delivery of components its own engineers assemble into something advertisers want to buy, sometime over the next several quarters. A few of the largest networks have the engineering team and patience for that.
Most do not. Commerce media operations already stretch across e-commerce, merchandising, analytics, legal, and privacy, with audience definitions that drift between teams and workflows that break at the handoffs. The networks taking on these build projects have the least resources and bandwidth to absorb them. I’ve watched networks buy an identity capability and go a full year before they had a single new audience product to sell.
What a network needs on day one is their customer data enriched, identities unified, the activation paths connected, and the measurement loop closed.
Your identity layer shouldn’t become someone else’s ecosystem
The second challenge is control. A media network’s customer data is its one unique asset, and the identity infrastructure decides how far that asset travels. A commerce media network should own its customer intelligence and use identity infrastructure that expands where that intelligence can go.
Identity should make that intelligence more useful and not make you dependent on someone else’s ecosystem. That means retaining control of customer data and choosing infrastructure that can connect it across partners, platforms, activation environments, and measurement providers.
For commerce media networks, independence is commercial infrastructure. Your identity layer should expand your options, not limit them.
Three tests separate structural neutrality from the claim
Independence costs nothing to assert. These three are harder to fake:
Experian clears all three because of how our business is built. We sell no media, own no supply, and represent no buyer, so the incentive to steer a media network’s spend never exists in the first place.
What Experian’s infrastructure layer delivers for commerce media networks
Many clients own their onboarding, data platforms, and clean room relationships, and our work improves the data entering those workflows. Match rates rise, addressable audiences expand, and the existing investment gets more productive.
Choose the infrastructure layer with no stake in your outcome
Run the three tests on every partner currently holding your identity, activation, or measurement. The answers will tell you how much of your business you’ve already made visible to someone who can act on it.
Experian serves as the neutral infrastructure layer for commerce media networks, connecting first-party data, identity, activation, and measurement while competing for none of your advertiser revenue. Bring us your current stack, your match rates, and the advertiser demand you turn away today, and we’ll map which layers close the gap and what that growth is worth.
Watch the conversation
About the author
Kevin Dunn
Chief Revenue Officer, Experian
Kevin Dunn joins Experian Marketing Services with more than 20 years of leadership experience across marketing and advertising technology, most recently serving as Senior Vice President of Brands and Agencies at LiveRamp. In that role, he led growth across retail, CPG, travel, hospitality, financial services, and healthcare, overseeing new business, account expansion, and channel partnerships.
Kevin is known for building cohesive, accountable teams and leading with optimism, clarity, and a strong sense of shared purpose. His leadership philosophy centers on empowering people, driving positive outcomes for clients and fostering a culture where teams can grow, take smart risks, and succeed together.
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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