Originally published in Adweek
Key takeaways
- 56% of U.S. media buyers plan to increase commerce media spend this year, but 55% cite missing cross-network measurement standards.
- New-to-brand definitions, lookback windows, and attribution rules differ by network, so results across networks can’t be compared directly.
- Off-site commerce media is growing twice as fast as on-site, carrying purchase signal away from the retail environment that created it.
- A shared identity layer lets brands recognize the same consumer across every network, and Experian’s independence from demand-side platforms (DSPs) and supply paths keeps that layer neutral.
Commerce media keeps expanding, and brands are running campaigns across more networks than ever. Most still can’t say which network performed better.
More media networks haven’t produced a more complete view
Investment keeps climbing, and 56% of U.S. media buyers plan to increase commerce media network spending this year, often by reallocating from social, display, and search budgets. Nearly half of advertisers, 47.5%, now fund those campaigns from two or three separate budgets.
Every network in that mix brings its own identity system, audience definitions, and measurement approach. Brands add the fourth or fifth network expecting a fuller picture of performance. Instead, they get more numbers that no one designed to sit next to each other.
I don’t think commerce media has a data problem. Every network has audience data and purchase data. Consistency across them is what nobody has built yet.
The inconsistency lives in the definitions
Take new-to-brand, the metric meant to separate new customers from repeat buyers. It means different things at different networks. Lookback windows differ. Some definitions apply at the brand level, others at the product line. Some count in-store purchases, others count only digital. View-through credit varies across display, video, and connected TV (CTV). Attribution windows vary by partner and by format.
Audience portability compounds it. Retailer audiences often can’t move across platforms, so teams rebuild the same strategy in several systems and hope the definitions roughly align.
Over half of advertisers, 55%, name the lack of cross-network measurement standards as a top challenge. A campaign can look stronger on one network than another for no reason other than a different reporting rule. You can’t move budget on numbers that don’t compare or show incremental lift, and yet most teams are trying to.
Off-site growth makes this harder to ignore
On-site inventory is approaching its ceiling. Sponsored results now appear on 99% of product search pages at Amazon and Walmart, and ad density has reached as high as 50% on key retail search pages. Off-site spending is growing at twice the rate of on-site.
Off-site carries commerce signal away from the environment that created it. Every handoff is a point where the audience definition, the match rate, and the measurement logic have to hold. These are solvable problems, and they show up in the same form at every handoff, which makes them worth solving once instead of campaign by campaign. Fragmentation that stayed manageable inside a retailer’s own properties becomes something to design for the moment the signal travels.
Interoperability starts with a shared identity layer
Brands and networks need a common way to recognize the same consumer wherever a campaign runs. That layer has to draw on retailer, brand, and campaign signals together, and it needs consistent definitions from one network to the next. Get that right and performance sits on common ground, even with every network’s particular reporting rules.
That layer matters more as AI takes over more of the optimization. Models inherit whatever inconsistency sits in the data beneath them, so audience definitions that shift from one network to the next degrade every decision that follows. Better models won’t fix a fragmented identity layer, but they will scale its errors faster. Networks can get ahead of this by settling the identity layer first, standardizing audience definitions so they hold on-site and off-site and give the models downstream something dependable to learn from.
Signal expansion belongs in the same conversation. First-party data reflects the customers who logged in, joined the loyalty program, or completed a transaction. Irregular buyers, emerging segments, and high-value prospects who are actively in-market show up faintly or not at all in that view, even though signal about them exists elsewhere in the ecosystem. A shared identity layer that connects to a marketplace of data providers closes part of that gap, and it does so without asking any network to hand over raw records.
This is also what opens commerce media to brands that will never own a media network. Most companies sitting on valuable first-party data don’t have the traffic or the assortment to build what Amazon and Walmart built, and they don’t need to. Combining signal with trusted partners produces audiences larger and more valuable than any one of those brands holds alone. That’s how a brand participates in commerce media without becoming a media company.
Networks need infrastructure, and neutrality decides whose
A mid-sized commerce media network runs into the same ceiling from the other direction. Owned inventory has a limit, and so does owned data. Growing past both means off-site reach, enriched audiences, and measurement that holds up next to the largest players, and building all of that in-house isn’t realistic for most networks. What they need is infrastructure that supports expansion without giving up control of the customer relationship.
That makes the choice of whose infrastructure a consequential one. Brands and networks should look hard at the commercial position of any partner they put at the center of identity and measurement. A partner with interests in a specific demand-side platform (DSP), supply path, or set of agency relationships gains visibility into every deal that moves through it, and that visibility shapes incentives over time. Networks are right to weigh it carefully.
Experian competes in none of those layers. Our position strengthens when the ecosystem stays open and brands keep the freedom to work across partners, platforms, and evolving technologies. Independence is what makes shared infrastructure possible.
Consistency is the constraint on the next round of growth
Commerce media maturity trails its ambition. 42% of teams believe they have operationalized commerce media, while only 13% qualify as advanced against leadership, technology, measurement, and operations.
Closing that gap depends on identity discipline more than network count. A brand that recognizes the same consumer across every network it buys can compare results, move budget with confidence, and hold every partner to one standard. Without that, each new network adds spend and subtracts clarity.
Consistency is what turns commerce media from a set of separate programs into something a brand can actually manage.
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
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