AdTech has never had more data, yet it has rarely been harder for brands and agencies to answer a simple question: what actually drove the result?
Clicks, conversions, and platform-reported performance have long served as proxies for success, shaping how campaigns are evaluated, budgets are allocated, and results are communicated. But they were never designed to measure business impact directly. They offer a directional view of activity rather than a definitive answer.
Clicks indicate interest, conversions indicate action, and platform-reported metrics reflect performance within a given environment. Each of these signals plays a role, but none of them, on their own, can confirm whether marketing led to a business outcome.
That limitation isn’t new, but it’s becoming more visible as signals shift and measurement becomes more fragmented. Measurement systems are under increasing strain, shaped by signal fragmentation, privacy constraints, and data environments that make it harder to connect media exposure to outcomes. In fact, 75% of marketers say their current approaches are falling short.
Performance can appear strong in one platform and materially different in another, making it harder to reconcile results across partners. Connecting campaign performance to actual business outcomes remains difficult.
As identity, data collaboration, and measurement become more strategic to marketing performance, organizations are looking for infrastructure that can connect data across partners while preserving neutrality, flexibility, and interoperability.
Why performance doesn’t always reflect impact
Even when data is available, it doesn’t always tell a complete or accurate story.
A conversion after an ad exposure may suggest a relationship, but it doesn’t establish causation. Attribution models favor what’s easiest to measure, and platform-reported metrics often reflect biases toward their own ecosystems. Over time, this creates a version of performance that can appear accurate while overstating actual impact.
Measurement should move from signals to conversions, then to verified outcomes, and ultimately to incrementality. Each step brings measurement closer to understanding true business impact. In practice, most strategies stall in the middle, treating conversions as the endpoint even though they don’t show whether marketing drove the result.
This creates a gap between what’s measured and what matters. Incrementality is gaining focus because it isolates what changed due to marketing, separating true impact from what would have happened anyway. Industry guidance increasingly reflects this shift, recognizing incrementality as a reliable way to measure causal impact in a fragmented, privacy-first ecosystem.
As AI and agentic technologies become more involved in planning, optimization, and decision-making, the quality of the underlying identity and data foundation becomes increasingly important. Reliable outcomes require trusted identity and interoperable data.
The infrastructure shift: Why CAPI matters now
Measurement is evolving at both a conceptual and technical level.
As browser-based tracking becomes less reliable, the industry is shifting toward server-side approaches, including conversion APIs (CAPI). These approaches create a more direct, durable connection between advertiser data and platform systems, reducing reliance on signals limited by browsers and privacy controls.
Platforms are reinforcing this shift. Meta positions CAPI as a way to improve data quality, measurement accuracy, and optimization by enabling more complete event capture. Google similarly emphasizes server-side tagging to improve data control, resilience, and performance in modern measurement environments.
On their own, these approaches don’t solve the measurement challenge. Combined with identity, they create a stronger foundation for connecting marketing activity to real outcomes.
Stronger data collection infrastructure is most effective when paired with interoperable identity and privacy-first governance, giving marketers greater confidence in how data is connected, activated, and measured across environments.
Identity as the connective layer
Identity resolution is a key enabler of that foundation. By connecting identifiers across platforms, devices, and environments, it helps marketers tie exposure to consumers and, ultimately, to real-world outcomes. Without it, measurement stays siloed across platforms and channels. With it, marketers can see how activity across environments contributes to a single outcome.
Interoperable identity is becoming more than a marketing capability. It increasingly serves as a foundational layer that helps brands, agencies, publishers, platforms, and partners collaborate across a growing number of data and media environments.
Industry efforts around data clean rooms, interoperability, and privacy-safe collaboration all address the same challenge: how to connect data across environments without relying on outdated or fragile signals. Solutions that strengthen identity resolution within these environments improve match rates between partners, making collaboration more effective and measurement more complete.
As collaboration expands across clean rooms, platforms, and activation channels, marketers benefit from identity frameworks that support interoperability rather than limiting how data can move across the broader ecosystem.
What brands and agencies should expect next
For brands and agencies, the focus is shifting from what appears to perform within a platform and toward what drives results. That requires looking beyond platform-reported metrics, asking more of measurement partners, and incorporating incrementality into how success is defined.
It also requires investment in identity and measurement that enable outcome-based measurement. Without that foundation, even advanced reporting will struggle to provide a clear view of performance.
That foundation should include trusted consumer data, transparent governance practices, and identity capabilities that can adapt as technology, privacy expectations, and AI-driven workflows continue to change.
Many organizations are also evaluating how measurement, identity, and activation strategies can maintain long-term flexibility across agencies, platforms, publishers, commerce media networks, and emerging channels.
What this shift means for AdTech
Reporting within platforms or optimizing intermediary metrics is no longer enough. Success increasingly depends on demonstrating how marketing activity translates into business results across channels and environments.
As marketing systems become more automated, brands need visibility into the data and identity layers informing those decisions, along with confidence that those systems are operating on accurate, privacy-safe consumer information.
That shift requires interoperable identity, cross-platform measurement, and infrastructure that supports more complete and reliable data collection. It also requires validating whether marketing drove incremental business impact, rather than simply reporting observed conversions.
Independent identity and neutral data infrastructure can help support that effort by giving organizations the flexibility to work across partners, platforms, and channels while maintaining consistency in measurement and audience understanding.
This means building systems that connect exposure to outcomes, measure incremental impact, and link media investment and business results. Clicks and conversions remain useful, but their limitations are becoming more visible as reliability declines.
Trusted identity, privacy-safe data collaboration, and transparent measurement are becoming central to how marketers build durable strategies that can adapt as the ecosystem continues to change.
Measurement will be defined by the ability to connect marketing activity to verifiable outcomes, with incrementality at the center of understanding true impact.
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About the author
Ali Mack
VP, AdTech Sales
Ali Mack leads Experian’s AdTech business, overseeing global revenue across the company’s expansive tech and media portfolio. With over a decade of experience in digital and TV advertising, Ali drives strategic growth by aligning sales, customer success, and solutions teams to deliver impactful outcomes for clients and partners.
She has successfully guided teams through two major acquisitions, integrating sales organizations and product portfolios into unified go-to-market strategies. Under her leadership, Experian has consistently exceeded revenue targets while fostering collaborative, results-driven teams and mentoring emerging leaders. Working closely with finance, product, and marketing, Ali develops strategies that support a diverse ecosystem of publishers, brands, and technology partners, positioning Experian at the forefront of data-driven advertising and identity resolution.
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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