At A Glance
Your audience strategy works like a story. First-party data sets the outline, but scale and relevance come from connecting additional signals such as contextual, geographic, and behavioral data. Experian helps CMOs unify these inputs through identity, enabling consistent activation, privacy-forward targeting, and measurable outcomes as marketing strategies evolve in 2026.How should CMOs think about data as part of their audience strategy?
The best digital marketers possess excellent storytelling capabilities—and they fuel the plot with data.
When you think about it, your audience strategy is the whole story, and the type of data you use helps create each chapter. Just as any good book incorporates numerous literary devices, you must use more than one type of data to develop a dynamic, relevant, and timely narrative that captures your target users’ attention.
In 2026, marketers should prioritize and invest in data and targeting strategies beyond just first-party to drive growth, improve efficiency, and strengthen customer relationships.
2026 Digital trends and predictions report
Our 2026 Digital trends and predictions report is available now and reveals five trends that will define 2026. From curation becoming the standard in programmatic to AI moving from hype to implementation, each trend reflects a shift toward more connected, data-driven marketing. The interplay between them will define how marketers will lead in 2026.
Why is first-party data not sufficient on its own?
First-party data provides a strong foundation for targeting and measurement. It reflects information consumers have shared directly through brand interactions. That makes it reliable and central to audience strategy.
That foundation alone does not tell the full story. First-party data defines known customers, but limits reach and frequency. Growth depends on expanding beyond existing relationships.
Think of first-party data as a way to create an outline, not the whole story, about your target audiences—the main characters in your marketing. To flesh out the entire narrative about them, you must source, connect, and activate additional data.
The ability to unify different data sources with accuracy, scale, and privacy at the forefront sits at the core of Experian’s business. We unify household, individual, device, demographic, behavioral, and first-party signals, along with contextual and geographic data points, to build a reliable view of consumers, even when specific signals are missing. This clarity helps you personalize, target, activate, and measure with confidence.
By layering third-party data, contextual data, and geolocation data onto your first-party data foundation, your advertising strategies become stronger than if you used any of these sources as standalone solutions.
How do different types of third-party data add depth to audience profiles?
Third-party data expands understanding beyond known customers. If first-party data is the outline, third-party data helps with “character development”—a.k.a., addingdetailto your audience profiles. Good third-party marketing data complements first-party insights with demographic, behavioral, and transactional context, providing the missing puzzle pieces to complete the full customer profile. Filling in gaps in customer understanding helps youidentify, reach, and engage current and new customers more effectively. Third-party data allows brands to build loyalty with consumers by speaking to their interests and intent behind purchases.
Third-party data opens up new targeting tactics for advertisers, such as:
In addition to targeting, third-party data also remains critical to AI models, which must train on both structured and unstructured data. At Experian, our AI-powered technology interprets live bidstream data, device activity, content, and timing to optimize in the moment, ensuring campaigns deliver meaningful relevance, not just broader reach.
How are contextual and geographic approaches reshaping audience targeting?
Contextual and geographic approaches to targeting focus on environment and behavior rather than identifiers. Regulatory scrutiny, stricter and more fragmented compliance standards, and rising consumer expectations are transforming how marketers approach third-party data targeting. Evolving privacy laws and inconsistent identifiers across environments require new approaches that balance performance and privacy.
Contextual and geographic targeting help marketers reach relevant audiences while maintaining privacy.
What is data-informed contextual targeting?
Contextual targeting connects audience attributes to the content environments people choose. It helps determine the setting of your story—where your characters spend their time.
Solutions like Experian’s Contextually-Indexed Audiences harness advanced machine learning technology to combine contextual signals (a tried–and-true targeting tactic) with third-party targeting to ensure marketers reach their target audiences on the content they tend to consume, regardless of environment or location. What’s excellent about data-informed contextual targeting is that it moves beyond traditional keyword-based strategies to reach consumers on websites that over-index for visitors with the demographics, behaviors, or interests they are looking to target.
What is data-informed geotargeting?
Geotargeting uses shared location patterns to support relevance at scale. Geotargeting is another possibility for further developing the scene of your story.
People with similar behaviors and interests tend to live in similar areas, which is why so much effort goes into location planning for brick-and-mortar stores. Data-informed geotargeting combines geos with third-party data to make more informed media buys based on common behaviors within a geographic location.
We launched our Geo-Indexed audiences, which use advanced indexing technology to identify and reach consumers based on their geographic attributes. These audiences help marketers discover, segment, and craft messaging for consumers without relying on sensitive personal information, enabling them to reach target audiences while maintaining data privacy confidently.
What role does AI play in third-party data targeting?
AI acts like an automated editor of your book, refining and finding new ways to put valuable third-party audiences and data to work without relying on segments linked to known or disparate identifiers.
We’ve used AI and machine learning at Experian for decades to bring identity, insight, and generative intelligence together so brands and agencies can reach the right people, with relevance, respect, and simplicity.
Why does a balanced, integrated approach that combines first-party, third-party, contextual, and geo-targeting data matter?
The combined effects of integrating third-party, contextual, and geotargeting data (and the marketing tactics it underpins) with first-party data will drive your success.
Think of how any good author crafts a story. Regardless of whether it’s fiction or non-fiction, they draw on both first-person experience and external research and sources to develop their plot. No single data source tells the full story. Integration allows marketers to understand audiences more completely and act with confidence.
Pooling these inputs together moves you closer to your goal of understanding the whole story about your target customers. In fact, an almost even number of marketers plan to use contextual targeting (41%) and first-party data (40%) as their main targeting strategies, amid privacy laws and the loss of persistent advertisers.
| Primary data strategy | Percent of marketers that plan to use this data strategy |
| Contextual targeting | 41% |
| First-party data | 40% |
A brand with strong first-party insights can extend reach by layering in additional signals. For example, a nutrition brand that knows who purchases protein supplements can expand prospecting by combining:
By connecting these inputs, the brand can identify new health-conscious audiences with similar interests and behaviors. This approach supports privacy-safe targeting while improving engagement and performance.
How can marketers build an integrated data strategy in 2026?
An integrated data strategy reduces friction and supports scale. The right data partner offers a unified solution that helps unify data, activate audiences, and adapt as the ecosystem evolves. Here’s how:
Marketers who want to create and activate campaigns more efficiently and effectively in 2026 need an integrated approach that combines first-party, third-party, contextual, and geotargeting data. Streamlining data integration and activation positions brands and agencies for sustainable growth and stronger consumer relationships in a privacy-conscious marketplace.
Build your next chapter on a connected data foundation
As audience strategies evolve, connection and interoperability matter more than ever. Connect with our team to learn how Experian helps marketers unify data, identity, and activation across channels.
About the author
Scott Kozub
VP, Product Management, Experian
Scott Kozub is the Vice President of the Product Management team at Experian Marketing Services working across the entire product portfolio. He has over 20 years of product experience in the marketing and advertising space. He’s been with a few startups and spent many years at FICO and Oracle Data Cloud heavily focused on loyalty marketing and advertising technology.
FAQs
In 2026, CMOs should prioritize and invest in data and targeting strategies that combine first-party, third-party, contextual, and geographic data to drive growth, improve efficiency, and strengthen customer relationships.
First-party data is not sufficient on its own because first-party data defines known customers but limits reach and frequency. Growth depends on expanding beyond existing relationships. The ability to unify different data sources with accuracy, scale, and privacy at the forefront sits at the core of Experian’s business. We unify household, individual, device, demographic, behavioral, and first-party signals, along with contextual and geographic data points, to build a reliable view of consumers, even when specific signals are missing. This clarity helps you personalize, target, activate, and measure with confidence.
Third-party data expands understanding beyond known customers. Third-party data opens up new targeting tactics for advertisers, such as:
– Location: Where people live, work, or spend large amounts of time
– Health: A combination of demographics, behaviors, and health needs
– Purchases: Using previous purchase behavior to identify the right audiences
– Behavioral: How people engage with brands or how they use social media
– Interest: Delivering ads based on interests, hobbies, or online activities
– Psychographics: Shared characteristics like attitudes, lifestyles, and interests
– Demographic: Age, gender, education, income, and religion
In addition to targeting, third-party data also remains critical to AI models, which must train on both structured and unstructured data. At Experian, our AI-powered technology interprets live bidstream data, device activity, content, and timing to optimize in the moment, ensuring campaigns deliver meaningful relevance, not just broader reach.
Data-informed contextual targeting connects audience attributes to the content environments people choose. It helps determine the setting of your story—where your characters spend their time. Experian’s Contextually-Indexed Audiences harness advanced machine learning technology to combine contextual signals (a tried–and-true targeting tactic) with third-party targeting to ensure marketers reach their target audiences on the content they tend to consume, regardless of environment or location.
Data-informed geotargeting uses shared location patterns to support relevance at scale. Experian launched our Geo-Indexed audiences, which use advanced indexing technology to identify and reach consumers based on their geographic attributes. These audiences help marketers discover, segment, and craft messaging for consumers without relying on sensitive personal information, enabling them to reach target audiences while maintaining data privacy confidently.
In third-party data targeting, AI refines and finds new ways to put valuable third-party audiences and data to work without relying on segments linked to known or disparate identifiers. We’ve used AI and machine learning at Experian for decades to bring identity, insight, and generative intelligence together so brands and agencies can reach the right people, with relevance, respect, and simplicity.
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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