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.
Latest posts
Originally appeared on MarTech Series Marketing’s understanding of identity has evolved rapidly over the past decade, much like the shifting media landscape itself. From the early days of basic direct mail targeting to today's complex omnichannel environment, identity has become both more powerful and more fragmented. Each era has brought new tools, challenges, and opportunities, shaping how brands interact with their customers. We’ve moved from traditional media like mail, newspapers, and linear/network TV, to cable TV, the internet, mobile devices, and apps. Now, multiple streaming platforms dominate, creating a far more complex media landscape. As a result, understanding the customer journey and reaching consumers across these various touchpoints has become increasingly difficult. Managing frequency and ensuring effective communication across channels is now more challenging than ever. This development has led to a fragmented view of the consumer, making it harder for marketers to ensure that they are reaching the right audience at the right time while also avoiding oversaturation. Marketers must now navigate a fragmented customer journey across multiple channels, each with its own identity signals, to stitch together a cohesive view of the customer. Let’s break down this evolution, era by era, to understand how identity has progressed—and where it’s headed. 2010-2015: The rise of digital identity – Cookies and MAIDs Between 2010 and 2015, the digital era fundamentally changed how marketers approached identity. Mobile usage surged during this time, and programmatic advertising emerged as the dominant method for reaching consumers across the internet. The introduction of cookies and mobile advertising IDs (MAIDs) became the foundation for tracking users across the web and mobile apps. With these identifiers, marketers gained new capabilities to deliver targeted, personalized messages and drive efficiency through programmatic advertising. This era gave birth to powerful tools for targeting. Marketers could now follow users’ digital footprints, regardless of whether they were browsing on desktop or mobile. This leap in precision allowed brands to optimize spend and performance at scale, but it came with its limitations. Identity was still tied to specific browsers or devices, leaving gaps when users switched platforms. The fragmentation across different devices and the reliance on cookies and MAIDs meant that a seamless, unified view of the customer was still out of reach. 2015-2020: The age of walled gardens From 2015 to 2020, the identity landscape grew more complex with the rise of walled gardens. Platforms like Facebook, Google, and Amazon created closed ecosystems of first-party data, offering rich, self-declared insights about consumers. These platforms built massive advertising businesses on the strength of their user data, giving marketers unprecedented targeting precision within their environments. However, the rise of walled gardens also marked the start of new challenges. While these platforms provided detailed identity solutions within their walls, they didn’t communicate with one another. Marketers could target users with pinpoint accuracy inside Facebook or Google, but they couldn’t connect those identities across different ecosystems. This siloed approach to identity left marketers with an incomplete picture of the customer journey, and brands struggled to piece together a cohesive understanding of their audience across platforms. The promise of detailed targeting was tempered by the fragmentation of the landscape. Marketers were dealing with disparate identity solutions, making it difficult to track users as they moved between these closed environments and the open web. 2020-2025: The multi-ID landscape – CTV, retail media, signal loss, and privacy By 2020, the identity landscape had splintered further, with the rise of connected TV (CTV) and retail media adding even more complexity to the mix. Consumers now engaged with brands across an increasing number of channels—CTV, mobile, desktop, and even in-store—and each of these channels had its own identifiers and systems for tracking. Simultaneously, privacy regulations are tightening the rules around data collection and usage. This, coupled with the planned deprecation of third-party cookies and MAIDs has thrown marketers into a state of flux. The tools they had relied on for years were disappearing, and new solutions had yet to fully emerge. The multi-ID landscape was born, where brands had to navigate multiple identity systems across different platforms, devices, and environments. Retail media networks became another significant player in the identity game. As large retailers like Amazon and Walmart built their own advertising ecosystems, they added yet another layer of first-party data to the mix. While these platforms offer robust insights into consumer behavior, they also operate within their own walled gardens, further fragmenting the identity landscape. With cookies and MAIDs being phased out, the industry began to experiment with alternatives like first-party data, contextual targeting, and new universal identity solutions. The challenge and opportunity for marketers lies in unifying these fragmented identity signals to create a consistent and actionable view of the customer. 2025: The omnichannel imperative Looking ahead to 2025 and beyond, the identity landscape will continue to evolve, but the focus remains the same: activating and measuring across an increasingly fragmented and complex media environment. Consumers now expect seamless, personalized experiences across every channel—from CTV to digital to mobile—and marketers need to keep up. The future of identity lies in interoperability, scale, and availability. Marketers need solutions that can connect the dots across different platforms and devices, allowing them to follow their customers through every stage of the journey. Identity must be actionable in real-time, allowing for personalization and relevance across every touchpoint, so that media can be measurable and attributable. Brands that succeed in 2025 and beyond will be those that invest in scalable, omnichannel identity solutions. They’ll need to embrace privacy-friendly approaches like first-party data, while also ensuring their systems can adapt to an ever-changing landscape. Adapting to the future of identity The evolution of identity has been marked by increasing complexity, but also by growing opportunity. As marketers adapt to a world without third-party cookies and MAIDs, the need for unified identity solutions has never been more urgent. Brands that can navigate the multi-ID landscape will unlock new levels of efficiency and personalization, while those that fail to adapt risk falling behind. The path forward is clear: invest in identity solutions that bridge the gaps between devices, platforms, and channels, providing a full view of the customer. The future of marketing belongs to those who can manage identity in a fragmented world—and those who can’t will struggle to stay relevant. 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