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.
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
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We’re excited to introduce our new Q&A series, Ask the Expert! Ask the Expert will feature a series of conversations with product experts. We’ll spotlight and dive into the areas you care most about: identity resolution, targeting, attribution, and more. Our first segment features a conversation on Hashed Email. Jeff Tognetti, the Product Development Team Lead at DealerX joins us to chat with Experian’s Chief Revenue Officer, Chris Feo. Chris and Jeff review how to future-proof your identity strategy by exploring Hashed Email use cases, technical details, and offer an expert point of view on the cookieless future. Let’s review a few highlights from their conversation. DealerX’s use case When DealerX first started working with us, we focused on digital identity. DealerX wanted to understand the browsing habits of their first-party shoppers that relate to their clients: What they’re doing How they’re interacting with client sites and products Apply those learnings to target them across the web Eliminate ad fraud and targeting waste Our partnership gave DealerX the ability to take an anonymous consumer from anywhere across their portfolio of customers and understand who they are, while in an anonymous state. Then, they could activate on any channel where that consumer may be in the market for a product. This allowed DealerX to resolve who these people are as they browse the web, leading to reduced ad spend and targeting waste. This was the original and primary use case for DealerX when partnering with us. So, when did Hashed Email come into the mix for DealerX? Before we dive into the specifics, let’s take a step back and understand Hashed Email. What is Hashed Email? Hashed Email is a privacy-safe identifier that can further enrich the connection between the online (digital) and offline (real world) ecosystems. When paired with the Tapad Graph with access to Tapad’s universe of email data, it can provide maximum coverage for targeting and measurement when combined with IDs such as cookies, mobile ad IDs (MAIDs), connected TV (CTV) IDs, and IP addresses. Email hashing uses a method of coding to transform an email address into a jumble of numbers and letters so that it’s fully pseudonymized and privacy safe. Hashed emails can then be used as a digital identifier when a user is logged in to that email and trace their activity – without linking back to the user’s real email address. This allows marketers to collect data on their users and understand their behavior without knowing their email address – a win for both consumer privacy and marketer insight. 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Get in touch About DealerXIn just a few short years, DealerX has grown to serve 1,000’s of Tier 3 dealerships across all brands, enterprise partners and OEMs. Their keen approach to data, analytics, machine learning and programmatic initiatives have led DealerX to quickly become the most savvy player in the automotive space. DealerX has helped automotive retailers save 10’s of millions of dollars by avoiding fraud and eliminating wasteful ads pends, while dramatically reducing “cost per sale." In doing so, their partners significantly outperform those leveraging generic “one size fits all” competitive offerings. To learn more, visit their website at Dealerx.com.