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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Commerce media networks have had a strong start. Growth has been fast, demand has been strong, and brands have made it clear they want closer access to commerce-driven audiences. But as more networks mature and enter the space, many are starting to feel the same pressure point: scale. Most commerce media networks were built as managed service businesses. That model works well early on. High-touch, white-glove partnerships make sense when you’re working with a handful of strategic brands. But there’s a ceiling. There are only so many teams, only so much inventory, and only so many advertisers that model can realistically support. It’s one thing for a large retailer to build custom programs for a P&G. It’s another to do that at scale for hundreds or thousands of brands. At some point, growth slows, not because demand disappears, but because the model can’t stretch any further. The scale problem no one likes to talk about That’s where many commerce media leaders find themselves today. Pausing to assess what comes next. For a long time, growth has been measured almost entirely through media dollars. That mindset is understandable. Media is familiar, it's easy to quantify. It shows up clearly in negotiations and revenue reports. But viewing commerce media networks purely as media sales engines creates long-term risk. It can strain brand relationships, limit innovation, and distract from what commerce media networks actually do better than almost anyone else: understand consumers deeply. Signals are the real asset Commerce platforms sit close to decision-making. They see what people search for, what they consider, what they buy, and when those behaviors change. Those signals are incredibly powerful. And yet, most networks only activate them inside their own walled environments. That’s a missed opportunity. Curation represents the next area of growth for commerce media networks, and it doesn’t require replacing or diminishing existing media revenue. In fact, it complements it. No single commerce media network has all the data needed to give advertisers the scale and reach they're looking for. And no advertiser wants to recreate the same audience in dozens of disconnected platforms. That friction creates inefficiency and slows decision-making. Why collaboration supports sustainable growth The opportunity is to look beyond first-party data alone and start thinking about collaboration. Second-party data. Data partnerships. Signal sharing done responsibly and transparently. Imagine an advertiser defining an audience once and being able to understand and reach that audience across multiple commerce environments. Not through a series of disconnected buys, but through a more consistent approach built on shared understanding leading to increased reach and more impactful campaigns. That’s easier for advertisers to manage, and it creates an additional revenue stream for commerce media networks that complements media sales rather than competing with them. Curation strengthens media, it doesn't replace it Media will always play an important role. There is clear value in custom experiences tied directly to a commerce environment. Think buyouts, sponsored experiences, custom creative integrations. Those are situations where brands want to work closely with the network itself. But the signals commerce media networks hold don’t need to be limited to those moments. Those signals can be monetized independently through data products, co-ops, and partnerships that extend their value into other channels. That’s how curation adds value without undercutting existing revenue. A practical path forward for commerce media leaders For commerce media leaders thinking about their next phase of growth, the focus should be on sustainability. Building a massive media operation takes time and investment. Data-driven revenue streams can be introduced more quickly, require fewer internal resources, and provide steadier margins. It’s a practical approach. Use signal-based revenue to fund growth. Let that revenue support investment in tooling, talent, and media innovation over time. Bootstrapping, in the truest sense. Why transparency matters early There’s also a broader responsibility here. In many advertising channels, transparency followed growth, often after pressure from the market. Commerce media networks have an opportunity to do this differently. To lead with transparency from the start. To be clear with brands and consumers about how data is used, how signals are created, and how value flows through the ecosystem. Because the reality is this: commerce media networks are holding some of the most valuable intent signals in the market today. But those signals don’t retain their value in isolation. If they aren’t enhanced, combined, and made accessible in the right ways, someone else will step in to do it. And when that happens, control shifts away from the source. The bottom line The next chapter of commerce media isn’t just about selling more media alone. It’s about recognizing the value of the signals already in hand, working together to make them more useful, and building additional revenue streams that support long-term growth. That’s how commerce media networks grow without eating their own lunch. 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. Latest posts