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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In this article…Why AI and CTV are a great matchThe rising popularity of CTVHow is AI already being used in CTV?Demand-side platforms Artificial intelligence (AI) and connected TV (CTV) have a perfect synergy that’s revolutionizing how advertisers connect with their audiences. CTV serves as a medium for streaming content, while AI acts as a sophisticated technology that improves the performance of CTV advertising campaigns. The integration of these two technologies has paved the way for advertisers to reach their target audience more effectively, making CTV advertising a powerful and efficient tool. In this blog post, we’ll dive into how these technologies work together — and why you should jump on board with AI for CTV advertising if you haven’t already. Why AI and CTV are a great match CTV and AI are transforming how advertisers connect with their audiences and improving the performance of their advertising campaigns in the CTV space. They work together to make advertising smarter and more enjoyable for everyone involved. AI uses sophisticated computer programs to analyze and understand data, while CTV refers to the streaming services that consumers use at home. But what makes them a great match in advertising? AI uses data to determine which TV ads are most exciting and relevant to certain people, and it can even adjust ads in real time to ensure viewers are always getting the most personalized experience. AI can provide suggestions to viewers based on previously watched content to help them find what they’d enjoy watching next. To sum it up, AI allows for: Precise targeting: AI uses data to determine which TV ads are most exciting and relevant to certain people. Personalization: AI can adjust ads in real time to ensure viewers are always getting the most personalized experience. Effective ad insertion: AI can provide suggestions to viewers based on previously watched content to help them find what they’d enjoy watching next. CTV facilitates these AI-driven strategies for enhanced user engagement and satisfaction. The rising popularity of CTV CTV has become increasingly popular as people change the way they watch TV. Instead of the traditional approach, more viewers are now choosing CTV platforms for their entertainment. One of the main reasons for this shift is that CTV offers greater flexibility and lets viewers watch content at their convenience. The ability to skip ads on many CTV platforms also improves the experience. CTV offers a great opportunity to interact with your target audience in a more engaging way. CTV allows for highly targeted advertising capabilities so you can reach specific demographics and households with tailored messages. Additionally, CTV provides valuable data insights that enable you to measure campaign effectiveness accurately. If you haven’t embraced this advertising channel yet, you may be missing out on a growing and engaged audience. Here are three reasons you should add CTV to your advertising strategy. Global video ad impressions As a global platform, CTV has the unique ability to reach audiences worldwide. Unlike traditional TV, CTV transcends geographical boundaries and brings marketers a global audience, which makes it an ideal channel for global ad campaigns. No matter your target audience, they’re consuming content on CTV. In fact, a recent study showed that 51% of global video ad impressions came from CTV in 2022. This abundance of global video ad impressions generates vast amounts of data, which AI can process in real time to help you make data-driven decisions and optimize your campaigns for diverse international audiences. AI can analyze viewer data from various regions, identify audience preferences and behaviors across borders, and tailor ad content accordingly. These data analysis capabilities ensure your ads get in front of the right viewers. Viewers prefer ad-supported CTV In 2020, the viewing time of ad-supported CTV surged by 55% while subscription video on demand decreased by 30%, according to TVision Insights. Viewers have a well-established preference for ad-supported CTV due, in part, to cost-effective access to premium content. Viewers are more engaged and less resistant to ads, as AI tailors ad content to viewer preferences and behavior to enhance ad relevance. AI-powered insights can also aid in viewer retention and help you optimize your CTV campaigns. By accommodating viewers’ preference for ad-supported CTV and harnessing AI to improve the ad experience, you’re more likely to be successful in your marketing efforts. CTV outpaces mobile and desktop for digital video viewing eMarketer recently reported that U.S. adults spend 7.5+ hours each day on CTV — more than half of their digital video viewing time. Comparatively, they only spend 37.5% of their viewing time on mobile and 10% on desktops and laptops. These statistics demonstrate that CTV has become the preferred platform for digital video consumption, as viewers enjoy larger screens with superior quality for an immersive experience. It's important to note that AI is an essential CTV marketing tool, as it allows for precise targeting and content optimization. By utilizing AI on CTV, you can take advantage of this trend and deliver more engaging and effective campaigns to a growing and engaged audience. How is AI already being used in CTV? CTV has been integrated with AI across various facets and has revolutionized the television landscape. Here's a look at how AI is already shaping the CTV experience: Generative AI ads Generative AI ads are taking CTV personalization to a whole new level. These innovative ads are customized versions of the same CTV ad to suit individual viewers. Some AI tools can generate several versions of the same CTV ad — swapping the actor’s clothing and voiceover elements like store locations, local deals, promo codes, and more — and can create up to thousands of personalized iterations in just a few seconds. Such capabilities are a game-changing approach to connecting with your audience. Next, we dive into the advantages and impact of generative AI ads, and explore their transformative role in CTV advertising. Contextual ads vs personal data Generative AI ads use personal data, such as viewing history and demographics, to create highly personalized ad experiences. This sets them apart from contextual ads, which rely solely on the content being viewed. Using AI to harness this data, you can move beyond traditional contextual targeting and ensure your ads connect with viewers on a more individualized level. Generative AI ads can be used to A/B test Generative AI ads are not just about personalization; they also open the door to A/B testing. Being able to create several versions of one ad quickly allows you to experiment with various ad elements, such as messaging, visuals, and calls to action, to identify what works best for different segments of your audience and drives the best performance. This flexibility is especially valuable for refining ad campaigns and maximizing their impact. What’s next for AI-generated ads like this? The potential of AI-generated ads is exciting. As AI technologies constantly advance, we can expect even more personalized and automated CTV advertising. It’s a good idea to keep up with the latest AI-driven innovations to create more effective ad campaigns in the fast-evolving CTV space. The possibilities are endless, and you’ll likely find the most success when you embrace AI in CTV advertising. Optimize streaming quality AI helps viewers enjoy more seamless CTV experiences. By assessing network speed and user preferences, AI optimizes video quality in real time to reduce buffering interruptions. For instance, streaming platforms use AI to adjust video settings based on a user's connection speed. This guarantees an uninterrupted and enjoyable viewing experience. Review content for compliance AI also has a part to play in quality assurance and compliance management. It assesses content alignment with technical parameters and moderates compliance with local age restrictions and privacy regulations. This means AI can identify and filter out unsuitable content to provide a safer and more enjoyable viewing environment for audiences while safeguarding brands from association with undesirable material. Voice command AI-powered voice command technology is increasingly used to control CTV viewing. This technology is embedded in streaming devices and smart TVs and allows viewers to interact with their CTV content through voice-activated commands. This personalizes the viewing experience and improves convenience, as it eliminates the need for remote controls. CTV-integrated voice assistants like Google Assistant, Amazon Alexa, Apple Siri, and Samsung Bixby offer a more human-like interaction with the television, allowing users to give commands and receive tailored responses. Content recommendations AI can offer content recommendations that provide viewers a more personalized and engaging experience. Major over-the-top (OTT) services like Netflix, Hulu, and Amazon Prime use AI-driven data analysis to deliver tailored content suggestions to their audiences. By analyzing user habits in detail, AI can recommend content based on factors such as actors, genres, reviews, and countries of origin. This personalized approach helps viewers discover content that matches their preferences and enhances their viewing experience. Advertising Programmatic ad buying, driven by AI, automatically matches ad placements to specific audience segments based on behavioral patterns. It improves ad delivery by moving away from gross rating points (GRP) to more intelligent and targeted placements. This benefits marketers by ensuring ads are seen by the right people at the right time. It’s also cost-effective for publishers, as it maximizes the sale of ad spots to suitable buyers. Automatic content recognition (ACR) technology, which AI powers, is integrated into smart TVs and streaming devices to improve ad relevance. It provides contextual targeting and extends the reach of ads across multiple devices. For example, platforms like Roku use ACR data to display ads to viewers who haven’t seen them on traditional TV. Similarly, Samba TV retargets mobile users based on IP address and aligns their viewing habits with their smart TVs. Demand-side platforms CTV advertising relies heavily on demand-side platforms (DSPs) to efficiently manage and optimize ad campaigns. These platforms use machine learning and AI in several important ways: Using machine learning and AI to address data fragmentation Data is abundant but fragmented when it comes to CTV advertising. DSPs are flooded with a massive amount of data, including information about households, viewer behavior, and viewing patterns. This data is far too much for manual analysis to handle effectively, which is where AI comes in. By integrating machine learning algorithms into DSPs, AI can harmonize this fragmented data and provide valuable insights and a holistic view of your audience. AI can process zettabytes of data in real time, which streamlines the decision-making process and empowers you to compete quickly for limited CTV impression opportunities. Predicting advertising outcomes with AI AI is quickly changing the way we predict and optimize advertising outcomes. TV buying and optimization platforms are now using AI to improve ad performance. With machine learning, these platforms can anticipate which ad creatives will produce the best results based on various non-creative factors. These include the context of the ad, the audience's profiles, the time of day it is displayed, and the frequency of the ad display. By relying on AI to make these predictions, you can make sure your campaigns are highly optimized for success and deliver more relevant, compelling ads to viewers. Optimizing generative ads AI is also driving optimization in generative ads. These personalized versions of the same CTV ad can be tailored to suit individual viewers. By utilizing AI-driven analytics, DSPs can process extensive amounts of data in real time and optimize generative ads to ensure they align with viewers' preferences and behaviors. This level of personalization is a game-changer in CTV advertising that boosts engagement and delivers content that truly resonates with the audience. Add AI to your CTV strategy today Integrating AI into your CTV strategy can help you stay competitive and ensure your ad campaigns are effective and engaging. At Experian, we’re ready to help you elevate your CTV advertising and implement AI as part of your strategy. Our solutions, such as Consumer View and Consumer Sync, provide valuable audience insights, enhance targeting capabilities, and optimize engagement on TV. Plus, our partnerships with leading media marketing solutions can help you achieve greater success through effective advanced television advertising. As you incorporate AI into your CTV strategy, you’ll be able to make more data-driven decisions, deliver more relevant content, and reach the right audience at the right time. Explore Experian's TV solutions and empower your CTV advertising with AI today. Start exploring Latest posts