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.
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Consumers engage with content and advertisements across various devices and platforms, making an identity framework essential for establishing effective connections. An identity framework allows businesses to identify consumers across multiple touchpoints, including the relationships among households, individuals, and their devices. Combined with a robust data framework, businesses can understand the relationship between households, individuals, and marketing attributes. Consequently, businesses can tailor and deliver personalized experiences based on individual preferences, ensuring seamless consumer interactions across their devices. We spoke with industry leaders from Audigent, Choreograph, Goodway Group, MiQ, Snowflake, and others to gather insights on how innovations in data and identity are creating stronger consumer connections. Here are five key considerations for advertisers. 1. Embrace a multi-ID strategy Relying on a single identity solution limits reach and adaptability. Recent data shows that both marketers and agencies are adopting multiple identity solutions. By embracing a multi-ID strategy with solutions like Unified I.D. 2.0 (UID2) and ID5, brands can build a resilient audience targeting and measurement foundation, ensuring campaigns remain effective as identity options evolve across channels. A diversified identity approach ensures that advertisers are not left vulnerable to shifts in technology or policy. By utilizing multiple ID solutions, brands can maintain consistent reach and engagement across various platforms and devices, maximizing their campaign effectiveness. "I don't think it will ever be about finding that one winner…it's going to be about finding the strengths and weaknesses and what solutions drive the best results for us."Stephani Estes, GroupM 2. Utilize AI and machine learning to enhance identity graphs Identity graphs help marketers understand the connections between households, individuals, their identifiers, and devices. This understanding of customer identity ensures accurate targeting and measurement over time. AI and machine learning have become essential in making accurate inferences from less precise signals. These technologies strengthen the accuracy of probabilistic matches, allowing brands to understand consumer behavior more effectively even when data fidelity is lower. Adopting a signal-agnostic approach and utilizing various ID providers enhances the ability to view consumers' movements across platforms. This strategy moves measurement beyond isolated channels, providing a holistic understanding of campaign effectiveness and how different formats contribute to overall performance. By integrating AI and machine learning into identity graphs, advertisers can develop more cohesive and effective marketing strategies that guide customers seamlessly through their buying journey. "What we're finding is more and more identity providers are using Gen AI to locate connections of devices to an individual or household that maybe an identity graph would not identify."David Wells, Snowflake 3. Balance privacy with precision using AI AI-driven probabilistic targeting and identity mapping provide effective solutions for privacy-focused advertising. Rather than relying on extensive personal data like cookies, AI can use limited, non-specific information to predict audience preferences accurately. This approach allows advertisers to reach their target audience while respecting privacy—a crucial balance as the industry shifts away from traditional tracking methods. According to eMarketer, generative AI can further enhance audience segmentation through clustering algorithms and natural language processing. These tools enable more granular, privacy-compliant targeting, offering advertisers a pathway to reach audiences effectively without needing third-party cookies. "I think the biggest opportunity for machine learning and AI is increasing the strength and accuracy of probabilistic matches. This allows us to preserve privacy by building models based on the features and patterns of the consumers we do know, instead of transmitting data across the ecosystem."Brian DeCicco, Choreograph 4. Activate real-time data for better engagement Real-time data enrichment introduces dynamic audience insights into the bidding process, enabling advertisers to respond instantly to user actions and preferences. This agility empowers marketers to craft more relevant and impactful moments within each campaign. "Real-time data enrichment–where data companies can have a real-time conversation with the bid stream–is an exciting part of the future, and I believe it will open the door to activating a wide variety of data sets."Drew Stein, Audigent 5. Create and deploy dynamic personas using AI Generative AI transforms persona-building by providing advertisers with richer audience profiles for more precise targeting. This approach moves beyond traditional demographic categories, allowing for messaging that connects more meaningfully with each consumer. By using generative AI to craft detailed personas, advertisers can move beyond generic messaging to create content that truly resonates on an individual level. This personalized approach captures attention and strengthens consumer relationships by addressing their specific needs and interests. "One cool thing we've built recently is a Gen AI-based personas product that generates personas to create highly sophisticated targeting tactics for campaigns."Georgiana Haig, MiQ Seize the future of data-driven engagement Focusing on these five key innovations in data and identity allows you to adapt to the evolving media landscape and deliver personalized experiences to your audience. 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Originally appeared on Total Retail Retail media networks (RMNs) continue to demonstrate how they can be a powerful monetization driver for retailers, creating a win-win-win for everyone involved. Retailers can monetize their valuable first-party data as well as their online and in-store inventory, while customers benefit from timely, relevant content that enhances their shopping experience. At the same time, advertisers can reach highly targeted audiences at critical moments near the point of purchase Achieving this type of success requires overcoming challenges around fragmented and incomplete first-party data, which can limit a retailer's ability to organize and use their data effectively. Additionally, many RMNs lack the analytical capacity to generate customer insights, build addressable audiences, and accurately measure success. To realize the full potential of their platforms, RMNs need partners that provide complementary data, strong identity solutions, and the expertise to transform insights into actionable strategies. This allows RMNs to drive winning outcomes for themselves, marketers, and their customers. Here are the five steps an RMN should consider when selecting the right partner. 1. Build an identity foundation First, the right partner needs to be able to organize and clean customer data. Given the millions of customer records and data points that a retailer has, RMNs need to make sure their data is highly usable. Whether it is a known customer record or an unknown customer with incomplete data, partners should fill in missing information and connect fragmented customer records to a single profile. For example, RMNs need to know that a purchase made in-store is by the same customer who bought online. The best partners will then organize those profiles into households since targeting (and purchasing) is often done at the household level. Without a strong identity foundation future steps of segmentation, insights, audience creation, and activation will not be successful. Experian identity Experian's identity solutions provide RMNs with a comprehensive and accurate view of their customers across both offline and digital environments. We clean an RMN's first-party data and organize their customer records into households since targeting is often done at the household level and purchases are made at the household level. Using Experian's Offline and Digital Graphs we work with the RMN to fill in the missing information they have on their customers (e.g. name, address, phone number or digital IDs like hashed emails, mobile ad IDs, CTV IDs, Universal IDs like UID2 or ID5 IDs). This ensures that the retailers' entire customer base can be reached – and measured – across devices and channels. 2. Segment your customers An RMN’s ability to segment its customer base and derive insights depends on the availability and usability of their data assets – not to mention some serious analytical chops. Some RMNs will split their customers into different product segments based on what’s relevant to an advertiser. For example, a home improvement retailer may segment customers by who is buying DIY supplies versus improvement services. Other RMNs may develop custom segments from their customer data and third-party data sources, so that advertisers can personalize their marketing based on life stage, age, income level, geography, and other factors. Either approach is effective but requires working with a partner who has high quality data and deep analytical expertise to develop those segments. Segment with Experian Experian Marketing Data helps an RMN learn about their customer beyond their first-party data. With access to 5,000 marketing attributes, RMNs can fill in the holes in their understanding of a customer. We provide them with demographic, geographic, finance, home purchase, interests and behaviors, lifestyle, auto data and more. RMNs can use this enriched data set to create addressable audience segments. 3. Generate actionable insights about these segments Once the RMN determines how they will segment their customers, they can utilize demographic, attitudinal, interest, and behavioral data from a trusted partner to develop a customer profile that compares its customers against a relevant sample of consumers. Here, the RMN will gain insight that will help them answer questions about its customers. Examples include: What age and income groups are more likely to purchase my product? What is the current life stage of my customers – do they have children, are they married, are they empty-nesters? Is price or quality more important to customers in their decision-making process? What sort of activities do my customers enjoy? How frequently do my customers shop for similar merchandise? What media channels do my customers use to get their information? Expanded insights with Experian With Experian’s advanced customer profiling, RMNs can go beyond basic customer segmentation. We build detailed customer profiles by utilizing accurate, attribute-rich consumer data, so RMNs can gain a more comprehensive understanding of their customer’s preferences, life stages, and purchasing behaviors. Having this insight enables the RMN to: Design a targeted email campaign promoting home essentials to recently married new homeowners. Develop a social media post announcing the opening of a new hardware store to users within a specific location interested in do-it-yourself products. Create brochures and flyers at a local community event tailored towards parents with small children that promote equipment for youth sports leagues. 4. Create high quality lookalike audiences The RMN now knows what distinguishes their customers from other consumers and can create audiences that enable advertisers to run personalized marketing campaigns at scale. RMNs can do this in several different ways: Work with a data provider who can create custom audiences for the RMN (e.g., Ages 40-49 and Leisure Travelers and past purchase of travel item) These custom audiences are created by joining multiple first- and third-party data attributes found to be significant in the customer profile or using machine learning techniques to develop a custom audience unique to the advertiser. Custom audiences with Experian With an enriched understanding of their customers, RMNs can create addressable custom audience segments, including lookalike audiences, for advertisers. 5. Expand addressability of audiences and activate on multiple destinations Once audiences are created, RMNs will want to increase a marketer’s reach across on-site and off-site channels. With the right identity graph partner, an RMN can add digital identifiers to customer records that enable activation across media channels, including programmatic display, connected television (CTV), or social. RMNs should work with identity providers that are not reliant on third-party cookies. They should select partners that offer more stable digital IDs in their graph like mobile ad IDs (MAIDs), hashed emails (HEMs), CTV IDs, and universal IDs like Unified I.D. 2.0 (UID2). Experian powers data-driven advertising through connectivity Using Experian's Digital Graph, RMNs expand the addressability of their audiences by assigning digital identifiers to customer records. Marketers will be able to reach an RMNs customers onsite as well as offsite since Experian provides several addressable IDs. Audiences can be activated across an RMNs owned and operated platform as well as extended programmatically to TV and the open web through Experian's integrations across the ecosystem. Maximize your RMN’s revenue potential with Experian Organizing customer data, segmenting customers, generating insights, creating addressable audiences, and activating campaigns are all critical steps for an RMN to realize that revenue potential. RMNs should select a partner that provides the data, identity, and analytical resources to create the winning formula for marketers, customers, and retailers. Experian’s data and identity solutions are designed to help RMNs maximize their revenue potential. Reach out to our team to discover how we can support your path to RMN success. Connect with us Latest posts
Originally appeared on MediaPost As the digital ecosystem becomes more complex, managing multiple identifiers for consumers has emerged as a significant challenge. From cookies and IP addresses to mobile IDs and universal IDs, marketers and platforms face increasing difficulty in maintaining a unified view of their consumers. Without a coherent identity strategy, campaigns can suffer from poor targeting, limited personalization, and flawed attribution. Experian understands these challenges and offers solutions to help our partners navigate the complexities of a multi-ID landscape. By utilizing both digital and offline data, we provide the tools to unify fragmented identifiers and maintain a persistent view of consumers. As a result, marketers and platforms get rich insights, accurate cross-device targeting, improved addressability, and measurable advertising. The shifting identity landscape For years, the industry has relied on cookies to identify consumers across devices and platforms. However, with ongoing signal loss, including the uncertainty around cookies, and the evolution of privacy regulations, the digital identity landscape has grown more complicated. As consumers hop from one device to another, they are now represented by multiple signals, each tied to a different aspect of their digital behavior. While this shift brings complexity, it also opens the door for innovation. Marketers and ad platforms now have the opportunity to rethink their identity strategies and adopt more flexible approaches that are not reliant on a single identifier. This is where Experian comes in. Connecting the dots: A holistic view of the customer journey Our identity solutions are designed to help manage today’s multi-ID ecosystem by connecting digital and offline identifiers to a single customer profile. This creates a unified view of the consumer, and when combined with our understanding of customer behavior (e.g. demo, interests, shopping patterns) marketers and platforms get both insights about their customers and the addressability to reach them across channels. Four examples of what you can do with a strong identity foundation If an advertiser wants to make its first-party data more addressable, it can utilize our Digital Graph with universal IDs, hashed emails (HEMs), and connected TV (CTV) IDs to extend its reach. A publisher who wants to gain further insights into their audiences and create private marketplaces (PMPs) can achieve this goal with the use of our Digital Graph with hashed emails, universal IDs, mobile ad IDs (MAIDs), CTV IDs, and IPs. The publisher can use this in concert with Marketing Attributes to understand age, gender, household income, buying behavior, and more. The publisher can connect marketing attributes to the Digital Graph via our Living Unit ID (LUID) to understand more about consumers that fall into their segments. A demand-side platform (DSP) who wants to extend first-party and third-party audience reach across all digital devices on their platform will use the Digital Graph with all digital IDs to allow users of their platform to select cross-device extension against first-party and third-party audiences. A retail media network (RMN) can use our Offline and Digital Graphs to connect in-store and online purchases to a household profile—even when purchases are made by different people. The RMN can then reach that household across digital media platforms and accurately attribute the in-store purchase back to digital ad exposure. Identity as a strategic asset: Today and in the future In our paradoxical world where consumers are represented by multiple identifiers, yet marketers and platforms face signal loss, identity is more than a technical issue—it’s a strategic asset. The ability to unify identity data into a single profile provides marketers with the customer intelligence needed to drive growth and stay competitive. Here’s how we do it: Deep, persistent customer understanding: With roots in offline, deterministic data like names, addresses, and emails, we provide an accurate and persistent view of identity to our customers. This allows you to maintain a consistent and comprehensive understanding of your customers and their marketing attributes over time. Highly accurate and refreshed digital identities: Our signal-agnostic graph is not reliant on any one signal as it includes HEMs, cookies, MAIDs, IPs, Universal IDs, and CTV IDs. Our Digital Graph is updated weekly, ensuring the data is always fresh and addressable. This persistent linkage of individuals and households to their identifiers and devices means your campaigns are always targeting the right people. Connected offline and digital graphs for holistic insights: We connect offline and digital identities by following privacy-first best practices, such as preventing re-identification, to allow insights from the offline world to be used in the online world. This integrated approach, enriched with marketing data, gives you better insights, more addressable advertising, and the ability to engage customers across multiple devices while accurately measuring campaign impact. Transform challenges into opportunities The rise of the multi-ID landscape presents both challenges and opportunities for the advertising industry. We stand as the trusted partner to navigate this complexity, utilizing insights from the offline world to inform decisions in the online world, enabling personalized marketing and accurate attribution, and helping you achieve your current and future goals. Get started today Latest posts