At A Glance
AI can make marketing more human when it understands people in context. Experian’s technology interprets real-time contextual signals—from temporal to situational intent—to align every message with the moment. By connecting identity and context, marketers can create timely, relevant, and responsible engagement that builds trust and drives meaningful outcomes.Personalization without context misses the moment
Marketers have spent years perfecting personalization — but personalization alone often misses the mark. We’ve all seen it. You shop for a weekend getaway, then get served travel ads weeks later when you’re already home. The data was right. The timing wasn’t.
Personalization based only on identity and behavior knows who you are but not when or why you’re ready to act.
At Experian, we believe AI should make marketing feel more human. That means understanding people in context, recognizing their environment, mindset, and the moment, to create relevance that feels timely, not intrusive.
The context gap: Why identity and behavior aren’t enough
Identity and behavioral data can reveal the kind of consumer someone is and the kind of products they may want to buy. But they don’t explain what’s happening right now.
The missing layer is context: the dynamic, real-time signal that shows why this moment matters. Context bridges the gap between knowing something about a consumer and understanding their intent.
In an era of fragmented signals and stricter privacy rules, context is one of the most reliable ways to stay relevant without over-reliance on personal identifiers. It helps marketers adapt to shifting needs while keeping privacy intact.
How Experian interprets context in real-time
By context, we mean all the subtle, in-the-moment signals, like time of day, location, or what someone’s watching, that shape what people care about in real-time. At Experian, our technology interprets these in real-time:
Temporal signals
Time-based patterns such as daypart (morning vs. evening), recency (how fresh a signal is), seasonality (holidays, life events), and micro-moments (split-second intent-driven actions).
Environmental signals
The media or content environment; what type of program, article, or channel someone is engaging with when they see your ad.
Situational intent
Signals like browsing behavior or purchase patterns that hint at a person’s stage in the buying journey, from early research to final decision.
By layering these signals over verified identity and behavioral data, Experian’s AI-powered technology helps marketers predict not just who will act, but when they’re ready to act.
Experian’s approach: Turning context into relevance
Consumer behavior changes by the minute, and marketers need to adapt just as quickly. Our technology interprets live bidstream data, device activity, content, and timing to optimize in the moment, ensuring your campaigns deliver meaningful relevance, not just broader reach.
Our process combines:
Input
Clean, accurate identity and audience data anchored in our privacy-first framework.
Enrichment
Our models fuse household, device, and publisher context to reveal moment-based intent.
Activation
We’re investing in agentic workflows that help marketers plan and execute performance campaigns at scale, activated via our real-time technology that dynamically adjusts deals and surfaces contextually aligned opportunities.
Governance
Every signal and recommendation follows Experian’s principles of transparency, consent, and ethical oversight.
We call this AI-powered simplicity tools that help marketers work more efficiently, with intelligence that feels intuitive and human-centered.
How context changes the game for marketers
AI without real-time context can only react based on what it already knows. AI-powered by in-the-moment contextual data points enables marketers to anticipate, not just react.
A travel brand can shift creative from “dreaming” to “booking” mode when AI detects signals of active planning.
A retailer can align promotions with trending content or regional weather shifts in real time.
A CPG brand can trigger different product messages based on the context of recipes or household occasions.
Adjustments based on contextual signals compound into meaningful gains — higher engagement, better efficiency, and a consumer experience that feels natural rather than intrusive.
Context makes AI more human
Context introduces empathy into automation. It’s what keeps AI from overstepping, ensuring the message fits the moment. When marketers respect timing, environment, and intent, ads feel like service, not surveillance. Context transforms relevance into respect.
At Experian, our vision is to make every signal serve people, not profiles. Because the more our technology (including our AI tools and capabilities) understands context, the more human marketing becomes.
At Experian, responsible intelligence is built in
Every contextual model we deploy adheres to our standards for transparent and responsible innovation. We validate inputs, monitor model drift, and ensure no context-based variable introduces bias or privacy risk. This is what responsible automation looks like in practice: intelligent, explainable, and ethical.
From who to when: Context is the future of AI-driven marketing
Identity tells us who someone is. Context tells us when it matters.
The next wave of AI-driven marketing will unite privacy-first identity with contextual intelligence to deliver real-time relevance, responsibly. At Experian, we’re building that future now. Our AI-driven capabilities bring identity, insight, and generative intelligence together so brands, agencies, and platforms can reach the right people, at the right moment, with relevance and respect.
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About the author
Matthew Griffiths
SVP of Technology, Audigent, a part of Experian
Matthew Griffiths is a seasoned technology entrepreneur and a driving force in advertising technology, data technology, and AI. As the Co-Founder and former CTO (now SVP of Technology) at Audigent, a part of Experian, he plays a pivotal role in shaping the company’s cutting-edge solutions for data activation, curation, and identity management.
With years of executive experience across the U.S., Africa, and the U.K., Matthew has a proven track record of leadership in steering the adoption and use of cutting-edge technologies to drive business outcomes. His expertise spans from collaborating with top global corporations and governments to spearheading award-winning technology projects that deliver life-changing impacts in some of the world’s most underserved communities.
Matthew’s dynamic approach to solving complex business and technology challenges makes him a visionary leader in the AdTech space, consistently driving innovation and performance through technology.
FAQs
Context makes AI-driven marketing more effective because it helps marketers understand people in context, recognizing their environment, mindset, and the moment, to create relevance that feels timely, not intrusive. Context helps marketers understand not just who a person is, but when and why they’re ready to act. Experian’s AI-powered technology layers contextual signals over verified identity data to deliver relevance that feels intuitive, not invasive. This approach connects recognition with understanding, making every campaign more effective and more human.
Identity and behavioral data can reveal the kind of consumer someone is and the kind of products they may want to buy. But they don’t explain what’s happening right now. That’s the context gap—the missing link between knowing something about a consumer and understanding their intent. Context closes this gap by analyzing environmental, temporal, and situational signals that reveal intent—without using invasive identifiers.
Yes, at Experian, our technology interprets contextual signals, including temporal, environmental, and situational, in real-time. By layering these signals over Experian’s verified identity and behavioral data graph, marketers can predict when consumers are most receptive, turning data into real-time opportunity.
At Experian, every contextual model we deploy adheres to our standards for transparent and responsible innovation. We validate inputs, monitor model drift, and ensure no context-based variable introduces bias or privacy risk.
– Privacy-first clarity: We unify household, individual, device, demographic, behavioral, publisher first-party signals, and contextual data points to build a reliable view of consumers, even when certain signals are missing. This clarity helps marketers personalize, target, activate, and measure with confidence.
– Predictive insight: Our models go beyond describing the past. They forecast behaviors, fill gaps with inferred attributes, create lookalikes, and recommend next-best audiences so clients can anticipate opportunity.
– AI-powered simplicity: We’re investing in generative AI and exploring emerging agentic workflows to reimagine how marketers work. Our vision is to move beyond basic audience recommendations toward intelligent audience discovery and automated setup, helping teams uncover opportunities they may not have considered, while spending less time on manual work and more time on strategy and outcomes.
– Real-time intelligence: Consumer journeys never stand still. 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.
– Transparent and responsible innovation: We drive safe, modular experimentation, from generative applications to agentic workflows, always balancing bold ideas with ethical guardrails. We stay at the forefront of evolving legislation and regulation, ensuring our innovations protect consumers, brands, and the broader ecosystem while moving the industry forward responsibly.
Latest posts
Retail media has been on everyone’s radar for a while. Commerce media has also established itself as a significant player in the AdTech industry over the past few years. While retail media focuses on engaging customers within a retailer’s ecosystem, commerce media goes beyond these boundaries to capture the entire shopping journey, spanning multiple touchpoints, channels, and platforms.But what is commerce media, and why should we care? Commerce media is here to stay Estimated to hit $33.86 billion this year and more than double by 2028, the hunt is on to capture as much of retail media’s projected ad spend as possible. However, given the numerous verticals expanding their retail media strategy to include any touchpoint within the commerce channel, it might be time to lower the retail media flag and hoist the LUMA dubbed "commerce media flag." So why are Travel, Financial Services, and other verticals focusing on the commerce media ship? Authenticated and digital users (usually app-driven) Consented data that provides unique insight into the household’s or consumer’s intent/purchase behavior Emerging focus on advertising as an important revenue stream for the future With all this “data” at their disposal — why is it not smooth sailing for commerce media to build an ad-supported business? What’s missing for them to acquire the lucrative billions efficiently and effectively? Why retail media networks are important Retail media networks (RMNs) are now a major tool for brands to connect with shoppers. These networks gather valuable data from customers who browse and shop on e-commerce sites and apps. What makes RMNs so powerful is that they allow brands to advertise directly to people who are already interested in buying, leading to more successful sales. For marketers, RMNs offer a clear way to reach potential customers and ensure their advertising dollars are well spent. But as competition grows and consumer habits change, these networks must keep improving. To stay ahead, brands will have to find new ways to use RMNs effectively, linking them with other parts of the commerce media world to unlock even greater results. Differences between building a loyalty program and developing ad products Loyalty programs are the backbone of commerce media networks; however, creating a loyalty program is much different than building an advertising product. It requires commerce companies to bring on additional people, technology, and partners to execute flawlessly. There are four areas to consider: 1. Organizing your data at scale To successfully build an ad-supported business at scale, data must be organized to initiate action (targeting and/or measurement). However, this requires changes in company culture. Both the business and technology infrastructure must be updated. Additionally, commerce media companies must update their mindset around creating and selling products. 2. People We have seen this story before, with large opportunities comes the requirement for new talent. Where are we seeing commerce media companies recruit from? AdTech and MarTech. Whether it’s engineers or data scientists, business development and partnership leads, or even your direct sales team, the poaching has begun. To build a successful business around advertising, experts are needed who can navigate the waters. 3. Partner vs. build The Requests for Information (RFIs) and Requests for Proposals (RFPs) for any combination of agency, demand-side platform, supply-side platform, customer data platform, identity graph, clean room, and beyond are piling up. One trend is clear: commerce media companies are looking for collaborative partners to provide a true strategic partnership to take on the complexities of the transition away from retail media. 4. Identity will remain the keystone to success All commerce media companies have some identity data that reveals a slice of their customers’ viewpoint. Yet, unlocking the broad view of these audiences is crucial to success. These companies need to use the “full pie” to see well-rounded profiles, gain the reach required to access them across many channels, and turn opportunities into revenue. Advertisers can finally close the loop with commerce media networks Commerce media companies with real-time transaction data enable advertisers to see true ROI on their ad spend when products move off the shelves. Measuring real product lift/sale touch points across multiple channels will put performance and measurement front and center. Programmatic was the promise of performance advertising. Well, commerce media may finally fulfill that vow, creating enough value for companies to make it a real competitor to social channels. While retail media will always exist, the transition to commerce media has become increasingly popular and is here to stay. The journey might not be a straight shot to perfect results, but the data, partnerships, and resources are out there and ready to hop aboard to help guide commerce media companies to success. The future of commerce media Commerce media shows no signs of slowing down. More industries are seeing the benefit of making every customer touchpoint an opportunity to drive sales. Whether through social media shopping or in-app purchases, commerce media pushes businesses to create smoother, more connected shopping experiences for consumers. In the future, brands won’t just compete on prices or products — they’ll stand out by offering simple, seamless shopping experiences across all devices. With better data and tools to track consumer behavior, brands will be able to personalize their ads and measure their success in real time. Commerce media allows brands to see a direct link between their ads and sales. Those who can adapt and keep up with these changes will come out on top. Create a connected customer view with Experian At Experian, we empower RMNs to unlock the full potential of their first-party data through comprehensive identity and audience solutions. Our data-driven capabilities enable RMNs to build a deeper understanding of their customers, optimize audience targeting across channels, and create enriched, actionable segments that drive measurable outcomes. By seamlessly connecting our offline and digital data, we help RMNs organize identities into households, device IDs, and more. Each household is enriched with valuable marketing insights, allowing you to gain better customer understanding, create targeted advertising, and reach the right customers across different devices. Additionally, you’ll be able to measure the effectiveness of your advertising efforts. With our support, RMNs can maximize revenue opportunities, extend reach, and confidently demonstrate the value of the network to advertisers. Contact us today to find out how Experian can help you succeed in commerce media. Contact us Latest posts
Originally appeared on MarTech Series Marketing’s understanding of identity has evolved rapidly over the past decade, much like the shifting media landscape itself. From the early days of basic direct mail targeting to today's complex omnichannel environment, identity has become both more powerful and more fragmented. Each era has brought new tools, challenges, and opportunities, shaping how brands interact with their customers. We’ve moved from traditional media like mail, newspapers, and linear/network TV, to cable TV, the internet, mobile devices, and apps. Now, multiple streaming platforms dominate, creating a far more complex media landscape. As a result, understanding the customer journey and reaching consumers across these various touchpoints has become increasingly difficult. Managing frequency and ensuring effective communication across channels is now more challenging than ever. This development has led to a fragmented view of the consumer, making it harder for marketers to ensure that they are reaching the right audience at the right time while also avoiding oversaturation. Marketers must now navigate a fragmented customer journey across multiple channels, each with its own identity signals, to stitch together a cohesive view of the customer. Let’s break down this evolution, era by era, to understand how identity has progressed—and where it’s headed. 2010-2015: The rise of digital identity – Cookies and MAIDs Between 2010 and 2015, the digital era fundamentally changed how marketers approached identity. Mobile usage surged during this time, and programmatic advertising emerged as the dominant method for reaching consumers across the internet. The introduction of cookies and mobile advertising IDs (MAIDs) became the foundation for tracking users across the web and mobile apps. With these identifiers, marketers gained new capabilities to deliver targeted, personalized messages and drive efficiency through programmatic advertising. This era gave birth to powerful tools for targeting. Marketers could now follow users’ digital footprints, regardless of whether they were browsing on desktop or mobile. This leap in precision allowed brands to optimize spend and performance at scale, but it came with its limitations. Identity was still tied to specific browsers or devices, leaving gaps when users switched platforms. The fragmentation across different devices and the reliance on cookies and MAIDs meant that a seamless, unified view of the customer was still out of reach. 2015-2020: The age of walled gardens From 2015 to 2020, the identity landscape grew more complex with the rise of walled gardens. Platforms like Facebook, Google, and Amazon created closed ecosystems of first-party data, offering rich, self-declared insights about consumers. These platforms built massive advertising businesses on the strength of their user data, giving marketers unprecedented targeting precision within their environments. However, the rise of walled gardens also marked the start of new challenges. While these platforms provided detailed identity solutions within their walls, they didn’t communicate with one another. Marketers could target users with pinpoint accuracy inside Facebook or Google, but they couldn’t connect those identities across different ecosystems. This siloed approach to identity left marketers with an incomplete picture of the customer journey, and brands struggled to piece together a cohesive understanding of their audience across platforms. The promise of detailed targeting was tempered by the fragmentation of the landscape. Marketers were dealing with disparate identity solutions, making it difficult to track users as they moved between these closed environments and the open web. 2020-2025: The multi-ID landscape – CTV, retail media, signal loss, and privacy By 2020, the identity landscape had splintered further, with the rise of connected TV (CTV) and retail media adding even more complexity to the mix. Consumers now engaged with brands across an increasing number of channels—CTV, mobile, desktop, and even in-store—and each of these channels had its own identifiers and systems for tracking. Simultaneously, privacy regulations are tightening the rules around data collection and usage. This, coupled with the planned deprecation of third-party cookies and MAIDs has thrown marketers into a state of flux. The tools they had relied on for years were disappearing, and new solutions had yet to fully emerge. The multi-ID landscape was born, where brands had to navigate multiple identity systems across different platforms, devices, and environments. Retail media networks became another significant player in the identity game. As large retailers like Amazon and Walmart built their own advertising ecosystems, they added yet another layer of first-party data to the mix. While these platforms offer robust insights into consumer behavior, they also operate within their own walled gardens, further fragmenting the identity landscape. With cookies and MAIDs being phased out, the industry began to experiment with alternatives like first-party data, contextual targeting, and new universal identity solutions. The challenge and opportunity for marketers lies in unifying these fragmented identity signals to create a consistent and actionable view of the customer. 2025: The omnichannel imperative Looking ahead to 2025 and beyond, the identity landscape will continue to evolve, but the focus remains the same: activating and measuring across an increasingly fragmented and complex media environment. Consumers now expect seamless, personalized experiences across every channel—from CTV to digital to mobile—and marketers need to keep up. The future of identity lies in interoperability, scale, and availability. Marketers need solutions that can connect the dots across different platforms and devices, allowing them to follow their customers through every stage of the journey. Identity must be actionable in real-time, allowing for personalization and relevance across every touchpoint, so that media can be measurable and attributable. Brands that succeed in 2025 and beyond will be those that invest in scalable, omnichannel identity solutions. They’ll need to embrace privacy-friendly approaches like first-party data, while also ensuring their systems can adapt to an ever-changing landscape. Adapting to the future of identity The evolution of identity has been marked by increasing complexity, but also by growing opportunity. As marketers adapt to a world without third-party cookies and MAIDs, the need for unified identity solutions has never been more urgent. Brands that can navigate the multi-ID landscape will unlock new levels of efficiency and personalization, while those that fail to adapt risk falling behind. The path forward is clear: invest in identity solutions that bridge the gaps between devices, platforms, and channels, providing a full view of the customer. The future of marketing belongs to those who can manage identity in a fragmented world—and those who can’t will struggle to stay relevant. 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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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