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Why context is key to making AI successful in marketing

Published: November 24, 2025 by Matthew Griffiths, SVP of Technology, Audigent, a part of Experian

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.

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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:

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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).

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Environmental signals

The media or content environment; what type of program, article, or channel someone is engaging with when they see your ad.

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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:

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Input

Clean, accurate identity and audience data anchored in our privacy-first framework.

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Enrichment

Our models fuse household, device, and publisher context to reveal moment-based intent.

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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.

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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.

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A travel brand can shift creative from “dreaming” to “booking” mode when AI detects signals of active planning.

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A retailer can align promotions with trending content or regional weather shifts in real time.

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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

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

How does context make AI-driven marketing more effective?

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.

What is the context gap in AI-driven marketing?

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.

Does Experian interpret contextual signals in real-time?

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.

What does responsible intelligence look like in practice?

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.

What does Experian’s foundation in responsible automation look like?

– 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.


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These moves demonstrate how retailers are rethinking data monetization strategies to scale beyond O&O limitations. Drive new revenue streams with off-site activation Off-site activation enables RMNs to drive incremental reach on channels where audiences are actively engaging, including CTV, programmatic display, and social media. This expansion allows brands to connect with consumers beyond retail websites. Retailers are also utilizing non-endemic advertising opportunities in environments like gas stations and kiosks. Unlike traditional grocery or apparel aisles, these spaces are brand-neutral, allowing advertisers who don’t sell products in-store to still activate campaigns using retailer data. In fact, 53% of brands have already partnered with a retailer that doesn’t carry their product, and that number is expected to grow as advertisers seek new ways to tap into retail media’s rich targeting capabilities. Retailers looking to extend the value of their data beyond O&O inventory have two primary off-site opportunities: First, they can use an identity graph to resolve customer identifiers into addressable IDs that can be enriched with additional attributes and activated across channels like the open web and CTV. This allows retailers to find and reach known customers with relevant messaging outside of their owned platforms. For example, a grocery RMN can identify lapsed snack buyers and deliver streaming TV ads that reengage them on CTV platforms. CTV retail media ad spending alone is expected to grow 43.1% this year, reaching $4.86 billion, highlighting the appetite for video-based upper-funnel strategies. Second, RMNs can broaden reach by activating first-party audiences, syndicated segments, or custom-built audiences through onboarding capabilities. These audiences can be sent to a variety of programmatic and CTV destinations, enabling advertisers to engage shoppers in high-impact environments. For example, a home improvement retailer can send its audience segments to programmatic ad exchanges, ensuring DIY shoppers see relevant offers even while browsing unrelated sites. Together, these approaches allow retailers to monetize their data more effectively while giving brands the ability to reach consumers in moments that matter beyond just retail websites and apps.  Scale and measure success with data partnerships For smaller RMNs to compete with larger players, they need more than just inventory – they need the ability to scale campaigns and prove performance. Data partnerships play a critical role in both expansion and measurement. Measurement remains one of the biggest challenges for RMNs moving off-site. On-site retail media offers closed-loop attribution, but off-site activations introduce complexity. Retailers can work with an identity resolution partner like Experian to connect ad exposures to actual retail outcomes, such as store visits or purchases, across digital and physical environments. Whether it's through pixels placed on campaign ads or TV impression logs, these connections help RMNs demonstrate real impact. This approach helps unify disparate data – such as a CTV ad exposure and a subsequent online or in-store purchase – into a clear, measurable outcome. These insights not only show what’s working, but help RMNs optimize future campaigns and provide advertisers with transparent, third-party-validated reporting. As retailers like Walmart integrate loyalty programs like Walmart+ into their physical extensions, they gain valuable behavioral insights into how customers shop across formats – from fueling up to filling carts. These data signals help refine identity graphs and improve measurement across increasingly hybrid consumer journeys. Beyond ads: The data monetization opportunity Smaller RMNs may struggle to scale ad-supported revenue, but there’s another path forward: Data-as-a-Service (DaaS). Providing anonymized, privacy-compliant audience insights to brands offers a high-margin, scalable revenue stream. In fact, some retailers are already embracing this model by licensing their data to programmatic platforms. A playbook for smaller RMNs to win off-site The future of retail media belongs to those who harness data to influence consumer behavior across all digital marketing channels. To succeed, RMNs should focus on: Moving beyond owned inventory: Activate first-party data across CTV, social, and programmatic channels to meet advertisers where their audiences are. Expanding reach through partnerships: Collaborate with identity resolution providers to maximize match rates and campaign effectiveness. Building a full-funnel offering: Position off-site retail media as a brand-building play, tapping into ad budgets that traditionally fund upper-funnel campaigns. Monetizing data, not just ads: Explore DaaS models to generate passive revenue. The time to move off-site is now Retailers that wait too long to embrace off-site activation risk falling behind. Those that expand beyond their owned inventory, invest in off-site data strategies, and build strategic partnerships will be the ones that shape the future of retail media. Experian isn’t just part of the RMN conversation. We’re driving it. Let’s talk. Connect with our team Latest posts

Apr 24,2025 by Sam Zahedi, Sr. Enterprise Partnerships Manager

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