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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Commerce media networks have had a strong start. Growth has been fast, demand has been strong, and brands have made it clear they want closer access to commerce-driven audiences. But as more networks mature and enter the space, many are starting to feel the same pressure point: scale. Most commerce media networks were built as managed service businesses. That model works well early on. High-touch, white-glove partnerships make sense when you’re working with a handful of strategic brands. But there’s a ceiling. There are only so many teams, only so much inventory, and only so many advertisers that model can realistically support. It’s one thing for a large retailer to build custom programs for a P&G. It’s another to do that at scale for hundreds or thousands of brands. At some point, growth slows, not because demand disappears, but because the model can’t stretch any further. The scale problem no one likes to talk about That’s where many commerce media leaders find themselves today. Pausing to assess what comes next. For a long time, growth has been measured almost entirely through media dollars. That mindset is understandable. Media is familiar, it's easy to quantify. It shows up clearly in negotiations and revenue reports. But viewing commerce media networks purely as media sales engines creates long-term risk. It can strain brand relationships, limit innovation, and distract from what commerce media networks actually do better than almost anyone else: understand consumers deeply. Signals are the real asset Commerce platforms sit close to decision-making. They see what people search for, what they consider, what they buy, and when those behaviors change. Those signals are incredibly powerful. And yet, most networks only activate them inside their own walled environments. That’s a missed opportunity. Curation represents the next area of growth for commerce media networks, and it doesn’t require replacing or diminishing existing media revenue. In fact, it complements it. No single commerce media network has all the data needed to give advertisers the scale and reach they're looking for. And no advertiser wants to recreate the same audience in dozens of disconnected platforms. That friction creates inefficiency and slows decision-making. Why collaboration supports sustainable growth The opportunity is to look beyond first-party data alone and start thinking about collaboration. Second-party data. Data partnerships. Signal sharing done responsibly and transparently. Imagine an advertiser defining an audience once and being able to understand and reach that audience across multiple commerce environments. Not through a series of disconnected buys, but through a more consistent approach built on shared understanding leading to increased reach and more impactful campaigns. That’s easier for advertisers to manage, and it creates an additional revenue stream for commerce media networks that complements media sales rather than competing with them. Curation strengthens media, it doesn't replace it Media will always play an important role. There is clear value in custom experiences tied directly to a commerce environment. Think buyouts, sponsored experiences, custom creative integrations. Those are situations where brands want to work closely with the network itself. But the signals commerce media networks hold don’t need to be limited to those moments. Those signals can be monetized independently through data products, co-ops, and partnerships that extend their value into other channels. That’s how curation adds value without undercutting existing revenue. A practical path forward for commerce media leaders For commerce media leaders thinking about their next phase of growth, the focus should be on sustainability. Building a massive media operation takes time and investment. Data-driven revenue streams can be introduced more quickly, require fewer internal resources, and provide steadier margins. It’s a practical approach. Use signal-based revenue to fund growth. Let that revenue support investment in tooling, talent, and media innovation over time. Bootstrapping, in the truest sense. Why transparency matters early There’s also a broader responsibility here. In many advertising channels, transparency followed growth, often after pressure from the market. Commerce media networks have an opportunity to do this differently. To lead with transparency from the start. To be clear with brands and consumers about how data is used, how signals are created, and how value flows through the ecosystem. Because the reality is this: commerce media networks are holding some of the most valuable intent signals in the market today. But those signals don’t retain their value in isolation. If they aren’t enhanced, combined, and made accessible in the right ways, someone else will step in to do it. And when that happens, control shifts away from the source. The bottom line The next chapter of commerce media isn’t just about selling more media alone. It’s about recognizing the value of the signals already in hand, working together to make them more useful, and building additional revenue streams that support long-term growth. That’s how commerce media networks grow without eating their own lunch. About the author Kevin Dunn Chief Revenue Officer, Experian Kevin Dunn joins Experian Marketing Services with more than 20 years of leadership experience across marketing and advertising technology, most recently serving as Senior Vice President of Brands and Agencies at LiveRamp. In that role, he led growth across retail, CPG, travel, hospitality, financial services, and healthcare, overseeing new business, account expansion, and channel partnerships. Kevin is known for building cohesive, accountable teams and leading with optimism, clarity, and a strong sense of shared purpose. His leadership philosophy centers on empowering people, driving positive outcomes for clients and fostering a culture where teams can grow, take smart risks, and succeed together. Latest posts
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