Five considerations for the future of innovation in data and identity

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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