MarTech Interview with Jessica Saunders

A fresh perspective on AI, audience intelligence, and privacy-first strategies shaping modern advertising performance.

Jessica Saunders, as Vice President of Global Partner Success & Operations at Eyeota, your career has spanned global markets, data innovation, and audience strategy. What experiences have most influenced the way you approach the future of data-driven advertising today?

My perspective has been shaped by working across markets where privacy expectations, media behaviors, platform rules, and advertiser needs often move at different speeds. Having worked with teams, partners and clients across North America, Europe and Asia-Pacific, I’ve seen firsthand that there is rarely a one-size-fits-all approach to audience strategy. That experience has made one thing clear: Audience strategy cannot depend on a single signal, identifier, or channel.

The most effective advertising strategies are built on flexibility. Marketers need data that is accurate, privacy-first, transparent, and ready to activate across the places where people spend time. At Eyeota, that means helping brands and agencies connect high-quality B2B and B2C audience signals to real campaign use cases, whether they are planning for CTV, display, mobile, social, audio, or digital out-of-home.

The future belongs to organisations that can adapt to changing consumer behaviour and regional market dynamics without sacrificing data quality or trust.

Signal fragmentation continues to challenge marketers across channels and platforms. How is the industry redefining audience intelligence in a landscape where traditional identifiers are becoming less dependable?

Audience intelligence is becoming less about one identifier and more about a connected understanding of people, behaviors, and intent. Marketers still need scale, but they also need confidence that the audiences they activate are built from trusted signals and can move across platforms without being rebuilt for every campaign.

This is why interoperability matters. Eyeota’s approach is ID-agnostic by design, which helps advertisers activate data across different identifiers, platforms, and addressability requirements. As signal loss continues, the value shifts towards audience data that is portable, privacy-first, and transparent enough to support both planning and activation.

Eyeota and ShareThis are shifting the conversation from historical targeting to predictive intent. What makes intent-based audience modeling more valuable for modern advertisers compared to relying solely on past behavior?

Past behavior is useful, but it does not always tell advertisers what someone is likely to do next. Predictive intent helps close that gap by identifying signals indicating near-term interest and likely action.

With ShareThis In-Market, Powered by Eyeota, ShareThis uses predictive behavioral signals, and Eyeota applies AI-driven modeling to translate those signals into scalable audiences for activation. The offering includes 900 in-market audience segments and is designed for global activation across programmatic platforms, including The Trade Desk.

Real-time behavioral data is becoming increasingly important in campaign performance. How are marketers balancing immediacy with accuracy when activating audiences at scale?

Marketers are learning that speed only creates value when the underlying data is reliable. Real-time signals can help advertisers respond to current intent, but those signals must be filtered, validated, and modeled to protect quality.

The balance comes from combining fresh behavioral indicators with disciplined data practices. That means understanding where the data comes from, how intent is scored, how audiences are modeled, and how those audiences can be activated consistently across channels.

Privacy-first advertising has become a business necessity rather than a trend. How can brands maintain personalization and performance without depending on cookies or legacy identifiers?

Brands can maintain personalization by building strategies that are not tied to one legacy identifier. They need privacy-first data partnerships, stronger first-party data strategies, and interoperable activation options that support targeting across addressable and non-addressable environments.

At Eyeota, we believe privacy and performance should be built together. Marketers can still understand and reach relevant audiences when they use high-quality data, ID-agnostic infrastructure, and partners that are transparent about sourcing, compliance, and activation.

The partnership combines AI-driven insights with global activation capabilities. Where do you see AI having the greatest impact on audience strategy and campaign optimization over the next few years?

AI will have the greatest impact in turning large, fragmented sets of signals into clearer audience decisions. It can help identify patterns, score intent, expand reach, and improve activation quality, but only when the inputs are strong.

The real opportunity is not automation for its own sake. It uses AI to make audience strategy more precise, scalable, and responsive while keeping privacy, transparency, and data quality at the center.

Advertisers today are expected to deliver consistency across CTV, mobile, social, audio, and display environments. What operational challenges emerge when building scalable audience solutions across such diverse channels?

The biggest operational challenge is maintaining consistent audience definitions across environments that use different identifiers, formats, and activation requirements. A segment that works in one platform may need to be translated, matched, or modeled differently in another.

This creates complexity for planning, measurement, and optimization. Marketers need audience solutions that can travel across channels consistently while respecting each platform’s rules and each market’s privacy requirements.

Audience quality is often discussed, but not always clearly defined. From your perspective, what separates truly actionable intent data from broad or ineffective targeting signals?

Actionable intent data is recent, transparent, specific, and connected to a real marketing outcome. It should help advertisers understand not only who an audience is, but why that audience is relevant now.

Broad signals can create waste when they are not tied to a clear behavior, interest, or likelihood to act. Quality intent data should be sourced responsibly, modeled carefully, and made available in a way that advertisers can activate across the channels that matter to their campaign goals.

Eyeota has evolved from a traditional third-party marketplace into a broader ecosystem for data collaboration and monetization. What market shifts accelerated that transformation, and how are brands responding to it?

The transformation has been shaped by market shifts we saw emerging early – signal loss, tighter privacy regulation, increasing platform fragmentation, and the growing need for data collaboration. Brands are no longer looking for isolated data buys. They are looking for partners that can help them enrich insights, scale activation, and maintain continuity across a changing media mix.

Eyeota has evolved alongside these shifts to provide a dependable data and identity layer that works seamlessly across regions, identifiers, and platforms. As part of Dun & Bradstreet, we are also uniquely positioned to integrate B2B and B2C signals, enabling true business-to-human targeting at scale across global markets.

Throughout your leadership journey across multiple regions and cultures, what has remained the most valuable lesson about building innovative teams and staying ahead in a rapidly changing ad tech industry?

The most valuable lesson is that strong teams need clarity, trust, and a shared understanding of the customer problem they are solving.