Yanina RohovskaMany audience data strategies running today will be obsolete within a year — not because regulations killed them, but because they never actually drove revenue. Most teams use audience data out of habit, not because it drives measurable outcomes. This is a problem.
At which stage do quality issues most often appear — and why? How do you align context with the audience when identity signals are limited? Which legacy practices still dominate despite being incompatible with privacy-first requirements?
In this conversation, we speak with Mikhil Patel, Global Platform Partnerships lead at Eyeota, who works at the intersection of audience activation, supply strategy, and data quality in global markets. With extensive experience building integrations across the digital advertising ecosystem, Mikhil sees patterns others miss — where teams make costly mistakes, which approaches actually scale beyond single markets, and how the shift from volume to quality plays out operationally throughout the ecosystem.
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Yanina: How is audience data actually used in programmatic today? In which scenarios does it really help teams make better decisions, and where does its impact stay limited?
Mikhil: Audience data is the foundation of modern marketing. It helps maximize efficiencies and cut waste in a world where budgets are tightening and expectations are rising. When used intelligently, audience data exposes gaps and builds strategies that prioritize quality, consistency, and applicability, engaging both existing and new customers.
Audience data is most impactful when it guides planning and decision‑making, helping teams understand where to invest and who to reach. Its impact becomes more limited when teams rely on it in isolation, without aligning context, creative, or measurement with the audience strategy.
You work with many different markets and teams. At which stage of working with audience data do quality issues most often appear, and why there?
Quality issues most often appear at the sourcing and onboarding stages, simply because this is where the greatest diversity of inputs comes together. We work with partners across the entire AdTech ecosystem and with markets that each operate under their own regulations, taxonomies, and levels of data maturity. That breadth is one of our strengths, but it also means fragmentation can surface early if standards aren’t aligned.
To address this, we remain intentionally disciplined about applying unified global standards. By creating consistency at the entry point, regardless of market, partner, or platform, we’re able to reduce discrepancies, strengthen data quality from the start, and enable smoother, more effective activation across all regions.
There is a sense that audiences are often used out of habit. What are the most common mistakes you see teams make when working with data segments?
A common mistake is opting for cheaper data as a short-term cost-saving measure, often at the expense of the quality needed to build a strong data infrastructure. Another mistake is relying on static data and ignoring behavioral or market shifts. Teams need to look at behaviors and signals over time to deliver more accurate, relevant, and respectful experiences.
Over the past few years, how has the role of audience data changed in advertising strategies? Which use cases have become more common, and which ones are being used less?
Data enrichment has become key, as identity remains important not only for finding the customer but also for predicting behaviors to remain relevant. Audience data has evolved from a narrow targeting input to a full‑funnel, privacy‑first capability that guides planning, creative, activation, and measurement.
At the same time, audience behavior itself has changed. The lines between professional and personal life are becoming increasingly blurred, underscoring the importance of B2B2C strategies. Advertisers are now required to combine B2B and B2C data signals to understand audiences more holistically and reach them effectively across both work‑ and life‑based contexts.
Treating each channel in isolation is becoming less effective, while integrated, omnichannel audience strategies are proving more impactful in driving consistency and performance.
Many publishers question whether audiences actually improve results on the sell side. In which cases does audience data have a real impact on revenue, and when does it make little difference?
Audience data allows publishers to shift from selling broad site sections to high-value specific cohorts, creating premium opportunities for advertisers. It also improves the user experience by helping publishers better understand their audiences, which is essential for retaining attention.
Where does responsibility for performance sit today when working with audiences — between the buyer, SSP, publisher, and data partner? Why do expectations between these parties often not align?
Responsibility is increasingly shared, but misalignment can happen when each party defines “performance” through a different lens. Buyers, publishers, SSPs, and data partners all measure success differently, which can create gaps in their approaches. All parties must work together to bridge the transparency gap and ensure privacy‑safe signals translate into measurable business growth.
How important is the combination of context and audience today? In which situations does audience targeting work better when paired with context rather than on its own?
Combining audience and context has become a strategic necessity. Audience data tells you who a person is, but context tells you what frame of mind they are in. Pairing the two often drives better relevance and performance, especially in environments with limited identifiers.
Omnichannel remains a major topic in the industry. In which channels does audience data work most consistently today, and where does scaling remain difficult?
Audience data performs consistently across digital channels, but reaching real scale requires orchestrating multiple touch-points rather than relying on any single channel or platform. Social and CTV continue to evolve and will remain major focus areas.
Scaling remains challenging in environments with limited identity signals or inconsistent measurement frameworks.
Looking toward 2026, which approaches to working with audience data would you call outdated, even though the market still uses them? And which practices can already be considered truly relevant and effective today?
As we look to the future, the shift toward privacy‑first, interoperable, and outcome‑driven data practices is accelerating, yet many legacy habits remain firmly in place. Traditional approaches, such as static segmentation and the high‑volume mindset, will begin to lose relevance. Success is no longer defined by how much data you can collect but by permission and prediction.
Effective practices today include transparent, auditable data pipelines, privacy‑first methodologies, and persistent identity resolution to connect user behaviors across channels. These are the foundations that will enable scalable and future‑proof audience strategies.
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The programmatic industry spent years chasing scale over quality — collecting data without validating it, selling impressions without enriching them, and adding partners without measuring their contribution. That era is ending.
The conversation with Mikhil makes clear where the market is actually moving: from volume-based strategies to quality-driven outcomes, from regular inventory to value-added activation.
This shift changes priorities across the ecosystem. Publishers enhance monetization results by packaging verified audience and first-party data into premium deals. Buyers win by aligning context, creative, and measurement with the audience strategy. And platforms strive to enable both sides to execute these strategies transparently.
The return on investment in audience data depends on three factors: quality of the source data, strategic integration across channels, and infrastructure that enables control. But it all boils down to simply choosing the right business partners. Those who adhere to the high industry standards and can guide you through all the complexities of integrating a comprehensive audience intelligence strategy.
If you're evaluating how audience intelligence fits into your programmatic infrastructure strategy, we're happy to discuss your specific context. Reach out to explore how the TeqBlaze-Eyeota partnership can help you achieve your objectives.
