As streaming continues to pull viewers away from traditional TV, advertisers are finding it harder to get a straight answer about what their money is actually buying. One platform might say an ad reached 200,000 households, another claims 260,000 for the same campaign, and a third reports a completely different audience mix altogether. In some cases, a viewer who watches the first half of a show on a smart TV and the second half on a tablet is counted twice, or not at all.
For advertisers, this lack of consistency makes it nearly impossible to know which numbers to trust and which platforms are actually delivering value. Brands trying to track whether their ads led to a website visit, store footfall, or an actual sale often face a maze of disconnected data. Without clearer, more reliable systems that can talk to each other, advertisers are left guessing which results are real and which are noise.
Jigar Captain, Head of Engineering at Premion, has spent more than twenty years architecting large-scale systems that support the rapid evolution of advertising technology. Premion, which delivers brand-safe streaming TV ads to viewers in every U.S. market, provides regional and local advertisers with high-quality streaming inventory at scale. In this conversation, Captain discusses the shifting demands placed on CTV platforms, the engineering challenges behind accurate measurement, and why the future of television hinges on data clarity and collaboration.
Many executives feel overwhelmed by fragmentation in TV measurement. How do you define the challenge today?
I see the shift from traditional television to streaming creating a measurement gap that the industry is still trying to close. The panel-based systems that once supported the entire TV economy simply don’t hold up when audiences are spread across dozens of apps and devices. Executives want clarity, not conflicting dashboards, and I completely understand that. The next generation of measurement has to bring together data from connected devices, apps, and linear sources so it reflects how people actually watch.
Leaders also expect more than basic reach numbers now. They want real proof that their investment is driving business outcomes. That expectation changes how engineering teams like mine build systems. Accountability isn’t optional anymore — it shapes every decision we make.
As Head of Engineering at Premion, what has shaped your approach to building CTV platforms?
When you’re responsible for billions of impressions a year, there is no room for fragile infrastructure or unclear data. Leading Premion’s engineering organization through a major M&A integration while scaling teams across four countries pushed me to design systems that are resilient, transparent, and able to adapt quickly as measurement standards evolve. Local and regional advertisers rely on us to deliver trustworthy inventory and clean reporting, and I take that responsibility seriously. They need to know the audience they paid for is the audience they reached.
As more measurement systems enter the market, how do you think advertisers should navigate these new currencies?
Currencies can sound abstract, but it’s actually pretty simple. For decades, everyone used one standard way of measuring TV performance. Now several companies offer different systems for counting audiences and outcomes, which means advertisers can choose which measurement methodology they want to transact on. That flexibility is good, but it also adds complexity. I think the industry needs to treat these currencies like different tools in a toolbox. Each one measures slightly differently, so platforms like ours have to be able to work with all of them. It pushes us to build more adaptable systems and helps advertisers compare results with more context instead of assuming one number is universally correct.
Groups like the Joint Industry Committee are doing important work in evaluating these systems and helping the market understand their strengths. When broadcasters, advertisers, and platforms align on expectations, it raises the bar for everyone and brings more clarity to the entire ecosystem.
AI has become central to CTV analytics and identity resolution. What opportunities stand out to you?
AI has opened the door to understanding CTV performance in ways traditional systems never could. Identity stitching, fraud detection, and outcome tracking all become more accurate when machine learning fills the gaps between devices and platforms. The real value is in turning fragmented signals into clear insights. AI lets us forecast performance, spot creative fatigue, and adjust budgets in real time. It moves us from looking backward to making proactive decisions.
What guidance would you offer leaders preparing for the next chapter of CTV?
I encourage teams to treat measurement innovation as a strategic advantage. Test multiple measurement sources, dig deeper into analytics, and help your organization understand why numbers differ across systems. Slight discrepancies don’t mean failure. They simply reflect different methodologies. The companies that stay curious, experiment confidently, and lean into data-driven measurement will ultimately get the most out of their video investments.
Explore more of Jigar Captain’s engineering vision and CTV leadership on his LinkedIn.
