From Counting Logos to Capturing the Moment — How AI is Powering the Next Era of Sponsorship Measurement

Why Sponsorship Measurement Needs Context

Sponsorship measurement has evolved well beyond just tallying logo appearances. At Relo Metrics, we enrich exposure data with size, clarity, duration, and placement — helping sponsors and rights holders understand not only how often their brand was seen, but also the quality of that visibility. Still, something was missing: context. Was the logo visible during a game-winning shot or a quiet timeout? Was the crowd erupting or standing still? Context is the next frontier.

Rights holders want to know which placements shine in key moments. Sponsors want to connect their brand with plays that drive true engagement. Broadcasters want to spotlight impactful sponsor moments in real time. Until recently, producing this kind of insight at scale was nearly impossible. Now, Vision Language Models (VLMs) can bridge the gap by linking what is seen with what it means — turning exposure data into stories of the moments that matter.

Relo Metrics’ Existing Strength — Industry-Leading Sponsor Detection & Media Value Measurement

Relo Metrics has long set the standard in sponsor visibility measurement. Relo’s computer vision captures every logo appearance on LED boards, jerseys, signage, and graphics, and more, translating exposure into Sponsor Media Value (SMV) — a transparent way to measure advertising value. Sponsors use SMV to evaluate effectiveness, rights holders use it to prove value, and broadcasters use it to quantify programming impact.

While knowing visibility and value is powerful, knowing what was happening during those exposures is transformative.

Vision AI and the Role of VLMs in Sponsorship Analytics

This is where Vision Language Models enter the picture. These AI systems don’t just recognize logos and players; they understand and can describe scenes in natural language. They merge computer vision with reasoning, enabling them to detect players and environments, interpret actions and sequences, and generate coherent summaries of events. Instead of reporting “Gatorade logo visible for 3.2 seconds,” a VLM can output, “Gatorade logo visible on courtside banner during a game-winning three-pointer; crowd erupts in celebration.” This leap in intelligence transforms SMV into context-aware value. It makes sponsorship measurement more human-like, telling the story of the moment and why it mattered.

To explore this potential, we designed an experiment using NBA game footage. We selected a fast-paced matchup filled with sponsor logos across multiple placements, crowd reactions, coach interactions, and broadcast replays. We tested prompts designed to find structured play-by-play logs, game summaries, and grouped highlight sequences.

Each prompt had a distinct purpose. The play-by-play log was designed to capture timestamped micro-actions, feeding into precise SMV calculations. The game summary was meant to read like an executive recap, highlighting momentum shifts, crowd reactions, and sponsor visibility. The highlight grouping prompt merged related plays into coherent, narrative-driven sequences that could power automated highlight reels.

For this test, we used NVIDIA’s Cosmos-reason1-7b, a vision language model that has demonstrated strong abilities in connecting plays, recognizing the flow of action, and describing the surrounding environment in structured, meaningful ways. Cosmos Reason went beyond raw action logs by describing how moments connected together, detecting crowd anticipation, and narrating sequences with more coherence.

In the play-by-play outputs, it produced structured, time-anchored logs of what players were doing and how possessions flowed. In the summaries, it highlighted turning points, like a three-pointer that cut a lead and sparked a loud reaction from the crowd. And in grouped outputs, it began to stitch together sequences that felt closer to human highlight storytelling. These outputs showed real potential for scaling game context intelligence across hours of footage, turning sponsorship measurement into something richer and more actionable.

Overall, Cosmos-reason1-7b excelled in structured precision and sequence tracking and feels like a reliable scorer’s table, capturing every key detail and linking it into coherent sequences.

From Counting Logos to Capturing the Moment

Looking ahead, we’re excited to integrate Cosmos Reason into the Relo Platform

• Automated Context Tagging: Attach game context to every sponsor appearance.
• Context‑Aware SMV: Adjust values by the impact of the moment.
• Highlight Generation: Instant, sponsor‑relevant clips with natural captions.
• Extending this framework beyond basketball to soccer, tennis, esports, and beyond.

By integrating models like Cosmos-reason1-7b into the Relo platform, we can automate context tagging for every sponsor appearance, adjust SMV by the impact of the moment, and even generate sponsor-relevant highlight clips with natural captions instantly. While our experiment focused on the NBA, the framework extends to soccer, tennis, esports, and beyond.

Sponsorship measurement has always been about proving value. With AI, and in partnership with NVIDIA, we’re entering a new era — one where we’re not just counting exposures but capturing the moments that matter most to fans and brands alike.

To learn more about Relo Metrics’ computer-vision sponsorship measurement capabilities, visit relometrics.com/sponsorship-measurement-platform. 

Relo Metrics Contacts:

• Jay Prasad, CEO, Relo Metrics
• Raghav Gupta, VP Engineering, Relo Metrics
• Hannah Shain, VP Marketing, Relo Metrics, marketing@relometrics.com