Major TV Network

Company Background

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This major television network delivers programming to millions of viewers across multiple channels and platforms. Like many media companies, they rely heavily on audience data to drive advertising decisions, but the unpredictable nature of viewership made it difficult to generate timely, actionable insights. Their existing process for analyzing viewership data was time-consuming and leaned heavily on institutional knowledge rather than scalable, data-driven tools.

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The Challenge

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The network needed to develop a new analytics platform—fast. The existing forecasting workflow took six weeks, limiting the ability to respond quickly to shifting audience trends and ad inventory demands. Internal teams worked in silos, with limited visibility and collaboration between data science and app development. The organization sought a partner who could bring strong Agile expertise, a cross-functional product team, and a user-centered approach to building a modern, efficient platform.

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The Solution

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Stride assembled a cross-functional team of product managers, designers, and engineers to partner closely with internal stakeholders. The team embedded with users to observe workflows, identify friction points, and uncover key requirements. From day one, Stride applied Agile practices tailored specifically to the network’s needs—improving collaboration, increasing transparency, and bridging the gap between technical teams.

Rapid visual prototyping was central to the process. It allowed stakeholders to validate functionality early and often, reducing rework and accelerating delivery timelines. Stride also established a workflow that successfully aligned the efforts of the data science and app development teams, ensuring shared ownership of outcomes and continuous feedback.

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The Business Outcomes

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In partnership with Stride, the network launched a polished analytics platform that dramatically improved the speed and accuracy of viewership forecasting. The new platform enabled:

• Granular and holistic data views
  
  
• Customizable baselines based on real-time insights
  
  
• A reduction in forecasting cycles from six weeks to just one
  
  

The impact was significant: researchers could now generate timely, data-driven forecasts in days instead of weeks—transforming how the company aligned ad inventory and made revenue-critical decisions.

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The platform not only improved operational efficiency but also positioned the network to avoid millions in potential lost revenue. It’s a first-of-its-kind analytics tool in the industry—delivered in weeks, not months.

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