🧠 Enhancing Audience-Reach Models with External Signals
June 21st, 2025
By Ben Anchor
Capstone project submitted for the Berkeley Executive Education Program in AI Strategy (April – June 2025)
📌 Executive Summary
During my time at Berkeley, I explored a business case that had been forming in my consulting work: could external, real-time signals (like search trends, news sentiment, or social media) be used to inform broadcast scheduling — not to replace human decision-making, but to augment it intelligently?
Rather than presenting a technical build or proof of concept, this capstone was designed as an eight-slide business case, showing how broadcasters might benefit from AI-powered scheduling layered with human editorial oversight.
🎯 The Challenge
In the first module of the program, we were prompted to define a potential capstone topic — a “straw man” use case ripe for AI application. I initially considered projects related to accessibility in live sports and real-time captioning workflows, but I eventually settled on something that aligned more closely with my decade of experience: intelligent scheduling for server-based playout systems.
Scheduling teams — the people responsible for aligning programming, creative content, and commercial obligations — work in high-pressure, manual environments. The aim of this project was to explore whether AI agents, augmented with external signals, could support these teams in making faster, audience-led decisions.
🧪 The Approach
The capstone proposed a model that could ingest historical broadcast schedules, recent performance data, and third-party signals — such as:
- Google Trends
- Twitter sentiment
- Viewer discussions in forums and social platforms
- Instagram engagement metrics
The concept wasn’t about full automation. Instead, it focused on “augmented intelligence” — with a human-in-the-loop approach designed to maintain trust and editorial oversight.
To give it a realistic framework, I modeled the project using metrics like Mean Absolute Percentage Error (MAPE) — commonly used to track the gap between forecasted and actual viewership. Thresholds were established to guide how much intervention might be required, depending on performance range
The plan also accounted for future hand-off to ML Ops teams to handle retraining, monitor model drift, and ensure the AI system would remain calibrated to changing audience behaviors and broadcast cycles.
💬 Reflections
This capstone project felt like the culmination of the last ten years of my work in broadcasting — migrating scheduling systems, working side-by-side with programming teams, and observing how operational bottlenecks emerge.
It also reframed what AI can offer to the industry. Instead of replacing creative and strategic input, this system is designed to free up time, so teams can focus on bigger-picture storytelling, campaign planning, and strategic collaboration — not just lining up promos in a spreadsheet.And importantly, it lets broadcasters respond to change at the speed of culture — adapting to shifts in minutes or hours, rather than days.
🚀 What’s Next
This isn’t just a concept on slides — it’s now being positioned as part of the strategic advisory services we offer through Ancast Intelligence. Whether you’re exploring AI scheduling, signal-based insights, or the future of audience workflows, feel free to connect for a full slide deck presentation.
📬 contact@ancast.co.uk
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