1944. Data-Driven TV Scheduling: Optimizing Public Broadcasting at ZDF
Invited abstract in session TA-26: OR Making an Impact: 3 case studies and a discussion, stream Making an Impact: The Practitioners' Stream 1.
Tuesday, 8:30-10:00Room: Maurice Keyworth 1.33
Authors (first author is the speaker)
| 1. | Carolin Isabel Bauerhenne
|
| Automation & Data Products, ZDF (Zweites Deutsches Fernsehen) | |
| 2. | Xenija Neufeld
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| Accso - Accelerated Solutions GmbH | |
| 3. | Andrea Reckenthäler
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| ZDF | |
| 4. | Andreas Gruen
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| Audience | Automation & Data Products, ZDF |
Abstract
Public-service media play a crucial role in informing, educating, and entertaining audiences across Europe. As a German TV broadcaster, ZDF leverages advanced analytics to enhance decision-making, including TV scheduling. This talk focuses on the practical implementation of a mathematical programming approach for TV scheduling. Using several years of audience data, we apply constraint programming to maximize audience reach while ensuring smooth transitions between topics and adhering to key constraints - public-service obligations, compliance requirements such as youth protection, license availability, and market-driven aspects like key moments when viewers tend to switch channels during prime time. Beyond methodology, we explore the challenges of adoption, including persuading program schedulers to trust optimization-driven recommendations, refining schedules based on feedback, and integrating data-driven insights into editorial workflows. We share key lessons on balancing analytics with editorial needs and adapting OR models to the complexities of stakeholder-driven media environments.
Keywords
- Practice of OR
- OR/MS and the Public Sector
- Decision Support Systems
Status: accepted
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