Primary Submission Category: Matching, Weighting
Time-Series Matching: Analyzing the Causal Impact of Netflix Subscription on IPTV Viewing Behavior
Authors: Yongho Yoon, Dahai Jung, Kwonsang Lee,
Presenting Author: Yongho Yoon*
Streaming services like Netflix have reshaped media consumption patterns, posing challenges and opportunities for traditional IPTV platforms such as Comcast. This study investigates the causal impact of Netflix subscriptions on IPTV viewing behavior, leveraging real-world, proprietary data not commonly accessible for analysis. A key focus is the dynamic nature of Netflix subscriptions, which fluctuate due to seasonal trends and the release of popular content such as Squid Game. To address these complexities, we extend time-series matching methodologies, capturing nuanced temporal trends that are often overlooked by existing approaches. Our analysis explores several critical questions: Does subscribing to Netflix reduce IPTV views, or does it establish an independent, non-interfering viewing pattern? Does long-term Netflix subscription accelerate the decline in IPTV usage, and does IPTV viewership rebound after subscription cancellations? Additionally, we evaluate whether viral content creates lasting effects on IPTV usage or only transient spikes. To answer these questions, we utilize a range of matching techniques, enabling a robust assessment of both short-term and long-term effects. By bridging gaps in current time-series analysis methods, this research provides actionable insights into the interplay between streaming services and traditional IPTV platforms.