After months of anticipation, many fans of the HBO hit series “Game of Thrones” encountered problems viewing the season premiere. Because of the overwhelming number of people tuning in simultaneously to watch the show on HBO GO, the system was unable to serve all the requests, leaving hundreds of thousands of subscribers stranded and unable to watch their favorite show when and where they wanted to.
Could this problem — and many other similar performance issues plaguing popular TV series or reality show voting — have been prevented? In short, yes. The answer does not include expensive and time-consuming overbuilding of the network, which leaves reserve capacity to handle peak demand. Instead, it’s about using existing data to anticipate and adjust to changing consumer demands.
For example, a smart approach would be to examine changes in subscriber web traffic. Are consumers viewing video content over Wi-Fi, 3G or LTE? What types of content do they download on a regular basis? When do subscribers watch certain types of content? Predicting future events is often difficult, but clear indicators typically exist. For example, increased search activity, increased Twitter references, and increased viewership statistics of trailers all represent clear signs of future viewership. While examining a single aspect of these data points might be interesting, it’s the fusion of network activity with external data that paints a very different picture.
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