Viacom: Viacom reduces video start delay by 33% by improving video streaming using machine learning to analyse network performance and resource utilisation | Case Study

Viacom reduces video start delay by 33% by improving video streaming using machine learning to analyse network performance and resource utilisation

Viacom uses machine learning to analyse quality of video feed and recognise patterns and thus predict on-demand video requests from customers. They platform also does real-time resource allocation to ensure there is no delay and have reduced time to start video by 33%. They also have better insights into customer preferences and use this for personalised advertising.

Context

Viacom Inc. is an American multinational media conglomerate with interests primarily in film and television

The Project

"Viacom, with its 170 cable, broadcast and online networks in around 160 countries, is using data to improve user experience and resource utilisation. Improving user experience: Streaming petabytes of video data across the world puts a strain on the delivery systems, resulting in videos failing to load or constantly stuttering as they rebuffer. Growing the audience: Making sense from huge troves of viewing data and determining the best actions to drive viewer retention and loyalty. Targeted advertising: With TV ad sales falling in recent years, Viacom needed to find better ways to engage with their audience via advertising."

Results

According to the company: * Reduced video start delay by 33% by predicting trends * Improved ad conversions by personalizing * Identified metrics to increase customer retention by 3.5-7x

Related Use Cases

Use Case

Forecast network demand

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Use Case

Optimise network resource allocation based on real time and predictive load analysis

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Use Case

Forecast product / service demand levels

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