Churn Reduction Algorithms to Drive QoE Video Analytics to a $2.3 Billion Market, Rethink Research Predicts

QoE video analytics is growing into an arms race to build third party tools, which allow all pay TV players and many free to air broadcasters, to behave more like Netflix, Amazon and Hulu, according to a new Rethink Research report.

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QoE Video Analytics has in a few short years gone from being a simple confirmation of whether or not video frames get through to a device, shown on a basic dashboard, to becoming a complex big data opportunity for all media enterprises everywhere.

The next phase of growth will be driven by churn reduction algorithms using this data to slow down churn, and then slowly the marketplace will move to towards the creation of generic data warehouses in the cloud, which individual silo applications tap into from every department in a media company.

This industry clearly began in the US, where some 89 percent of pay-OTT subscribers already have video QoE analytics reporting on their apps, the analysts say. This is why the U.S. has a global market share of some 47 percent. 

Europe is comfortably in second place with 31 percent of the market and Asia Pacific is slowed down by a preponderance of AVoD offerings in China – which are not counted in this forecast because their apps have different priorities. Currently in China there are around 1.2 billion AVoD accounts and at last count around 120 million SVoD.

First generation or phase 1 in video analytics was to build one siloed app which offers a visual representation of how well an operator’s video is currently being delivered. This included different ways of drilling down from high level global views to specific devices and requires the use of a real time visualization dashboard.

Current attempts on QoE Video analytics are trying to anticipate individuals who might churn because they have had some aspect of the experience which was not ideal and creating a formula for automatically preventing this churn. This application goes straight to the bottom line of every operator. The operator might offer a free month or a special feature to retain their business.

The final phase will be the acceptance that there are multiple elements of each operator’s business that can use this data – from purchasing and planning of media, through to store fronts and network planning and selection – and a fully featured data warehouse will be needed to manage data requirements for all of these.

The winner in each phase will not automatically be the winner in the subsequent phase.

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