Advanced Television

Research: QoE growth to be driven by churn reduction

March 26, 2018

UK analyst group Rethink Technology Research has published a five-year forecast on QoE Video Analytics entitled Video Analytics chases churn prediction to a $2.3 billion market. The forecast is based on revenue from 2017 to 2022.

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.

According to Rethink, this industry clearly began in the US, where some 89 per cent of pay OTT subscribers already have video QoE analytics reporting on their apps. This is why the US has a global market share of some 47 per cent. Europe is comfortably in second place with 31 per cent of the market and Asia Pacific is slowed down by a preponderance of AVoD offerings in China – which we have 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.

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.

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 visualisation 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.

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