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Data InsightsMetadata FoundryUX EngineVideoDocument
ContentWise Personalized Linear Channels
4 UX use cases to create and personalize linear channels
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Data InsightsMetadata FoundryUX EngineVideoDocument
ContentWise for Android TV
4 personalization use cases to improve Android TV UX
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UX EngineVideoDocument
ContentWise for Sports
4 personalization use cases to improve sports User Experience
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UX EngineVideoDocumentWhite Paper
White Paper – Personalization Technology: Build or Buy? 8 Critical Questions
How to choose between vendor and in-house software for media and entertainment UX personalization.
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Data InsightsMetadata FoundryUX EngineVideoVideoWebinar
Economics and ROI of Personalized Video UX
How a personalized UX unlocks new revenue drivers and reduces costs.
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Data InsightsUX EngineVideoDocumentWhite Paper
Executive Guide – TV Super Aggregators: The 3 User Experience Building Blocks
The 3 critical building blocks to build a successful super aggregators’ user experience strategy.
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Data InsightsUX EngineVideoDocumentWhite Paper
Paper – UX Personalization: The 7 Essential KPIs
How to gauge user experience personalization success in OTT, pay TV, and video streaming.
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Data InsightsUX EngineVideoVideoWebinar
The 2020 Roadmap to Effective UX Personalization
How to master UX personalization in OTT, pay TV, and video streaming.
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Data InsightsUX EngineVideoVideoWebinar
Personalization: Adapting to the New World of Television
How operators can drive subscriber engagement, retention and revenue growth.
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Data InsightsUX EngineVideoWhite Paper
Open UX Personalization
Why data-driven, AI-powered, extensible UX orchestration is the only way to deliver truly personalized TV.
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Data InsightsUX EngineVideoVideoWebinar
Webinar Recording – UX Personalization: The 7 Essential KPIs
How to gauge user experience personalization success in OTT, pay TV, and video streaming.
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Data InsightsMetadata FoundryUX EngineFashionPublishingVideoTalk
How We Use Amazon SageMaker in Production
Learn how ContentWise uses Amazon SageMaker to build, train, and deploy their custom recommendation models.