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Summary

Media enterprises are seeking to deliver service portfolios that are better aligned to customer preferences. Personalizing content and making it accessible from multiple devices is a key element of their digital transformation road maps.

Although multi-format content personalization is still nascent, media enterprises have started building video customization capabilities through cooperative partnerships. In May 2015 Iris.tv joined Kaltura’s Technology Partners Program, the Kaltura Exchange and will provide Kaltura customers with advanced personalized and behavioral video recommendation capabilities. This partnership is well aligned with Kaltura’s video platform-as-a-service (VPaaS) vision of providing video-centric services with an agnostic and flexible delivery platform to improve operational efficiencies and create adaptive video experiences.

Delivering a personalized video experience improves customer retention and engagement

Content discovery and recommendation solutions such as Iris.tv are investing in standardizing their processes to improve scalability and reach. Tight third-party integration across the digital content distribution workflow is central to enabling effective recommendation engines. These engines should leverage multiple data sources, both current and historical, to syndicate personalized profiles for individual viewers based on their social and purchasing attributes. They should include predictive analytics plug-ins to enhance personalized content delivery on multiple devices.

Personalized videos are one of the most popular interactive customer engagement formats across industries such as media, healthcare, higher education, and financial services. In the healthcare sector, for example, Kaltura collaborates with population health managers to provide tailored video experiences for individual patients based on their personal profile, medical condition, and prescribed medication path. This facilitates higher customer retention rates and ultimately, it is hoped, better health outcomes by making personal care available to the patient anytime and anywhere.

Its partnerships with Iris.tv and other players further streamlines and strengthens Kaltura’s user clustering recommendation approach. VPaaS uses a bottom-to-top clustering framework to enhance content personalization. The user clustering is based on time, usage, and preference factors. Tier-1 users show loyalty to a particular multiscreen video service, the tier-2 segment might defect, and early birds are first-time subscribers. Content recommendation for early birds is predominantly based on their third-party social and online behavior. These viewers are gradually elevated to the tier-2 segment based on their historical content popularity insights. Predictive analytics add-ons customize content based on individual users’ devices, preferences, and content viewership permissions on an ongoing basis.

As enterprises adapt and embed personalized videos into their digital customer engagement chain, collaboration and partnership between video platforms and in-player recommendation engines such as Iris.tv will increase. The next wave of personalization of content and user experience will see the vertical diversification of online video delivery platforms into newer segments.

Appendix

Author

Kedar Mohite, Senior Analyst, Media and Broadcast Technology Services

kedar.mohite@ovum.com

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