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Summary

Video is fast becoming one of the most pervasive digital formats for customer engagement in the media industry. Keeping pace with this growth is daunting, and the advent of nonlinear TV and consumption across multiple devices further complicates the challenge of effectively engaging customers with video. Consequently, media enterprises are investing in video-centric workflows to expand their multi-platform reach. However, the nonlinear TV segment is highly fragmented, and margin-pressed broadcasters must standardize their workflows for multi-platform content monetization to avoid creating unsustainable expense.

Advertising is still the dominant revenue model across the broadcasting value chain. In the case of nonlinear TV, the majority of streaming and video-on-demand platforms are developing ad-supported revenue models to offset dependencies on consumer-centric streams that are often subscription-and transaction-based. Standardization of video metadata based on SCTE-35 and SCTE-104 protocols is essential to provide advertisers with contextual monetization avenues on linear and nonlinear channels.

Earlier this year, Crystal introduced its Video Metadata Analyzer (VMA) module, offering broadcasters the capability to monitor discrepancies in operational and descriptive metadata. Ovum believes the launch of this capability positions Crystal well in the market because concise video metadata functionality is pivotal to reducing revenue leakages and capturing opportunities for multiscreen contextual advertising.

Poor video metadata inhibits multi-platform monetization and fails to impede revenue leakages

In a connected business environment, video metadata is fast becoming one of the essential principles of the rich media content value chain. An individual rich media asset is associated primarily with two video metadata types: operational and descriptive. The automated operational metadata can be further subcategorized into inserted and accompanied formats. Operational metadata, based on industry standards SCTE-35 and SCTE-104, provides necessary cue points for ad insertion in linear and nonlinear streams. As videos move from uncompressed to compressed broadcast environments, they often lose a portion of critical inserted operational metadata, increasing revenue leakages. In the user-generated streaming world, for example, Learning Solutions reports that almost 69,000 hours of video feeds are uploaded to YouTube on a daily basis, with the service compressing these assets at the source.This clearly illustrates the potential scale of the revenue leakage problem. Moreover, as transformation from uncompressed to compressed environments can result in lost cue points for ad insertion, TV networks are exposed to litigation and revenue loss when they fail to broadcast commercials at contractually agreed-upon times. Addressing this pain point is the core value proposition for Crystal’s VMA module.

Crystal’s VMA product provides comprehensive video-inserted metadata identification and monitoring functionality to increase media enterprises’ ability to monetize their multi-platform content. VMA sits at the core of Crystal's Insight product offering and consists of the Connect Program Packager and AdCheck ad verification modules. The Connect Program Packager streamlines the multichannel video transport workflow by converting video metadata for linear and nonlinear platforms in real time, including the addition of descriptive metadata for web streaming and on-demand servers rather than just the traditional operational metadata used for linear TV. Finally, the AdCheck Ad Verification module identifies the cause of the lost cue points across multiple distribution platforms to maximize monetization avenues for broadcasters in the connected devices arena.

Because broadcasters invest in delivering premium content such as sports, time-bound events, and original programming, with multiscreen capability to improve customer engagement, personalizing content and advertising in real time will be increasingly important to realizing a commercial return. Ovum believes the ability to achieve this goal in a scalable and cost-effective way depends on media enterprises leveraging robust video metadata through the inclusion of descriptive demographic metadata for content personalization and ad blanking for contextual advertising.

Appendix

Author

Kedar Mohite, Senior Analyst, Media & Broadcast Technology

kedar.mohite@ovum.com

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