Subscription Revenue for OTT Services In The US Continues To Increase

Regular, relevant, and engaging content is critical to OTT monetization. Content sellers and service providers must have better insights as to how consumers are engaging with their content.?

For this reason, measuring content performance has become a critical component of any business monetizing content through licensing partnerships or direct-to-consumer services. Data analysis can reveal new ways to attract and retain viewers, optimize revenue, and better understand what keeps viewers engaged. Having this information assists in making more accurate content decisions.?

Data analysis can help service providers predict what content to add or remove from their offering, understand the financial performance of specific titles, and provide real-time, accurate recommendations to viewers.

Companies in the ad-supported streaming video business rely on technology partners to deliver programming and advertising to their viewers. Tasks include curating and managing content libraries, marking ad breaks, retrieving, and inserting advertisements into those ad breaks, programming FAST channel schedules, and providing insights on content performance.?

These solutions can also identify new opportunities for monetization while optimizing potential contract terms and revenue-sharing models. In addition, they can aggregate and analyze data from various sources to assess the potential for new opportunities, such as monitoring upward trends within distributor reports for specific content, demographics, and regions. The ability for services to accurately measure and track these sources is essential.

Thanks to advances in data analytics, media organizations can make smarter, faster content investment and distribution strategy decisions to maximize revenue. AI-based analytics solutions require accuracy in data capture throughout the digital supply chain, which means media businesses must embrace new technological requirements and evolve into technology-based organizations.

Content analytics requires collecting large amounts of data (big data) to maximize effectiveness. But the previous, and in some cases current, workflows to collect and analyze the required data are laborious, prone to error, and simply cannot scale. Automated solutions reduce workflow complexity while increasing the efficiency and accuracy of insights, including predicting future content performance.

Without automated measurement and analysis, the manual workflows alone would create significant challenges preventing services from implementing content analytics properly and most effectively– especially considering the disparate data sources and formats involved in licensing content to distribution partners, a key component of today’s hybrid business models.

Subscription revenue for OTT services in the US is forecasted to increase from more than $34 billion in 2021 to more than $46 billion in 2026. Consumers can watch whatever they want, whenever they want, and on any device.?

Every facet of the digital media supply chain, from content delivery networks (CDNs) and cloud-based video encoders to improved TV user experiences, is advancing in capabilities. With so many independent data silos of content owners, platforms, and streaming services, content analytics are proving invaluable to content sellers and streaming service providers and are becoming a mission-critical asset.

Content analytics solutions are only as good as the data provided for analysis. Media organizations would benefit from standardizing data ingest and analysis processes and normalizing diverse data structures.

The performance will always be limited when there is imperfect sharing of data between platform owners, content sellers, and video services. In the end, it is in the interest of all parties to solve the issue of data sharing, as better data sharing benefits all parties. Data standardization will become more critical as AI capabilities and adoption grow in the media and entertainment industry.

The rise of hybrid revenue models alongside economic conditions drives pressure. Services must compete to develop or license content that viewers find valuable among proliferating options, while content sellers must precisely measure and predict the ROI of their licensing partnerships across every revenue model.

Considering the dynamic nature of the media and entertainment industry, content analytics solutions are essential for content sellers and streaming services to optimally monetize content in a complex revenue ecosystem.

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