As audience preferences shift towards online video streaming, the volume of content available to stream has grown exponentially and Over-The-Top (OTT) service providers have developed new business models, strategies, and tactics for monetizing video content.
In this week’s blog, we’re taking a closer look at the OTT content monetization strategies and data-driven tactics used by video streaming services to maximize revenue and stay competitive and fight for a share of the video streaming market.
8 OTT Content Monetization Strategies
Let’s start by looking at eight of the most popular content monetization strategies for OTT streaming providers. We’ve divided our list into five strategies for monetizing on-demand video and three for monetizing linear video content.
Monetizing OTT Video On-Demand Content
1) Subscription Video on Demand (SVOD)
SVOD is an OTT content monetization model where audiences pay a monthly recurring fee for unlimited access to stream video content from the service provider’s content library. Many of the largest OTT streaming providers are subscription-based services, including Disney+, Amazon Prime Video, Netflix, Hulu, and HBO Max.
SVOD subscriptions typically range between $5 and $15 per month, but multiplying those low monthly fees across millions of subscribers reveals that successful SVOD companies are earning billions in revenue every year.
SVOD audiences in the U.S. subscribe to an average of four streaming services and spend around $50/month. SVOD platforms are in constant competition to capture more subscribers by offering content that attracts audiences, optimizing the user experience, and working to increase subscriber retention. To compete effectively, these platforms spend billions every year to produce and license new video content.
2) Advertising Video on Demand (AVOD)
AVOD is an OTT content monetization model where the streaming provider allows audiences free access to their library of video content. In the AVOD model, content is monetized by presenting targeted advertisements to audiences as part of the viewing experience.
The largest ad-based platforms in the United States today include YouTube, Facebook Watch, Tubi, and Amazon Freevee. While some AVOD platforms distribute traditionally formatted television and film content licensed from content owners, others provide a platform where niche content creators can earn a share of advertising revenue by publishing short-form video content.
3) Transactional Video on Demand (TVOD)
TVOD is an OTT content monetization model where the video streaming provider or content owner allows audiences to rent or purchase access to video content for a one-time fee.
Digital rentals, also known as Download to Rent (DTR), allow audiences to access and stream a piece of video content for a limited time, usually up to 48 hours after the transaction. The alternative to DTR is Electronic Sell Through (EST), where consumers pay a one-time fee for unlimited access to a piece of video content on an OTT service provider’s platform.
4) Premium Video on Demand (PVOD)
PVOD is an OTT content monetization model where audiences pay a one-time fee for exclusive early access to stream premium, high-demand video content.
The PVOD monetization strategy emerged in response to mandated closures of public theaters during the COVID-19 pandemic, providing a means for large content producers to capitalize on the high initial demand for their premium content releases.
Disney’s Black Widow was one of the first titles released on PVOD. Disney gave its SVOD subscribers the option to stream Black Widow on its opening release weekend for a one-time fee of $29.99, reportedly generating $60 million in PVOD sales. Disney’s Mulan yielded a similar success, generating a reported $270 million in revenue from PVOD.
5) Hybrid Content Monetization
A hybrid model of content monetization is one that combines other VOD monetization strategies to generate revenue from more than one source (e.g. subscriptions, advertising, transactions, etc.).
Peacock TV and Hulu TV both use a hybridized content monetization strategy where audiences can choose to pay either a low monthly subscription fee for access to content with advertisements, or a higher monthly fee for an ad-free experience. Another example of hybrid monetization is the Freemium model, where streaming providers offer free and paid versions of their services. YouTube provides its audience with free access to ad-supported content, but viewers still have the option of subscribing to YouTube Premium for ad-free videos, music, and some additional features.
We’re also seeing platforms like Peacock TV offering a mix of VOD and linear programming, Amazon Prime Video offering 3rd-party, ad-supported content through its SVOD platform, Disney using its SVOD platform to generate PVOD sales, and numerous other examples of hybrid monetization.
Monetizing OTT Linear Video Content
While video on-demand services allow audiences to choose what to watch from a set library of content, linear video streaming means that OTT service providers are the ones configuring channels and making programming decisions, just like on traditional Pay TV.
1) Free Ad-Supported Streaming Television (FAST)
FAST is a OTT monetization model where audiences stream free television and film content presented in a linear format that resembles traditional Pay TV, and FAST service providers earn money by displaying paid advertisements to viewers. Popular FAST services today include Pluto TV, Xumo, Samsung TV Plus, and The Roku Channel.
2) Virtual Pay TV (vMVPD)
Virtual Multichannel Video Programming Distributors (vMVPDs) are OTT streaming services that provide multiple channels of video programming as part of a Virtual Pay TV service. Virtual Pay TV is an OTT content monetization strategy that’s similar to traditional cable or satellite, except that it’s delivered over an Internet connection.
The largest vMVPDs today include YouTube TV, Sling, Philo, AT&T TV Now, and Fubo TV.
3) Live Streaming Pay Per View (PPV)
PPV has always been a reliable way of monetizing highly anticipated live events (concerts, sports, etc.) on cable and satellite TV, but we’re now seeing those broadcasters shift from traditional mediums and into the OTT space.
In the PPV model, audiences pay a one-time fee for access to stream a live event as it unfolds in real-time. Organizations like the UFC and WWE rely on PPV revenue to monetize their live events.
How Do OTT Streaming Services Use Data to Maximize Revenue?
Regardless of which content monetization strategies they use, OTT platforms are competing in an aggressive marketplace to acquire the best content, keep target audiences engaged, retain customers, expand viewership, and uncover new revenue streams.
Data analytics plays a significant role in empowering streaming platforms to execute on their OTT content monetization strategies and stay competitive in the modern OTT market. By collecting, aggregating, and analyzing data from their platforms, OTT providers can uncover valuable insights that help them optimize the user experience and maximize revenue.
What Data Can OTT Streaming Services Collect?
1) Content Performance Data
OTT providers can track and analyze content performance data to discover which content assets are the most popular or receiving the most views from audiences. Viewing metrics like total minutes streamed and avg % completion can indicate engagement with a content asset, while performance metrics like average frame rate and video playback failures can indicate technical errors that negatively impact UX.
2) Audience Demographic Data
OTT providers can capture and analyze demographic data to better understand their audiences in terms of factors like gender, age, ethnicity, income, employment status, and nationality. Demographic data can be correlated with content performance data to better understand which demographic segments have the greatest affinity for a content asset, or even a whole genre.
3) User Behavior Data
User behavior data is a record of all the actions a user takes while either browsing or streaming content on an OTT app. User behavior data answers questions like:
- How long do users stay on the platform?
- How do users navigate the platform to discover new content or re-watch favorites?
- How do users navigate the sign-up process? Where does the most drop-off happen?
Paying attention to user behavior data helps OTT streaming providers find innovative ways to improve their products by reducing friction and making it easier for users to achieve their goals.
4) Financial Data
OTT providers can capture and analyze all of the financial data generated from their streaming platforms, including revenue from subscriber payments, TVOD transactions, and advertising income and expenses. Analyzing financial data can help OTT providers develop new pricing strategies and revenue models that drive profitability.
Five Applications for OTT Streaming Data
OTT streaming providers that efficiently collect and analyze their data benefit from powerful insights that can enhance the user experience and optimize revenue generation at scale. Here are just a few ways that OTT providers can use data to better monetize their content assets.
1) Delivering Content Recommendations
For streaming services with thousands of titles in their content libraries, an important challenge is making it easy for audiences to discover new content that’s relevant to their interests.
OTT streaming services can use content performance, user behavior, and audience demographic data to predict audience viewing preferences and deliver individualized content recommendations at scale. High-quality content recommendations help users spend less time searching for interesting content and more time watching it, resulting in a better platform experience and stronger engagement.
2) Choosing the Right Content Monetization Strategy
OTT streaming providers can analyze content performance data to determine which monetization strategy might work best for maximizing revenue from a given video asset.
Identifying which content assets can move the needle for subscription-driven services, and which ones might perform better on an ad-supported or TVOD platform can have a significant impact on revenue generation.
3) Optimizing Content Acquisition Investments
In much the same way that OTT service providers use data to recommend content, they can also use data to optimize their investments in content acquisition, licensing and original content production.
Analyzing content performance and audience demographics allows OTT service providers to determine what kinds of video content are in high demand and which new content investments would be most likely to succeed on their platforms.
4) Targeting Paid Advertisements
OTT service providers who depend on ad-based video monetization (e.g. AVOD, FAST, etc.) can analyze audience demographics and user behavior data to improve how advertisements are targeted on their platforms. Improved ad targeting means better click-through rates and higher conversions for advertisers, which allows OTT streaming providers to increase the cost of ad placements, resulting in higher ARPU revenue growth for the OTT service.
5) Optimizing the User Experience
OTT service providers can analyze user behavior data to better understand how users are interacting with their platforms and identify opportunities to improve the user experience.
Targeted UX improvements make it faster and easier for users to accomplish their goals and experience the true value of the platform. They often include things like streamlining the sign-up/registration process, making it easier for audiences to search or access desirable content, or adding new platform capabilities that upgrade the viewing experience.
Optimize OTT Content Monetization Strategies with Revedia Digital
OTT streaming providers have a range of strategies to choose from when it comes to effectively monetizing video content.
With the Revedia Digital platform from SymphonyAI Media, streaming providers can aggregate, normalize, and analyze platform data to optimize their OTT content monetization strategies, acquisition investments, recommendations, and to improve the overall user experience.
