Over-the-top (OTT) Video Streaming Services have changed the way we consume entertainment, providing viewers with the freedom to access content on-demand, without the limitations of traditional TV schedules. With millions of hours of content available on streaming platforms, it's impossible for viewers to sift through everything on their own. Effective search and recommendation algorithms are crucial to the success of OTT Video Streaming Services, not only helping viewers discover content they might like, but also increasing ad performance and ad viewability by serving relevant ads to the right audience.
Dynamic front-end experiences can also play a critical role in keeping viewers engaged. Personalized entitlements can also improve the viewer experience, enabling streaming services to serve viewers according to their subscription model, language, and other preferences.
Additionally, real-time analytics, data enrichment, and clickstream analysis can improve the search and recommendation experience, providing a more personalized experience that can lead to increased engagement and ultimately, increased revenue. Another key aspect of OTT Video Streaming Services is hyper-scale serving. With millions of viewers accessing content at any given time, it's critical for OTT Services to be able to handle large amounts of data and event processing required for real-time ad matching. Lastly with illegal streaming increasing, OTT Video Streaming Services need to implement mechanisms to stay compliant.
Search and Content Recommendations
While search and recommendations may seem like minor features, they play a critical role in enhancing the OTT video viewing experience. Through sophisticated algorithms and data analysis techniques, OTT Services can deliver personalized content and recommendations, leading to increased engagement, retention, and ultimately, revenue.
Algorithmic recommendations are crucial for OTT Video Services like Netflix to keep users engaged and ensure that they are satisfied with the content available. With the use of machine learning models, these services can provide highly personalized and relevant content suggestions to each individual user, which can ultimately lead to increased engagement and retention. According to Netflix, 80% of what its users watch is based on algorithmic recommendations. This demonstrates the importance of providing personalized content to viewers, which can keep them engaged and coming back for more.
Netflix uses machine learning models that analyze the viewing history of users and other factors such as the time of day, the device being used, and the user's location. These models then use this information to predict what the user is likely to watch next and suggest relevant content. To make these recommendations even more accurate and personalized, Netflix also uses deep learning techniques such as neural networks. These models can learn to represent the preferences and interests of a user in a highly complex and non-linear way, enabling them to make more accurate predictions.
Dynamic front-end experiences
Another way to keep viewers engaged is through dynamic front-end experiences. Maybe this could include the ability to host watch parties across different locations in a synchronized stream with chat. Such interactive experiences can deepen viewer engagement, fostering a sense of community and creating an immersive viewing experience. Real-time analytics can combine viewers' past history, current trends, and other behavior analysis variables for catalog customization. This approach enables viewers to discover new content based on their interests and previous viewing habits.
Better ad performance
Ad performance is critical for the success of any OTT Video Streaming Service, and this requires sophisticated ad targeting and inventory management capabilities. OTT Video Streaming Services need to be able to serve ads that are relevant to each viewer, based on their interests and viewing history.
A significant strategy for OTT providers is using hyper-targeted ad cohorts and dynamic ad matching. This involves segmenting viewers into groups, or cohorts, based on common characteristics like demographics, psychographic data, financial value, and location. By creating more specific cohorts, advertisers can increase their chances of reaching and engaging with the right audience, which ultimately results in higher monetization for the content and generates actual revenue from ads.
To address these challenges, many OTT Video Streaming Services are turning to solutions designed to provide fast, reliable, and scalable infrastructure for processing and delivering data. OTT Video Streaming Services can ensure that content and ads are delivered quickly and efficiently even with large volumes of ad cohort groups, regardless of where viewers are located.
Data enrichment for personalization
Data enrichment is a critical component of delivering personalized content. By leveraging additional data points like geolocation, social media activity, and past purchases, OTT providers can gain a better understanding of their viewers' interests and preferences. This information can then be used to tailor content recommendations and improve ad inventory, leading to increased ad performance and monetization. Dynamic ad matching using hyper-targeted ad cohorts is a critical tool for streaming services to increase ad performance and reach the right audience with the right ad.
Streaming services are challenged to keep track of subscription entitlements and serve viewers according to their subscription model, language, and location, among other factors. Subscribers expect a seamless viewing experience across all devices, but it can be challenging to manage entitlements for millions of viewers across the globe.
One approach is to offer subscription-based entitlements that enable customers of partner affiliates to access other services. For example, some wireless telecom providers offer free trials to video services like Hulu or Netflix with the purchase of a new device. However, managing entitlements for thousands of ad cohort groups can be challenging at scale for large events or new popular content releases.
Digital rights management (DRM) and preventing illegal streaming
Digital rights management (DRM) is a set of technologies and strategies aimed at protecting digital content from unauthorized use, copying, and distribution. Within the realm of DRM, there is a significant focus on combating illegal streaming and piracy, which pose significant challenges to OTT streaming services. The rise of digital platforms and the internet has made it easier than ever for individuals to access and share copyrighted material without proper authorization. To tackle this issue, various anti-piracy measures have been implemented, including stringent copyright laws, sophisticated encryption techniques, and digital watermarking. Additionally, collaborations between content creators, streaming platforms, and law enforcement agencies have become crucial in the fight against piracy. By employing robust DRM mechanisms like token management and employing strategies to curb illegal streaming, OTT video streaming services can help ensure legal compliance.
OTT Video Streaming Services face several challenges in delivering high-quality, personalized content and ads to viewers. Powerful data enrichment and real-time ad-matching capabilities can help meet these challenges. By leveraging sophisticated search and content personalization capabilities, hyper-targeted dynamic ad matching with data enrichment, and subscription-based entitlements, OTT Services can provide the best possible viewing experiences for their audiences. Learn more about Macrometa's ready to go OTT Video Streaming solutions to maximize revenue and streaming engagement, or chat with a solution architect.