How Important And Reliable Are Deep Video Analytics In The OTT Space?
The OTT space has been witnessing and undergoing a lot of changes as per how users are consuming and interacting with content. Advanced technologies like AI and ML engine power the UI/UX personalising content for users. However, what is interesting is, how worthy is the data?
The pandemic has resulted in a humungous rise in the consumption of services by OTT platforms. As per a recent report, the average time spent by a user is 4 hours more than that compared to the previous times of 1.5 hours. What’s interesting to note here is that technology has been acting like a backbone in the successful running of operations. Vinit Mehta, Country Manager, Brightcove says, “Deep video analytics is of utmost importance to the OTT industry. Analytics help the OTT industry and OTT service providers specifically with content recommendations and churn models. Serving up content viewers want to watch based on previous viewing habits keeps audiences engaged on the platform and coming back for more. For example, if a viewer signed up for a service to watch a specific series, recommending a movie or another series that is similar to what they just completed will help to keep them on the platform longer than originally planned. Meaning, because relevant content was recommended, the viewer stays to watch and the platform receives more revenue.”
Additionally, leading industry experts stated that the use of Internet of Things (IoT) has been massive, right from Information Technology (IT), security, broadcasting, digital media and mobile devices. Mehta continues, “Furthermore, understanding churn analytics it’s of utmost importance for OTT services. These predictive models use analytics to determine the churn threat of a viewer. Understanding where a viewer is in the churn model can help services draw them back in, by way of engagement campaigns. Churn analytics are important because they help services understand their audience more, if a viewer is going to leave the service, or if the viewer is engaged with the platform, therefore not a churn threat.”
How to fuel a growing appetite for this platform but, and how does technology act as the backbone? Prasanna Gokhale, Chief Technical Officer, Atria Convergence Technologies says, “To fuel this growing appetite and content consumption, a high speed reliable fiber broadband becomes a basic requisite to ensure unparalleled speed and connectivity. As a part of it’s brand promise of Feel the Advantage, ACT Fibernet has active partnerships with leading OTT players such as Netflix, Amazon Prime, Zee5, SonyLiv and Hungama, wherein ACT Fibernet customers can subscribe to these platforms and pay the charges as a part of their ACT bill, with several benefits. This feature enables customers to receive extra discounts, cashbacks on Netflix upto Rs 300 p.m. as well as extra months free streaming. Additionally, we have also launched our own Stream TV 4K, a full-fledged streaming device that combines all forms of content – live TV and streaming apps together into one. This creates a superior viewing experience and provides customers with a constant stream of enriched entertainment through our future-ready superior networks.”
At a time when the OTT streaming traffic has been rising to unprecedented levels, it’s also important for OTT players to take care of their costs, bandwidth and storage. Mehta concludes, “As OTT streaming traffic rises to unprecedented levels, OTT service providers also need to keep an eye on their costs, such as bandwidth and storage. Brightcove’s Context-Aware Encoding is designed to help reduce the operational costs of running a video streaming business, by lowering the cost of storing and streaming video with Brightcove’s groundbreaking video compression technology. Context-Aware Encoding uses machine learning to optimize encoding settings on a per-video and delivery context basis. The technology is proven to reduce storage and bandwidth costs associated with video delivery without compromising visual quality.
CAE uses machine learning and deep video analysis to achieve optimum quality for each video with the fewest bits necessary. Unlike other encoding solutions, CAE takes into account the broader context of the video experience, creating a custom encoding profile tailored to the combination of each individual video’s content complexity and predicted viewing environment. The result: a higher quality video that starts up faster and buffers less”.
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