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Decoding Intelligence in OTT Platforms | Role of AI in Media & Entertainment

Artificial Intelligence (AI) has reached a state of pervasiveness in everyday life. With significant adoption among industries as well as personal lives, AI is impacting enterprise transformation at scale, whilst changing the way humans interact with machines. The Media & Entertainment industry is one such realm that sees exceptional potential for AI use cases in the coming years.

With the world having 1.1 billion broadband connections and 4.5 billion smartphones, traditional media channels like radio and cable TV have been replaced with on-demand streaming platforms like Netflix and YouTube. Moreover, due to the pandemic, the global lockdown worked as a catalyst in shifting the power to OTT platforms as consumers resorted to entertainment at home. Naturally, the change in consumer behavior prompted media companies to change their business models. Today, almost all the major media houses have entered the OTT market and consumers enjoy seemingly limitless options for media consumption. To stay competitive, media companies are bound to raise the quality of their content, engagement, marketing, and promotion. Advanced technologies like AI are raising the bar of customer experience in OTT. Here’s a look at some defining trends in the space:

1. AI in Recommendation Engines for OTT Platforms

Imperative to predicting user preferences or interests and suggestions, the recommendation engine market size is projected to reach $12.03 billion by 2025.[1] These deep learning engines work by consolidating, comparing, and extracting information about the user’s historical data and then filtering out this information for suggestions or recommendations. OTT platforms are amongst the biggest conglomerates that rely on such engines to keep their content game strong. The end goal of these platforms is to keep consumers hooked by personalizing content attuned to their preferences. The prime example of it is the Netflix Recommendation Engine (NRE) which filters content based on each individual user profile. The NRE is a combination of algorithms that filter over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. The accuracy is astonishing – as over 80% of Netflix’s viewer activity is powered by these personalized recommendations, with total savings amounting to over $1 billion per year.

2. AI & Hyper- personalization

Often dubbed as the future of marketing, hyper-personalization leverages AI and real-time data to ensure highly contextual and the most relevant content /product/info to the end-user. This is especially useful in the right product placements over different channels based on user interests and preferences, and different time slots, optimizing the chance of conversions. Even music streaming sites can benefit from using AI as it allows for personalized listener recommendations and also the right ad placement based on the target audience. Spotify’s Discover Weekly feature is a classic example of Hyper-targeted personalization. Its AI algorithm studies individual music choices and cross-analyzes this data with the preferences of other users who listened to the same songs to create a highly-personalized playlist for each user. Discover Weekly generates billions of new streams for Spotify through AI-powered hyper-personalization.

3. Role of Metadata in Videos – AI in Ads for OTT

Videos uploaded on streaming sites usually include detailed information about the visuals such as descriptions, emotions, and nature of the show. This metadata forms the base from which AI technologies can analyze scenes to help in auto-generating trailers or teasers.

As OTT platforms amass viewer attention, it becomes increasingly obvious for advertisers to place their money on streaming platforms. By relying on a combination of markers such as changes in lighting, color scheme, and the semantics of the scene, AI can successfully detect and separate anomalies making it easy for the detection, removal, and placement of ads. Moreover, by gauging the rich metadata semantics, advertisers can also locate the ideal spots for product placements.

4. Anticipating Demand through Predictive Modelling on OTT

Several OTT streaming platforms anticipate consumer behavior by demand forecasting to understand what genre of shows would be popular among micro-segments of audiences at a later time. Predictive modeling allows platforms to answer tough questions such as genres of new content, the best periods for new releases, and further, which language to be used. Some of the most popular shows running currently were borrowed from other languages, dubbed, and renamed to match the global demand. Some of Netflix’s most popular shows on their platform, namely, Squid Game, Money Heist, and Dark, are Korean, Spanish, and German origin shows, respectively.

5. AI and Gaming

As virtual reality combines with augmented reality to enhance gaming, there are other elements that are equally crucial that can transform user experiences. AI can be used to control the actions of the non-player characters, whose unpredictability can add nuances to the game while advancing the storylines in different directions.

Several media companies leverage AI-based technologies like natural language processing (NLP) and natural language generation to automatically generate subtitles or closed captions for videos. Having AI-generated, multilingual subtitles makes their content easily accessible and global as well while saving manual hours. Often created using deep fake technologies, AI can help organizations detect spuriously generated fake content in the form of text, images, or videos. The onus then falls on media companies to stop the proliferation of these.

The Future of AI in Media & Entertainment

We live in a world that generates data by the second, and the media and entertainment industry is known for its quick adaptation to changing consumer demands. An industry ruled by competition and efficiency will need to depend on AI systems to innovate and keep pace with rapid growth. Brands and conglomerates jostle in the attention economy, and by investing in the right AI systems, they can stand out and reach their targets. A fundamental industry shift towards direct-to-consumer powered by digitalization is compelling M&E companies to increase investments and innovations in the field.

Enterprises need to focus on bridging the gap between different data sets, both structured and unstructured, and finding out the underlying patterns in them to correlate real insights. At BRIDGEi2i, we work with humongous amounts of data, and we are committed to making AI real for enterprises by focusing on actionable insights and real-time recommendations through the use of advanced AI and analytics. Our recommendation engines are used in cutting-edge AI applications to boost efficiencies and accelerate decision making.

To conclude, the future will see far more avenues for AI to make OTT video platforms more effective and profitable, especially among workflows that depend on data-based learning cycles that can contribute to automated systems.

Authors: Meghna Singh & Kshitij Vishnoi