In a competitive environment, video platforms (SVOD, AVOD, TVOD etc.) are turning to personalization, but as it’s now expected from services, providers need to ensure that they’re offering top-tier experiences. Not only in the accuracy of suggestions but also by giving time back to viewers by reducing the time they spend looking for content and increasing the time they are consuming it. That is where Generative AI and Machine Learning (ML) can play a significant role – in enhancing personalization and content discovery to improve the overall user experience on OTT platforms.
What is Generative AI? How is it used for personalization?
With the explosion of ChatGPT at the start of 2023, Generative AI has become an industry buzz term over the past few months. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. This type of AI has already significantly provided opportunities for productivity and innovation. Despite it seeming like this is a new innovation, the use of Generative AI is nothing new, it was introduced in the 1960s for chatbots.
More recently, Generative AI has been making waves in the entertainment industry, where it is being used to create music, art, or even entire films. For example, you can now watch the short film ‘The Frost’, which has been generated purely by Open AI, DALLE-2.
For OTT platforms, generative AI can analyze content and user data such as watch history, viewing habits and ratings to identify patterns and assist in generating personalized recommendations. However, the user data is useless without the content knowledge that has been analyzed. Only when the content is within the semantic domain and combined with user data, then companies can gain the advantages of generative AI. The increasing capabilities and understanding of natural language with audio and video content can power personalized audience experiences. This can range from customized thumbnails for videos to creating personalized summaries or playlists created specifically for individual users based on their preferences. Helping build a unique UI that is attention-grabbing for different viewers by creating unique rows based on a group of content that a viewer has been enjoying.
What is Machine Learning and how is it used for personalization?
Machine Learning is an application of AI that allows systems to improve their performance through experience. ML focuses on the use of data and algorithms to learn behaviors and aiding to improve accuracy without being programmed to. When it comes to personalization on OTT platforms, it involves analyzing vast amounts of data about user behavior and preferences to continuously enhance the recommendations and personalized content provided to them.
For example, if a particular user tends to watch a lot of action movies, Machine Learning algorithms can identify this pattern and suggest similar movies or shows to the user. Over time, these algorithms can better predict user preferences and suggest content that is highly likely to appeal to them. The more interactions a user makes, the more they can learn about them and offer new recommendations they will enjoy most.
Ways to Leverage AI and Machine Learning to Enhance OTT Customer Experiences
AI and ML systems provide a powerful tool for enhancing personalization on OTT platforms, driving greater engagement, retention, and revenue. By integrating these technologies into their platforms, OTT providers can offer their users a more satisfying and engaging experience, ultimately driving better business outcomes.
Enrich Your Metadata
Metadata provides context for the content to match the assets with the right users and helps increase the reach of its content target audience. When accurate, metadata can improve search, recommendations and content information like a synopsis. For ML algorithms, it is vital that the data set describing the content is rich and consistent to be able to make the right connections between content and audiences.
AI and ML can simplify metadata enrichment by analyzing vast amounts of data about a particular video and plot and then using this analysis to create highly specific metadata to describe the video. Metadata can improve search results for users and enable more personalized use cases based on their viewing habits.
Become the Super-Aggregator
The TV market is becoming more and more fragmented, overwhelming viewers and adding challenges around acquisition and retention. As the landscape is evolving, super-aggregation becomes the way forward for operators and streamers. This aggregation of all services relevant to the user into one, easy-to-use content discovery experience drives consumer loyalty and customer value.
Utilizing Generative AI and ML to combine data from multiple sources can be helpful in producing complete user profiles and a consistent navigation experience. In the context of OTT platforms, this could include watch history, browsing history, and physical location data on the one hand and consistent thumbnails, summaries, and classes of content on the other. By combining all of this data, the platform can gain a greater understanding of each individual user’s preferences, enabling them to provide more personalized recommendations and content.
Personalize Search
Personalization of search involves tailoring search results based on each individual user’s preferences. To contribute to an excellent user experience, a search experience should analyze previous search history and the types of content they regularly watch, as well as understand each user’s intent behind their search terms.
In order to retain users, the whole OTT experience should be personalized, as 80% of users stay loyal to platforms in the streaming era that are offering personalized experiences. AI and ML can enable a more powerful search by understanding the meaning behind fuzzy search terms entered by the user and also by better understanding when natural language is used for searching your catalog.
The Power of Segmentation
Segmentation involves using AI and ML to categorize users into specific groups based on their interests and viewing habits. These segments can then be used to create more personalized recommendations or groups for targeted ads, increasing the relevancy of ads shown to audiences.
Benefits and Challenges of the Use of Generative AI and ML for OTT Personalization
Enhancing user experience to help retain subscribers with AI and ML is not without its challenges. Take a look below at the benefits and challenges when leveraging powerful technologies to deliver highly personalized experiences.
XroadMedia works with media companies to help drive results with ground-breaking personalization. The solution provides AI and ML based services to deliver fast and accurate results for personalizing every touchpoint media companies have with their users. If you are interested in learning more, please get in touch.
