The media and entertainment industry has been one of the earliest adopters of artificial intelligence (AI). AI has transformed the way content is created, delivered, and consumed.
The growing ubiquity of content creation tools like high-resolution cameras, content creation software, and smartphones is allowing pretty much anyone to create, publish, and distribute written, audio, and video content.
This trend is further accelerated by the proliferation of the internet, which has led to the replacement of traditional media channels like cable and radio with on-demand streaming platforms like Netflix and YouTube. As a result, consumers have potentially limitless options to choose from, in terms of media consumption.
Thus, media companies are facing the need to raise the quantity as well as the quality of content they create to attract as many consumers as they can to drive higher value. To help them achieve this objective, media companies are adopting advanced technologies like AI.
The use of Artificial Intelligence in the media and entertainment industry is helping media companies to improve their services and enhance the customer experience. Here are a few use cases of AI in media and AI in entertainment that are transforming the industry:
View: How to leverage customer data for the Media & Entertainment industry
1. Personalization of Content:
One of the most significant applications of AI in the media and entertainment industry is personalization of content.
AI-powered algorithms analyze user data to personalize content recommendations, advertising, and search results. Personalization not only enhances the user experience but also increases engagement and loyalty.
Music streaming services such as Spotify and Pandora use AI to create personalized playlists for their users.
The AI algorithms analyze user data, such as listening history, search history, and user preferences, to create playlists that cater to their tastes. Similarly, video streaming services such as Netflix and Amazon Prime Video use AI to personalize content recommendations based on the user's viewing history, ratings, and searches.
2. Metadata tagging:
With countless pieces of content being created every minute, classifying these items and making them easy to search for viewers becomes a herculean task for media company employees. That's because this process requires watching videos and identifying objects, scenes, or locations in the video to classify and add tags.
To perform this task on a large scale, media creators and distributors like CBS interactive are using AI-based video intelligence tools to analyze the contents of videos frame by frame and identify objects to add appropriate tags.
This technology is being used by content creators or media publishing, hosting, and broadcasting platforms like NFL Media to organize their media assets in a highly structured and precise manner. As a result, regardless of its volume, all the content owned by media companies becomes easily discoverable.
3. Content personalization:
Leading Music and video streaming platforms like Spotify and Netflix are successful because they offer content to people belonging to all demographics, having different tastes and preferences.
Such companies are using AI and machine learning algorithms to study individual user behavior and demographics to recommend what they may be most interested in watching or listening to next keeping them constantly engaged. As a result, these AI-based platforms are providing customers with content that caters to their specific likings, thus offering them a highly personalized experience.
4. Reporting automation:
In addition to automating day-to-day or minute-by-minute operations, AI is also helping media companies to make strategic decisions. For instance, leading media and broadcasting companies are using machine learning and natural language generation to create channel performance reports from raw analytics data shared by BARC.
The weekly data that is usually received from the Broadcast Audience Research Council of India (BARC) is generally in the form of voluminous Excel sheets. Analyzing these sheets on a weekly basis to derive and implement meaningful learnings proves to be quite daunting for the analytics team.
By using AI-enabled data analysis and natural language generation-based reporting automation tools, business leaders can create performance reports with easy-to-understand language commentaries, providing them accurate insights to make informed data-driven decisions.
5. Predictive Analytics:
Another application of AI in the media and entertainment industry is predictive analytics. AI-powered predictive analytics is used to forecast box office revenues, TV ratings, and other performance metrics.
Predictive analytics helps media and entertainment companies make informed decisions about content production, advertising, and distribution.
For example, Nielsen's AI-powered predictive analytics model can forecast TV ratings for upcoming shows. The model analyzes historical data and social media trends to predict the success of a show before it airs.
Similarly, AI-powered predictive analytics can forecast box office revenues for upcoming movies, helping studios make decisions about distribution and marketing.
6. Predictive Analytics:
AI-powered virtual assistants and chatbots are also improving customer engagement and collecting data about user preferences and behavior.
Virtual assistants such as Apple's Siri and Amazon's Alexa use natural language processing to interact with users and perform tasks. Chatbots are AI-powered programs that can engage in conversations with users.
Media and entertainment companies are using virtual assistants and chatbots to provide personalized recommendations, answer customer inquiries, and provide customer support.
For example, the NBA uses Facebook Messenger chatbots to interact with fans, providing them with real-time updates, scores, and highlights.
AI has transformed the media and entertainment industry, from personalization of content to predictive analytics and virtual assistants.
AI-powered algorithms are being used to create personalized content recommendations, forecast performance metrics, and interact with customers. However, there are ethical considerations that need to be addressed, such as job displacement, bias, and privacy.
7. Subtitle generation:
International media publishing companies need to make their content fit for consumption by audiences belonging to multiple regions. To do so, they need to provide accurate multilingual subtitles for their video content. Manually writing subtitles for multiple shows and movies in dozens of languages may take hundreds or even thousands of hours for human translators.
Besides, it may also be difficult to find the right human resources to translate content for certain languages. Additionally, human translation can also be prone to errors. To overcome these challenges, media companies are leveraging AI-based technologies like natural language processing and natural language generation. For example, YouTube’s AI allows its publishers to automatically generate closed captions for videos uploaded on the platform, making their content easily accessible.
To sum it up...
As competition and the need for efficiency continue to rise in the industry, the role of AI in entertainment is only expected to grow in the coming years. By exploring and experimenting with the above and other AI use cases, media and entertainment companies are maximizing their business performance by enhancing the user experience and entertainment value delivered by them with greater efficiency.