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Top Applications of AI in Media and Entertainment Industry

Today’s digital landscape demands that the Media and Entertainment (M&E) adopt intense transformations leveraging customer analytics resulting from customer Big Data. The fierce nature of competition and the dynamic marketplace is centered on the digital-literate customer who is the undisputed king. To sustain and grow, it is absolutely necessary that the M&E Industry implement AI as part of their business models.

A case in point is the burgeoning popularity of the entertainment platform Netflix that has adopted AI technology way back in 2016. AI has allowed Netflix customers to enjoy personalized experiences, automatically manage a range of functionalities, and access more personalized content recommendations.

Based on a paper by Netflix’s Chief Product Officer Neil Hunt, “the combined effect of personalization and recommendations save Netflix more than $1Billion per year.

The M&E Industry is driving higher value by raising the quality and quantity of content by leveraging the power of AI technologies in critical areas of operation. Let’s take a look at the various areas where AI is making an impact in the Media and Entertainment industry.

Hyper-targeted advertising

Entertainment companies are now predicting churn rates more accurately using AI and ML technologies. It lets them place advertisements at appropriate timings, and across exact platforms for more personalized offers. Such hyper-targeted offers are possible because of the real-time analysis of customer data from different sources. The ability to interact with customers based on their preferences and choices in advertising and television is based on the concept of addressability. Focusing on particular audience attributes lets AI and ML algorithms automatically suggest the best advertising options.

Targeted content generation using predictive modeling

Predictive modeling of content initiates the creative process, takes it forward, and airs it to target customers. This is the reversal of the conventional process where creative people meet up in a room, ideate, create a pilot, and then apply data to evaluate its performance. The House of Cards, aired on Netflix was claimed to be a runaway success because it was inspired by data first and not the content idea. The show was created based on the data for patterns of preferences. Communicating with subscribers in real-time lets the M&E industry anticipate customer choices, influencing investment decisions.

AI is helping the industry understand and analyze the type of content that is likely to be popular with micro-segment consumers in the near future. In addition, it also predicts platforms where customers are most likely to watch a particular genre of content. AI and ML algorithms suggest script ideas, suggest characters, and write out summaries.

Content Personalization

With colossal content evolving in the M&E industry every minute, AI applications have emerged as saviors by making them easily discoverable by target consumers. The highly competitive entertainment landscape would otherwise make it difficult for interested viewers to find relevant content of their interest. Using AI video intelligence, media producers and their distributors can examine videos, recognize objects, pictures, and append related tags. AI-based video intelligence tools analyze the contents of videos frame by frame and identify objects to add appropriate tags.Consequently, despite innumerable production houses, entertaining hosts, broadcasting, and publishing platforms bringing out content, the right ones become readily discoverable by interested consumers.

Programmatic advertisement buying

Traditionally, ad slot buying was a cumbersome manual process based on audience demography analysis. Despite all efforts, viewership fluidity failed to meet expected levels. AI-assisted programmatic ad buying leverages real-time data and analytics to automate ad purchases across a range of media and broadcasting platforms. This new method continually monitors audience dynamics across multiple channels and responds by purchasing ad space when it’s available.

Music recommendations

Successful music streaming companies like Spotify or Apple Music leverage ML algorithms to classify listeners and recommend playlists that are more relevant to them. AI technologies such as collaborative filtering work to segment songs and their potential listeners. Natural Language Processing (NLP) scraps information about songs and artists from the web, enhancing segmentation. The Convolutional Neural Network (CNN) of AI systems ensures that the system responds adequately to different parameters instead of sticking to mere historical streaming data. In addition, AI is actively assisting musicians to generate songs that are most likely to be popular with the target audience.

Subtitle generation

International media houses are continually aiming to popularize cross-country published content, and this is possible only with the help of subtitles. Generating accurate, multi-lingual subtitles for video content is possible using AI, making the content fit for consumption by a global audience within stipulated timeframes. AI and NLP can readily translate subtitles as close to the target interpretation as possible. It obliterates constraints like finding apt human resources to do the translation and reduces the burden of several thousands of hours needed to translate in dozens of languages.

Summing it up

The Media and Entertainment Industry is getting digitally transformed at a rapid pace and technologies like AI, ML, NLP, and deep learning are playing a key role in this transformation. Traditional methods are fast being replaced by AI-aided technologies that are allowing entertainment houses and their content to be discovered easily. Customizing and curetting content as per audience preference, reaching out to a global audience with relevant subtitles, is a breeze using AI.

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