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Three Reasons Why the Media & Entertainment Industry Should Embrace Machine Learning

Robert Koch

Global Head of Media & Entertainment
  • Media, Entertainment & Telecom

From news and media to cable services and internet providers, media and entertainment companies are faced with the challenge of how to describe, organize and monetize their media effectively as the amount of content being created continues to grow exponentially. Many businesses are turning to emerging technologies and intelligent automation tools to address this challenge. By utilizing machine learning, robotic process automation (RPA) and artificial intelligence, media and entertainment companies can organize and tag metadata to categorize and manage media clips and episodes more quickly and with less human intervention.

Here are three ways that machine learning and intelligent automation tools can help the media and entertainment industry save costs, deliver an engaging experience for users and increase the value of data.

Enhanced Content Discovery

Until recently, most media companies have relied on basic metadata like title, episode, synopsis or keywords to drive discovery, but even this data is too advanced for some archived content. By building out keywords to include props in a scene, specific actors and special guests, as well as themes and emotions, content libraries can be sliced and diced more effectively.

It’s difficult to find the time or resources to pay humans to watch each episode or clip and manually enter this missing or unseen metadata. Instead machine learning, natural language processing and image processing technology can be used to filter structured and unstructured data, so that when Tom Hanks appears in a red Ferrari disgruntled about finding love, all of these keywords are embedded as metadata along the timeline in the clip or episode. This rich metadata can be used to drive enhanced search, power recommendation engines and create new relationships between previously unlinked content, driving deeper engagement with audiences.

Cost Savings

As the trend of consumerism makes its way into every industry, the media and entertainment sector is no exception. Today’s customers expect a personalized experience from their streaming platforms. Media and entertainment companies can no longer deploy a one-size-fits-all approach and need to find an effective way to personalize content for each user’s individual needs and interests. With enhanced metadata and scene level identification of each piece of content from TV episodes to news stories to sports clips, the right content can be placed in front of its target audience.

Machine learning tools enable companies to identify content topics and tag metadata from live or linear streams in significantly less time and effort, compared to editors and producers, as computers sort and classify this content in real time. Leveraging this enhanced metadata enables automated clip and highlight creation and allows users to search more effectively to find those micro-moments that are important to creating a personalized viewing experience.

Monetization Opportunities

In addition to gaining efficiency and improving the user experience, complex metadata and a built-out tagging structure opens a huge opportunity for advertisers to deliver specific, relevant content to their target audience. By leveraging intelligent automation, advertisers can match the right ad to their demographic based on the user’s history on the platform or target a precise moment within a video with a similar sentimental tone to your brand.  With a deep library of content, media and entertainment companies can re-activate their earning potential without incurring the costs associated with manually updating metadata specs to match current standards. This can prepare content for SVOD or even enable deeply interactive experiences to previously flat content.

As the media and entertainment industry continues to navigate digital disruption and content costs continue to rise, companies need to consider how to leverage technology to increase the value of their content and implement a strategy to further monetize their existing assets. Utilizing machine learning and intelligent automation technologies can result in reduced production time and costs, increased content value and ultimately deliver a better user experience.

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