How can media-driven organizations start generating value with AI and machine learning?
We now live in a content economy that is fueled by digital media. It is everywhere. There is so much media being generated every day that it’s become impossible for anyone to manually manage their media operations or focus on extracting the most value out of their owned content.
Consider this: for every minute of the day, there are 1M Twitch videos, 527,760 Snapchat photos, 277,777 Instagram photos, and 4.8M Giphy gifs generated and 4.5M YouTube videos and 694,444 hours of Netflix videos watched. It’s not just the media and entertainment industry that’s profiting from media content. Today, everyone from influencers and end users to consumer brands are monetizing and distributing massive volumes of media through channels like social, digital media publications, and streaming services.
In a fight for screen time, nearly every organization has created their own digital media supply chain in order to create, manage, and distribute digital media content. But without thoughtful process automations and a metadata tagging system, these media pipelines won’t scale. They’ll run out of human resources needed to review and label petabytes of content. Valuable assets will get lost in digital asset management systems and sponsorship opportunities will go missed. To manage this challenge, modern brands and media companies have figured out how to implement AI—specifically, machine learning (ML)—to scale their media operations and start extracting more value out of their media libraries.
Nearly every part of a media management pipeline relies on some type of metadata, so automating metadata tagging using machine learning is a great use case. A well-managed metadata system has the ability to connect siloed asset management systems, making the content discoverable, reusable, and more valuable across the entire organization.
But what’s a good place to start? Below are examples of high-value ML applications commonly adopted in digital media management.
1. Metadata tagging
Nearly every organization spends hundreds of thousands of hours manually reviewing images or videos, labeling them frame by frame, and classifying their assets so that they can be managed, distributed, and monetized. This is typically a repetitive task that is best scaled using ML models trained to detect and label things like objects, faces, logos, or products. ML-automated tagging can convert underutilized media content into monetizable content and free up human resources, so that those resources can be reserved for more high-cognitive tasks.
2. Visual similarity search
Intelligent visual search is an excellent application for e-commerce, media production studios, and global marketing teams who are constantly creating and re-purposing their media content. If you’ve ever been on a commerce site that suggests visually similar items or used a drag-and-drop visual search in a digital/media asset management system, it’s very likely that computer vision models are powering the similarity search.
3. Content moderation
With the proliferation of media distribution channels and content authors like social media influencers and end users, it’s impossible for any organization to manually monitor the media content that is being associated with—or is damaging—its brand. Most machine learning providers offer off-the-shelf ML models trained to detect explicit or NSFW content, so that organizations can scale content moderation and catch high-risk material before they negatively impact its brand.
4. Product placement/embedded marketing
By layering ML models for logo detection, object detection, and speech-to-text, a brand can automatically detect the frequency of their brand or product appearance in a feature video. ML can also be used to auto-detect specific scenes that are ideal for product placement and sponsorships. Conversely, the same methodology can be used to detect and manage unwanted or unlicensed usage of your brand properties.
5. Archival content monetization / re-use
Few companies will retroactively make the effort to embed metadata in their archived meta. That’s petabytes of lost opportunity and a sure way to waste money recreating media assets they already own. By processing archived media through ML, teams can annotate videos and images with contextual information and start converting forgotten media archives into metadata-rich, monetizable assets.
Interested in learning about AI/ML applications for your company? Or have ideas that you’d like to discuss with the Boon AI team? Talk to us! Don’t have data science resources? Boon AI is a low-code machine learning integration platform that enables teams to start implementing machine learning in days, without data science expertise. Think of it as a way to integrate ML capabilities into your DAM, MAM, or media management workflows through simple API integrations.
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