The past few years have witnessed a dramatic increase in content demand and consumption.
While this is great news for content producers and marketers, there is also cause for concern. Starting from the rapid shift in consumer behavior and the surging competition levels from the rise in streaming services and on-demand viewing options.
This presents a new challenge for content producers and distributors, on how to do things differently in terms of content type, access, and delivery.
The desires of each competing brand is to attract more viewers to their own platform and retain them. This move would require the continuous development of content. The never-ending battle for greater market share will lead to an ever-growing sea of content, making it difficult for viewers to find and choose what they want to watch. Not to mention how long, (if possible), it will take for viewers to go through all of them.
According to Cisco, It would take a viewer more than 5 million years to watch the amount of video that will cross global IP networks each month. By this time over a million minutes or almost 17,000 hours of video content will cross IP networks.
With such a saturated landscape, it is crucial for content producers and distributors to make it easy for viewers to search, find and access their content.
Achieving this task would require the use of high-quality content metadata.
What is the content metadata?
Content metadata is descriptive in nature and provides information such as the title of the content, it’s subject, release date, author, genre, running time and so on.
These sets of descriptive information allow content creators to have a definitive structure for their content and also enables content distributors to release the created content with ease.
Metadata is key because it can significantly improve and has a direct impact on the ability of viewers to search and discover content efficiently.
This provides increased convenience in connecting to the most informative and thrilling entertainment they want at any time.
Discoverability is crucial.
The fast-changing media scene calls for an improved definition and description of content metadata on a more in-depth level.
This is imperative to provide viewers with an easier and faster way to access content anywhere and anytime they choose.
On this note, content distributors will have to adopt an effective plan for creating better content metadata, which also allows for the personalization of individual viewer experience.
Such a task would require some form of advanced technology, and this is where machine learning comes in.
Machine learning and content metadata.
Machine learning is a process in which computer systems analyze vast amounts of data and are capable of performing a specific task without direct instructions, but relying on data patterns and inference.
This subset of Artificial Intelligence can be applied in the analysis of a vast amount of content, enrich metadata, make content easy to find and enable distributors to provide the right audience with the right content at the right time.
The enrichment of content metadata through machine learning goes beyond regular content description and enables an Improved content description with additional descriptive properties like emotions, keywords, on-screen actions, etc.
A shift from the old.
Applying machine learning to content metadata can help to present the most appropriate content in real-time.
Under previous circumstances, viewers would normally depend completely on basic descriptive information to find content.
However, metadata enrichment with machine learning has greatly improved how viewers can search for content, using a variety of search criteria that were previously not possible.
A goldmine for real-time programming.
High-quality metadata can greatly improve the content programming of both digital and traditional content distributors. In traditional television programming format, the broadcaster would need to have a list of what to broadcast several weeks ahead.
But that tactic will be a drawback in today’s environment where trends are largely unpredictable and change rapidly. Hence, the need for a more effective content programming and management system.
Machine learning can effectively recognize real-time trends and instantly point it out for publication before it loses its mass appeal.
Machine learning also improves the programming workflow of traditional broadcasters, through the automated matching of metadata to their lineups of programs. This way contents are tagged without delay and faster than conventional methods.
Delivering value to content creators.
We live in a society heavily dependent on content. The massive surge in content consumption has attracted a vast number of players into the field. Consumers now have more options than ever before. And with an ever-increasing competitive landscape, there is also an increased level of concern amongst content creators. These concerns mostly center on how to get viewers to easily discover and enjoy their content. High returns on investment can only be possible when a substantial number of viewers can easily discover, access and watch their content.
The problem is that current content metadata commonly includes only the title, cast, and summary. Content accuracy and quality, particularly for older content, may also be a factor.
The beauty of machine learning is its ability to analyze vast amounts of information and assign them appropriately to the right content. By so doing, it can make content easy to find and view.
Additionally, to enhance content visibility machine learning improves the content metadata by completing fields omitted and tagging the content with richer and relevant descriptions.
The dynamic relationship between Man and Machine.
Machine learning plays a pivotal role in the development of enriched content metadata. However, there is still a need for human editors to keep watch and monitor their results.
There is no doubt that the manual edition of data is a lot of tedious work combined with limited resources, personnel and time constraints.
This is where advanced technology comes in, to effectively extract valuable information which can then be examined by human editors for confirmation of its authenticity and accuracy.
The goal of machine learning in content metadata is to improve user experience, eliminate any inconvenience to viewers, and ensure content consumption in the most enjoyable stress-free manner.
