Artificial Intelligence
Drive Video Intelligence across the Content Value Chain with AI-led Metadata Tagging
Oct 18, 2022
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In March 2022, Americans spent roughly 20 billion hours watching video content. In other words, 61 hours of video content were consumed per capita. Today, the global streaming software market is valued at $170 billion and is slated to grow with a 12.1% CAGR until 2028. As a result, it will be worth $932 billion by the end of the prediction period.
The rising tide of video content is not a recent phenomenon. Video content permeates every aspect of our lives across geographies and demographics, and the upward trend is intuitively in line with our experiences as an audience. As the media and entertainment industry grows, the need to understand viewership trends and patterns is in rising demand. Technologies that emphasize viewership-based personalization are driving both engagement and monetization.
According to a Deloitte report in 2021, subscription churn averages around 30% in the USA market. As a result, content creators/producers need to provide more personalized ways to appeal to consumers and stay ahead of the competition. Media houses must aim to speak to audiences by analyzing and acting on data based on viewership patterns.
Advertising forms an integral part of the M&E industry. Significant resources are invested to advertise and promote brands on streaming platforms. In order to assess the impact of commercials based on parameters such as brand visibility and its corresponding impact on revenue, considerable time and effort are required by way of human annotation of brand-specific information. As the need for time-saving processes gains ground, it is imperative for decision-makers to automate processes and reduce manual efforts.
In today’s economy, ads are considered an unpleasant interruption in the viewing experience with over 66% of people choosing to skip online video ads. However, that is not true for contextual advertising. Video intelligence enables OTT platforms to target specific moments in line with the content and prevent tone-deaf ads. These include the following use cases:
Custom-built AI/ML video intelligence solutions assist in boosting the effectiveness of contextual advertising, enhancing the productivity of content storage teams by automatically detecting duplicate videos and efficiently managing content archives. Content hyper-tagging is vital for effective management as it makes searching content easier by using metadata tags. Additionally, it can help monetize archived content and improve its reusability, drive deeper business intelligence, provide accurate financial reporting, and create new user experiences. Custom-built AI can further help generate relevant and contextually rich metadata by combining video intelligence with business needs.
Video intelligence involves combining computer vision with business acumen to solve niche media industry challenges. Depending on the content – TV Shows, News, or Web series – AI offers a contextual description of content through metadata and uses this understanding to solve the content objectives.
Metadata is a type of descriptive data that helps a person or computer identify the characteristics of a file. Simply put, metadata refers to people, objects, locations, dialogues, keywords, or any useful concept that meaningfully describes the constituents of content. While metadata is an important aspect for all types of content and files, its true benefits are observed with comprehensive video metadata for video content.
With the recent developments in AI and cloud technology, businesses can now process content at scale. Media houses can generate highly granular time-coded streams of metadata that explain the content structure with the desired details. This metadata is further used to create a meaningful taxonomy of content entities. However, it is necessary to understand that video intelligence isn’t only about generating volumes of metadata. Its purpose is to create meaningful metadata that describes your content per the desired use case. The effort of metadata creation must justify its business utility.
There are three main types of metadata, each with a slightly different use case and often focused on content accessibility:
Media houses can leverage the capabilities and opportunities provided by metadata tagging to enable powerful content discovery and engaging video content experiences. Let’s take a look at some of the applications of metadata tagging:
Quantiphi has combined computer vision, deep learning, and AI techniques to develop a bleeding-edge metadata tagging solution to make content more discoverable.
Success Story: Enriching Content Archives with Searchable Video Metadata
Problem Context: Sportscast, a subsidiary of Deutsche Fußball Liga (DFL), needed AI-enriched metadata to archive decades of football content.
Quantiphi Solution: Quantiphi developed a bespoke video intelligence platform to generate editorial-worthy moments in the DFLs content library.
Impact:
Quantiphi’s cutting-edge AI capabilities across the media value chain empower content owners with ample tools to innovate and differentiate. Our media intelligence offerings help media houses seamlessly adopt AI into their workflows and improve productivity, produce higher quality content, streamline operations, uncover deep content insights and unlock scalable revenue-generating business models.
Get in touch with our experts to learn how media houses can leverage AI-led video intelligence to maximize value creation from their video content.
