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Leveraging Technology To Customize Video Content To Match The Evolving Needs Of Consumer/Audience

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Leveraging Technology To Customize Video Content To Match The Evolving Needs Of Consumer/Audience

Meghna Krishna, Chief Revenue Officer, Toch.ai, 0

She has 14 years of working experience in PR & Marketing Communications and has achieved success in stakeholder management, corporate and product positioning and crisis communication.

Artificial Intelligence (AI) is constantly revolutionizing video production. There is a major shift in the way audience consumes video content. Content relevance, short-form videos, trending content, and actionable video formats are becoming increasingly important to them. And AI helps content creators, marketers, and advertisers efficiently strategize video content as per consumer expectation.

Traditional content development and advertising entailed broadcasting video content to mil-lions of viewers at once, regardless of relevance, making it an ineffective method of reaching the right set of customers. The post-production process, which included audio/video editing, graphics, and brand aspects for brand promotion, was time-consuming. Additionally, the lack of analytics tools made the overall video content production and distribution inefficient.

Today, leveraging technology to customize video content as per changing consumer needs is more realizable. Artificial Intelligence (AI) with Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP) are rapidly impacting all the video production stages, increasingly aligning video content to their audience.

Leveraging AI technology for better videos
Multiple technologies for video content personalization are now being used by content creators, marketers, and advertisers. By handling crucial data management duties, such technologies save two-thirds of video production personnel costs. These AI-based technologies screen content, adding value in the form of metadata and drastically improving the usability and relevancy of the content.

Metadata tagging for rich video content.
Every day, video businesses of news and media, film production, and sports must analyse a massive amount of data for video content. To produce rich video material, the video editing crew would have to look through a variety of videos and categorise the content according to the requirements. The resultant content tagging process is labor intensive, time-consuming, and sometimes inaccurate.

AI allows high-quality metadata tagging that improves the content search-ability from among multiple videos. It adds information/tags to moving objects, individuals, locations, or background elements like buildings. As meta-data becomes easily searchable, video editors can filter and extract object/subject-specific data quickly to create relevant video content assets.

Sentiment analysis for emotional video highlights.
Trends show that emotional moments in the videos form the best and highly influencing content for audience engagement. During live events like sports, broadcasters can use this emotional engagement of the audience and player gestures to create authentic video content assets.

AI uses image, speech, and face recognition systems to analyze audience sentiment. It recognizes and interprets facial expressions like smiles and excitement; crowd response like a sudden change in voice and tone to create “heartbeat” moments.

This focus on capturing metadata is instrumental in mining actionable information which helps in:

1. Automation for quick short-form videos.
The attention span of the audience on one specific video
is a rising concern to content creators, marketers, and advertisers. Viewers tend to skip the long-form video and are more interested in shorter more snackable content pieces. This audience response to content, forces content creators to reshape video advertisement strategies completely. Short-form videos with rich content get the highest audience engagement and AI can help in producing it at a scale with an increased speed to market.

2. Real-time graphic editing for improvised event visualization.
Graphic elements, animations, and computer-generated characters (CG) are useful to make videos more entertaining and attractive. In sports especially, content broadcasters may require adding real-time graphics or CG characters to recreate event occurrence on an assumption basis; explain highlights like player/ball movement like foul, offside.

Audience needs have constantly been changing over the years. From television sets to streaming video content directly on their devices, the audience has shifted their choices and preference


AI can generate real-time graphical statistics for the audience’s consumption. It also simplifies tracking every individual player's movement on the field, making for a more immersive experience for the viewer as they can view the game from unique perspectives beyond the single-camera stream. Cognitive technology integrates data-driven assets and visual effects in re-al-time to create beautifully designed content. More-over, marketers can insert brand elements like logo, brand slogan, and more for promotional purposes.

3. Audio interpretation for video transparency.
Brands are increasingly looking for audio enhancement in video content. Consumers have started streaming videos, movies, and sports in their preferred language. Thus, targeting an audience world-wide is successful only when the content delivery is in the most common or local languages. This shift has brought the necessity of adding language options for captions, audio, and subtitles.

The viewer perceives the transparency of video content and understands it without interruption when audio interpretation is used. A strong deep learning neural network algorithm helps AI transform voice to text. The ability to automatically translate any audio into multi-language subtitles aids with regional video targeting. Live transcriptions, captioning, and language translations are all automated using a neural network algorithm.

AI automates major time-intensive tasks which involve frequent manual inputs. It minimizes human intervention and long post-production activities like video editing. With metadata tagging and contextualization, extracting desired content pieces become faster. Clipping the rich data-led video content and converting it into small bite-sized videos is easy. The right use of technology allows content users to easily engage with their audience with multiple short-form videos like reels, teasers, and highlights.

Conclusion
Audience needs have constantly been changing over the years. From Television sets to streaming video content directly on their devices, the audience has shifted their choices and preference. To service their changing needs, content creators need to leverage technology at its best. The derivatives of AI mold video content strategy as per evolving needs of the audience.

The facial recognition system identifies facial expressions to make emotional content. Metadata tagging enables rich-video data extraction. Real-time graphic editing is useful for promotional purposes and drives more engagement. A speech-to-text conversion system enables better video understanding to a multi-lingual audience. And for such artificial intelligence proves as a one-stop solution making the video production process smarter, agile, and more efficient.

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