Over time, a business accumulates media files like photos and videos for sales products and marketing campaigns. Without proper storage and metadata tagging, these contents can get lost in the archives, and pulling them up manually will take time and resources for the business.
Artificial Intelligence (AI) allows automated content tagging systems for a more accurate and efficient search. It contextualizes digital assets and increases their findability according to keywords, tags, and metadata descriptions.
The emerging system of AI auto-tagging
The emergence of digital marketing and branding called the need for companies to establish Digital Asset Management systems (DAM) as a single place to store valuable digital properties. However, as businesses expand, their digital assets accumulate as well.
The growing volume of digital assets needs the power of AI to help assist queries and make each search process significantly faster.
Saturated media libraries cost storage space, not to mention the amount of time consumed going through the archive for a specific file. Today, AI assists in filtering and categorizing media files with improved findability using only a few keywords.
Machine learning is a type of AI that makes auto-tagging possible. Through predictive analysis and programmed algorithms, it can easily find media files with the tagged keyword no matter how numerous the stored photos and video files are.
Using computer vision, AI improves computer capacity to create contextual information from digital content like images and videos.
Aside from internal media, AI auto-tagging can also help in online communities because it can scan similar images that help users identify the form of the content. The best thing about AI auto-tagging is how quickly it can fetch relevant results after each query.
Nowadays, AI auto-tagging is not only a practice but also a business opportunity for AI software developers to develop innovative solutions to offer to businesses. Outsourcing companies may also tie up with tech firms to facilitate media asset tagging.
Challenges of manual tagging
Manual tagging for media files is subject to human error, differences in judgments, and contexts. It can cause problems and disorganization in libraries and, worse, in a page’s search engine results.
Limitations in the workforce also cause delays in task completion, taking more time and resources for the team. Chances of mistakes in categorizing and labeling are higher with manual tagging.
Automated metadata tagging
Metadata is information about the primary data stored in the DAM. It may contain creation and modification dates, authors, file size, type, and other classifications the organization needs to use.
The growing topics, key terminologies, and trends in digital marketing pollute the content management systems. Heavy clerical workloads with slightly unidentifiable differences are also overwhelming for a human.
Automated metadata tags using a machine learning approach produce refined search results and insightful recommendations. Thus, it promotes more contextualization and findability within the library and platforms.
With keywords in the metadata, search queries are faster and spot on, cutting down resource costs. And with a system dedicated to standardizing the storage techniques, assets are easily searchable for all organization members.
Automated metadata tagging is beneficial not only for existing media assets but also for storing the most recent ones.
When and where to use AI auto-tagging
The continuous accumulation of media files across all channels can become overwhelming for human content managers. This is especially challenging for digital marketing, production firms, and social media platforms.
Media managers and analysts use AI auto-tagging when creating metadata for their content. It is integrated into tools and software that groups can use for searchability.
Furthermore, online stores and pages may also benefit greatly from auto-tagging in making their products and services easy to access using only a few keywords. This may enhance their omnichannel approach to digital marketing, making platforms seamless and user-friendly.
To start using AI auto-tagging, content managers and publishers must decide on their keywords and categorization practices. Afterward, they can cascade the process outlines to media analysts, content executives, and content creators.
Outsourcing web and software development teams is also an option when a business wants to create its own content tagging system with AI. Existing media libraries can be restructured with a more cohesive and methodical repository strategy.
What AI auto-tagging can do for your business
The speed and accuracy of AI for auto-tagging help businesses accelerate their functions. For instance, looking for digital content for marketing campaigns is done in milliseconds, thanks to the augmented findability and correctness made possible by AI auto-tagging.
When it comes to optimizing customer touchpoints, investment in AI auto-tagging technology can help by:
- Promoting a positive customer experience for a company’s marketing campaign
- Increasing findability within the platform as well as internal libraries
Auto-tagging with AI also strengthens publishing efforts on a brand’s webpage by classifying contents using metadata descriptions as the basis for the results. AI-powered recommendations and keyword analysis with automated data tagging increase the page’s SEO performance, driving more visibility and traffic to the site.
