Everything You Need To Know About AI-Driven Auto-Tagging In 2023

Tagging is the process of assigning labels or keywords to items, such as images, articles, or videos, to categorize, organize, and retrieve them more efficiently. It is widely used in various fields, including social media, websites, and content management systems, to improve searchability and navigation. On Martech Zone, for example, tagging provides superior internal search results, improves the relevance of related posts, as well as lists the relevant articles with respect to each acronym on the site.

Since Artificial Intelligence (AI) has become an integral part of asset tagging and, in general, multimodal asset-recognition systems, its leverage for content creators and marketers has become increasingly significant. Advanced auto-tagging allows marking assets to be visible and accessible and without it, you can not properly store, retire and reuse your assets. 

How Auto-Tagging Works?

Auto-tagging is a popular feature that allows assigning particular tags to assets of a specific or any modality. In other words, the feature allocates assets, analyzes, and assigns all required tags. Naturally, the capabilities of the auto-tagging systems will directly rely on the AI that powers them. The more simple versions are able to deal with a single modality – visuals and pictures, text, audio, video, etc. In its most powerful deployments, multimodal AI technology can tag various types of media using different techniques and algorithms tailored to the specific characteristics of each medium:

• Text: For textual content, AI-based techniques like natural language processing (NLP) and machine learning algorithms can analyze and identify keywords, phrases, and topics within the content. Methods such as topic modeling, named entity recognition, and sentiment analysis can help generate tags that represent the content’s primary themes and elements.
• Images: AI can use techniques like computer vision and deep learning algorithms such as convolutional neural networks (CNN) to analyze and identify objects, scenes, and patterns within images. Image recognition and object detection models can generate tags based on the identified elements in the images. For example, an AI system might tag an image containing a dog, a park, and a ball with tags like dog, park, and ball.
• Audio: For audio content, AI can use techniques like speech recognition to convert spoken words into text and then use NLP and machine learning algorithms to analyze the text and generate tags. Additionally, AI can identify features like music genre, mood, or specific instruments by analyzing audio patterns and characteristics.
• Video: AI can tag video content by combining techniques used for audio and image tagging. AI can analyze the visual elements in video frames using computer vision and deep learning (DL) algorithms, while speech recognition and NLP can help process and tag the audio content. AI can also detect and tag specific actions, events, or scenes within the video, based on the analysis of both visual and audio components.
• Mixed media: For content containing a combination of different media types, AI can use an integrated approach to analyze and tag each component separately and then generate tags that represent the entire content. This process may involve using NLP for text, computer vision for images, and speech recognition for audio.

Today multimodal AIs, which are capable of analyzing different information, are becoming more and more advanced within top-shelf solutions that are already utilized in it. With the help of a more advanced AI, you can reach higher precision in auto-tagging and gain more options for tweaking the system. Also, due to ML algorithms, it is possible to gain even better results with tagging automation.

How Auto-Tagging Is Used By Content Producers

But why do we need to tag our assets after all? Why not just leave them as they are, storing them in the corresponding folders? Because this can’t be the case today when dealing with an extensive exchange of information that requires huge amounts of customized content. In order to reduce the time of production, marketers have to stick to template-oriented, modular content practices and, of course, automate a number of processes as well.

Global pharma companies frequently have to deal with a large amount of content, content that has to be stored, reused, reviewed, and retired. Every little piece of content has its particular value, and tagging helps us to highlight it. However, auto-tagging does not give 100% results, normally showing precision at the rate of 80% – 90%. Some work is still assigned to the human operator and yet the speed and capacity of machine tagging, which is performed almost instantly, completely justifies the invested effort.

From my own practice, a properly tagged asset base can make a significant change.

1. Tagging allows easy search and access. Whether all assets are located within the centralized system, the administrator is able to set the access levels for different users based on asset access tags.
2. Tagging strongly encourages content reuse since, as a practice, it perfectly secures your assets from being lost. At the same time, your writer and designer get direct access to all creatives relevant creatives that can be reused.

So you already see how auto-tagging contributes to global content consistency, making all assets visible and providing information about their features. When we are talking about consistency, we also frequently assume certain coherence across all markets. And it is namely advanced tagging that helps to identify required content for localization across new markets.

Benefits of AI-Driven Auto-Tagging

AI-based auto-tagging has a number of benefits:

1. Efficiency: AI algorithms can analyze and tag content at a much faster pace than humans, making it more time-efficient, especially for large data sets.
2. Consistency: AI-driven systems can maintain a consistent tagging structure, reducing the likelihood of duplicate or missing tags and ensuring a standardized approach across a dataset.
3. Improved searchability and discoverability: AI-generated tags can enhance search functionality, making it easier for users to find relevant content based on their interests or queries.
4. Real-time analysis: AI systems can analyze and tag content in real-time, keeping the tagging system up-to-date and allowing for prompt categorization of new content.
5. Context-awareness: Advanced AI algorithms can understand context and semantics, enabling more accurate and meaningful tags that better represent the content.
6. Scalability: AI-driven auto-tagging can handle large volumes of data, making it suitable for businesses and platforms that generate or curate massive amounts of content.
7. Multilingual support: AI algorithms can process and tag content in multiple languages, enhancing accessibility and discoverability for users worldwide.

Keep in mind that AI-driven auto-tagging is not perfect and may occasionally generate irrelevant or incorrect tags. Continuous improvements in AI algorithms and training data can help mitigate such issues and enhance the accuracy and reliability of auto-tagging systems.

Auto-tagging In The Focus Of Different Approaches

Generally, auto-tagging is only one of the features that define the modern pharma-oriented MarTech landscape. Of course, it can bring many benefits for marketers as a standalone solution or feature. The true potential can be discovered only in a bundle with other cornerstone marketing approaches such as a modular approach, automation, omnichannel approach, and, of course, the involvement of advanced AI and ML.

• Auto-tagging and modular approach – The modular approach is widely applied in pharma marketing, allowing content creators to use pre-stored modules for creating new original pieces of content. In this context, we can talk about any type of asset – parts of written text, visuals or design, etc. Most importantly, auto-tagging allows pharma businesses to save time for MLR approval by reusing of pre-approved modules and, in general, creating a more agile content management system that allows swift communication between all creators and managers.
• Auto-tagging and omnichannel approach – The omnichannel approach in marketing assumes extensive usage of different media channels for communication with customers, instead of limiting your scope of channels to the most advantageous ones. Naturally, the omnichannel approach requires a more complex content strategy and the introduction of new channels, which will require some additional effort from content creators. Also, auto-tagging helps to tag assets as channel-specific and arrange assets by campaign type, medium, customer cluster, etc.
• Auto-tagging and automation – Automation is one of the biggest trends of today in marketing and auto-tagging is one of the easiest, yet most useful, examples of technology implementation. The problem with operator-led tagging is not only limited to the issue with the capacity of the assets, because sometimes it may take a year for a human operator to review and tag all assets in the company library. Needless to say that the arrival of new assets is a continuous process, so the new assets will constantly supplement the library. Also, human interference itself can become an issue when we are dealing with enormous knowledge bases. And once again, the involvement of different operators over the course of time can cause a real mess in your asset’s library.

Today AI-based solutions are becoming more and more lucrative, allowing automation, high-precision asset recognition, and involvement of machine learning which also allows reducing the involvement of human operators drastically. Auto-tagging engine has a pivotal role within your MarTech tools bundle, as it allows asset management at a capacity that can’t be covered by human operators. Only with the help of metadata assigned to every single asset, the company can realize proper storage of assets and obtain the technical capacity to implement other advanced approaches.