A tag management system helps manage the lifecycle of digital marketing tags (sometimes referred to as tracking pixels or web beacons), used to track activity on digital properties, such as websites and web applications. It can also be used to make dynamic changes to the website or application.

Tag management system replaces a multitude of tags with a single container tag which sits across all areas of the property. The tag management system then "fires" individual tags as appropriate based on business rules, navigation events and known data. Typical functionality includes a testing environment (sandboxing), an audit trail and version control, the ability to A/B test different solutions, tag deduplication, and role-based access to data.

According to W3Techs survey, as of November 2023, Google Tag Manager constitutes 99.7% market share, followed by the Adobe DTM and Tealium with 0.4% and 0.2% market share respectively.^[1]

Artificial intelligence (AI) and machine learning (ML) are changing tag management systems by automating the tagging process, improving accuracy, and facilitating content organization. AI-driven tagging systems analyze unstructured data—such as images, videos, and documents—to assign relevant metadata tags. It makes the content more searchable and accessible. For example, NASA developed an automated tagging system using machine learning and natural language processing to improve information accessibility and promote data reuse. ^[2]