Tagging System Overview, Construction Methodology, and Quality Assessment

A tag is a concise feature identifier created by applying algorithms to objects based on business scenarios, used for labeling, classification, and feature extraction. Tags can be physical (e.g., product labels), network (keywords for content organization), or electronic (RFID tags).

The tag taxonomy groups various business-required tags, defines their attributes, and facilitates management and maintenance. It includes two parts: tag category taxonomy and tag content information.

In e‑commerce, tag categories are organized by business scenarios, such as product attributes, interaction behavior, supply‑chain attributes, and value dimensions. User tags form the basis of user profiling, which aggregates social attributes, consumption habits, and preferences to create a comprehensive user portrait.

Tag systems are applied in DMP (Data Management Platform), CDP (Customer Data Platform), recommendation engines, user profiling systems, and data middle platforms. They enable data collection, integration, labeling, audience segmentation, and support targeted marketing and personalized services.

Construction principles for a tag system include: aligning tags with specific business scenarios, enabling self‑service tag generation with reusable rules, and establishing efficient unified output interfaces. The architecture consists of three layers: data processing, data service, and data application.

Design steps involve identifying product lines, business objects, and associated data/behaviors, then classifying tags according to object attributes while adhering to the MECE principle. Tag hierarchy should be limited to three or four levels, with no more than ten top‑level tags.

Quality assessment of tags covers three dimensions:

Data quality: accuracy, coverage, and stability of tag values.

Application quality: usefulness of tags in product features, measured by usage frequency, heat, and call counts.

Business quality: impact on business outcomes such as ROI, evaluated through real‑world deployment results.

Effective tag quality evaluation combines data and application quality, followed by monitoring business performance after deployment to guide continuous improvement.