The PERCENT's Tag Works is a one-stop tag management middle platform that integrates tag management, definition, production, analysis and application. It builds a tag system based on data assets, helps the enterprise convert technical data into business tags, and realizes the visibility, understandability, usability and operability of data, which is the cornerstone of fine operation and precise management of the enterprise.
It supports cluster mode and single node mode, and supports single node installation and deployment at least.
The system architecture is open, and the metadata of tag definition and generation results are transparent and visible, which supports the customers or partners to carry out secondary development based on the tag system, and easily build an industry-level tag application system.
It integrates business rules and technical rules of tags, and defines automatic production in one step without any technical thresholds. The business personnel can flexibly define tags according to their own needs, which greatly improves the efficiency of tags generation and operation.
The global business data does not need to be uniformly connected to the tag system, and supports cross-source data modeling, which saves storage resources and reduces implementation costs.
It supports cluster mode and single node mode, and supports single node installation and deployment at least.
The system architecture is open, and the metadata of tag definition and generation results are transparent and visible, which supports the customers or partners to carry out secondary development based on the tag system, and easily build an industry-level tag application system.
It integrates business rules and technical rules of tags, and defines automatic production in one step without any technical thresholds. The business personnel can flexibly define tags according to their own needs, which greatly improves the efficiency of tags generation and operation.
The global business data does not need to be uniformly connected to the tag system, and supports cross-source data modeling, which saves storage resources and reduces implementation costs.
It supports cluster mode and single node mode, and supports single node installation and deployment at least.
The system architecture is open, and the metadata of tag definition and generation results are transparent and visible, which supports the customers or partners to carry out secondary development based on the tag system, and easily build an industry-level tag application system.
It integrates business rules and technical rules of tags, and defines automatic production in one step without any technical thresholds. The business personnel can flexibly define tags according to their own needs, which greatly improves the efficiency of tags generation and operation.
The global business data does not need to be uniformly connected to the tag system, and supports cross-source data modeling, which saves storage resources and reduces implementation costs.
For users and systems in different business domains, it provides data retrieval, downloading and service permissions control, and supports permissions control at tag level and ID level, which ensures the data security and controllability to the greatest extent.
Tag results automatically generate API , and supports third-party systems to quickly utilize tag and group results, and supports three sharing modes of API, file synchronization and database synchronization.
The system can build knowledge models and operate management for users, products, channels and other entities of enterprise and their relationships.
Based on the tag results, it displays the user portraits in all directions, including user attribute tags, statistical tags, algorithm tags, and various behavior tracks, helping the operators to understand, perceive and serve users in a more accurate manner.
It defines tags generation rules in full visualization, and easily builds multi-entity tag systems without needing any technical basis. It supports enumeration tags, qualitative tags, statistics tags, behavior preference tags and combination tags set according to business rules, and supports tags generation for external data files.
It supports selecting attribute class, statistics class and behavior class meta indicators from multi-source database to form tags generation metadata set, and users can set tag business rules and set production plans based on the data-focused indicator system, without concerning the storage, calculation and fusion of original data.
For users and systems in different business domains, it provides data retrieval, downloading and service permissions control, and supports permissions control at tag level and ID level, which ensures the data security and controllability to the greatest extent.
Tag results automatically generate API , and supports third-party systems to quickly utilize tag and group results, and supports three sharing modes of API, file synchronization and database synchronization.
The system can build knowledge models and operate management for users, products, channels and other entities of enterprise and their relationships.
Based on the tag results, it displays the user portraits in all directions, including user attribute tags, statistical tags, algorithm tags, and various behavior tracks, helping the operators to understand, perceive and serve users in a more accurate manner.
It defines tags generation rules in full visualization, and easily builds multi-entity tag systems without needing any technical basis. It supports enumeration tags, qualitative tags, statistics tags, behavior preference tags and combination tags set according to business rules, and supports tags generation for external data files.
It supports selecting attribute class, statistics class and behavior class meta indicators from multi-source database to form tags generation metadata set, and users can set tag business rules and set production plans based on the data-focused indicator system, without concerning the storage, calculation and fusion of original data.
For users and systems in different business domains, it provides data retrieval, downloading and service permissions control, and supports permissions control at tag level and ID level, which ensures the data security and controllability to the greatest extent.
Tag results automatically generate API , and supports third-party systems to quickly utilize tag and group results, and supports three sharing modes of API, file synchronization and database synchronization.
The system can build knowledge models and operate management for users, products, channels and other entities of enterprise and their relationships.
Based on the tag results, it displays the user portraits in all directions, including user attribute tags, statistical tags, algorithm tags, and various behavior tracks, helping the operators to understand, perceive and serve users in a more accurate manner.
It defines tags generation rules in full visualization, and easily builds multi-entity tag systems without needing any technical basis. It supports enumeration tags, qualitative tags, statistics tags, behavior preference tags and combination tags set according to business rules, and supports tags generation for external data files.
It supports selecting attribute class, statistics class and behavior class meta indicators from multi-source database to form tags generation metadata set, and users can set tag business rules and set production plans based on the data-focused indicator system, without concerning the storage, calculation and fusion of original data.
Based on the users' behavior preference, in combination with recommended algorithm, rule engine and effect evaluation system, the accurate matching between people, products and contents is established, and the personalized recommendation of "thousands of people and thousands of faces" is realized, which helps the enterprise effectively improve user activeness and conversion rate and optimize user experience.
It provides retail FMCG enterprises with the full-process products and tools required by intelligent marketing, conducts subdivision management of users, captures the key contacts of users' life journey, responds and stimulates users' immediate behaviors through setting intelligent scenarios, and carries out personalized interaction with users, so as to stimulate users' purchase behaviors and improve repurchase rate.
It gathers the different business models of real estate groups, manages and integrates them according to a unified data standard system, sorts out the user tag systems in line with the development of different business models, establishes a full customer membership operation system, abstracts customers, and depicts user portraits in an all-round way. It conducts fine service and precise marketing for customers based on user portraits.
Based on the users' behavior preference, in combination with recommended algorithm, rule engine and effect evaluation system, the accurate matching between people, products and contents is established, and the personalized recommendation of "thousands of people and thousands of faces" is realized, which helps the enterprise effectively improve user activeness and conversion rate and optimize user experience.
It provides retail FMCG enterprises with the full-process products and tools required by intelligent marketing, conducts subdivision management of users, captures the key contacts of users' life journey, responds and stimulates users' immediate behaviors through setting intelligent scenarios, and carries out personalized interaction with users, so as to stimulate users' purchase behaviors and improve repurchase rate.
It gathers the different business models of real estate groups, manages and integrates them according to a unified data standard system, sorts out the user tag systems in line with the development of different business models, establishes a full customer membership operation system, abstracts customers, and depicts user portraits in an all-round way. It conducts fine service and precise marketing for customers based on user portraits.
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