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Label Clustering
Quick Insight
360° Depiction
Ruihedata User Profiling Solution adopts social network analysis, fuzzy matching, aggregation & clustering, and other advanced technologies to divide customers into groups according to their tags. This helps frontline banking staff develop insights into customer needs more quickly
Advanced algorithms such as feature engineering are applied to establish a rich and multi-dimensional customer tagging system that covers a full spectrum of dynamic information such as customer attributes, financial attributes, trading behavior, product preference, interests & hobbies and business scenario. This ensures constant flow of real-time data, precision of AI algorithms, millisecond-scale export of customer tags, and continuous reiteration and optimization of tag algorithms, hence “growing precision” in customer profiling.
With finer granular in its user value system, the module sub-divides customer groups by multiple dimensions and levels and visualizes customer profile in a panoramic and dynamic manner.
User-defined sub-division into multiple combinations and levels can flexibly respond to the needs of various business scenarios, making targeted marketing more precise, effective and efficient.
Life-cycle management closes the loop for tag management. It automatically accumulates tag assets, empowers flexible despatch according to business needs, and facilitates rapid generation, application and optimization of user profile and precise marketing strategies & scenarios.
Greater precision in marketing makes marketing content less disturbing. It ensures supreme customer experience while gaining deep insights and prediction of potential needs.
By granting their business staff access to the automatic analysis and production capability for tagging and profiling, our clients can relieve IT engineers of maintenance burden, make marketing analysis more effective, automated and flexible, save the cost of conventional maintenance service, and better align data to business needs.