Customer portrait matching and positioning method and system

By constructing a multi-timescale profile structure under a unified time base and introducing a dynamic decay mechanism, the problem of insufficient accuracy and stability of existing customer profile matching schemes is solved, achieving more accurate customer positioning and interpretable matching results.

CN121280084AActive Publication Date: 2026-01-06BAIWEIJINKE (SHANGHAI) INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511862698.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-11
Publication Date
2026-01-06
Estimated Expiration
2045-12-11

AI Technical Summary

Technical Problem

Existing customer profiling matching solutions struggle to distinguish between long-term structural characteristics and periodic concentrated bursts of characteristics, and lack a dynamic decay mechanism for time factors, resulting in insufficient accuracy, time stability, and interpretability of the matching results.

Method used

Under a unified time base, behavioral time series and label time series are constructed to form a multi-time scale profile structure. The label time instability index and the phased behavior proportion index are introduced to calculate comprehensive analysis indicators. The multi-time scale profile and fusion weight are adjusted in combination with feedback event sequences to achieve time-related dynamic decay.

Benefits of technology

It improves the accuracy and robustness of customer profile matching, enhances time stability and adaptability, strengthens the interpretability of matching results, forms a closed-loop optimization mechanism, and reduces reliance on manual parameter tuning and empirical rules.

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Abstract

The invention discloses a customer portrait matching and positioning method and system, and particularly relates to the technical field of portrait matching and positioning, and the method comprises the steps: carrying out the cleaning and time correction of multi-source behavior data under a unified time base, and constructing a behavior time sequence and a label time sequence; on this basis, identifying a tag high-intensity activation interval, and forming a multi-time scale portrait structure composed of a long-term structural portrait, a life cycle stage portrait and an event portrait; for business task analysis time sensitivity and risk tolerance, calculating a comprehensive analysis index to judge whether time-dependent dynamic attenuation is introduced, respectively obtaining scores of all portrait layers, fusing the scores according to weights, generating a comprehensive matching score, and outputting a target customer set; and combining recommendation, marketing or risk control feedback to construct a feedback event sequence, and adjusting the multi-time scale portrait and the fusion weight. According to the method, long-term features, stage behaviors and short-term events can be distinguished, and the accuracy and interpretability of customer portrait matching and positioning are improved.
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