Method and apparatus for analyzing merchandise data

By using customer value analysis and geographic equilibrium clustering algorithms, customer tags and product similarity matrices are generated, solving the problems of inaccurate customer profiling and unintelligent product recommendations in traditional retail. This optimizes product structure and inventory management, and improves retail operational efficiency and customer satisfaction.

CN122114976APending Publication Date: 2026-05-29RICHFIT INFORMATION TECH +1
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
RICHFIT INFORMATION TECH
Filing Date
2024-11-29
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

In traditional retail models, inaccurate customer profiling, unintelligent product recommendations, low store operational efficiency, and a lack of dynamic adjustment mechanisms lead to significant discrepancies between product supply and consumer demand, resulting in severe inventory backlogs.

Method used

Based on a customer value analysis model and a geographically balanced clustering algorithm, customer tags are generated, a product similarity matrix and business district tags are constructed, and a product recommendation list is generated by comprehensively considering multi-dimensional information, thereby optimizing product structure and inventory management.

Benefits of technology

It enabled precise product recommendations, improved customer satisfaction and store operational efficiency, reduced inventory backlog, and allowed for dynamic adjustments to adapt to market changes.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122114976A_ABST
    Figure CN122114976A_ABST
Patent Text Reader

Abstract

The application discloses a kind of commodity data analysis method and device, the method includes: based on customer value analysis model, by the quantitative analysis of the transaction behavior of different customers in different merchants, determine customer value value;Different customers are clustered according to customer value value using clustering algorithm based on geographic balance;According to clustering analysis result and customer occupation image, the customer label of different customers is generated;According to the commodity attribute data and commodity transaction record of merchant, the commodity similarity matrix of different merchants is constructed;According to the transaction data and business circle data of merchant, the merchant is classified, and the type label and business circle label of different merchants are determined;According to the customer label, commodity similarity matrix, type label and business circle label, the store commodity recommendation list of merchant is generated.The application is used to improve the accuracy of commodity recommendation and customer satisfaction, optimize the commodity structure of merchant, realize the fine management and personalized recommendation of merchant.
Need to check novelty before this filing date? Find Prior Art