Store consumption behavior analysis guiding marketing system based on face recognition

Abstract

The invention provides a store consumption behavior analysis guiding marketing system based on face recognition. The system can comprise a face image acquisition module, an image recognition module, acustomer classification module, a customer grading evaluation module and a payment service module; the face image acquisition module acquires face information and the image recognition module and accurately matches the customer information in the system; the customer classification module automatically stores head portrait pictures of new customers and pushes related customer information to the customer service terminal; if the customer is identified to be a revisited customer according to the face, a linked entrance guard is automatically opened, the customer grading evaluation module formscustomer consumption rating evaluation and credit rating evaluation by utilizing customer information, and service content guidance is performed by utilizing a system recommendation package to promotecustomer service ordering; the payment service module generates an electronic consumption contract, and a client can perform service payment by using payment modes such as face recognition or a two-dimensional code to complete whole service tracking and management. According to the system, shop managers can conveniently carry out comprehensive monitoring and statistical analysis on the operationand sales conditions of shops by utilizing the shop visiting records of the users, and relevant management and marketing means are adjusted.

Description

technical field [0001] The invention relates to face recognition, in particular to a face recognition-based consumption behavior analysis and guidance marketing system for shopkeepers. Background technique [0002] With the rise of e-commerce, the maintenance of customers by physical stores is becoming more and more important. Currently, offline physical stores usually use manual or electronic documents to record customer information. However, the efficiency of traditional manual records and electronic documents to record customer information is low, the degree of differentiation between customers is low, and it is prone to errors. The business introduction and demand follow-up of customers who come to the store cannot be effectively analyzed. At the same time, it is inefficient in satisfying customers' consumption behavior trajectory inquiries. It can only provide customers and merchants with user historical consumption behaviors by looking up record sheets, and cannot ach...

AI Technical Summary

Problems solved by technology

However, the efficiency of traditional manual records and electronic documents to record customer information is low, the degree of differentiation between customers is low, and it is prone to errors. The business introduction and demand follow-up of customers who come to the store cannot be effectively analyzed

At the same time, it is inefficient in satisfying customers' consumption behavior trajectory inquiries. It can only provide customers and merchants with user historical consumption behaviors by looking up records, and cannot achieve targeted maintenance for customers.

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