Clustering Based Customer Segmentation Using Automatic Billing System for Smart Shopping

Abstract

Today with the coming of digital mode based online shopping it is very convenient for a customer to book the items online and get delivered it at home so in order to revive the culture of offline shopping it is very important to renovate the techniques with smarter techniques of shopping where user need not to waste time standing in line for billing. In order to attract users frequently with more attractive offers it is also important to segregate users as potential customers, target customers etc. on the basis of their purchase. Our proposed method perform clustering analysis based on mall customers on basis of some common attributes like salary, age, buying habits etc. using machine learning algorithms. Our work is divided into two prominent approaches where the first deals with smart shopping where shopping cart are embedded with RFID & ZigBee to store users product and generate online bills and second task deals with analyzing our target customers for future, based on their purchase history with the help of machine learning. This system includes a smart shopping trolley and effective billing software. Our task of clustering customers helps us to find our potential customers and the one whom the store has to motivate by certain offers. One of the greatest benefit of our work is that the shop owners will have to deploy less number of cashiers which would help in decreasing substantial expense. This paper proposes work which helps customers reduce their time standing in long queue for their turn for billing and also our work helps to segment customers based on their buying habits which will help in identifying our potential customers.