SURVEY: RECOMMENDATION SYSTEM FOR WEB PORTAL USING CUSTOMER SEGMENTATION

Abstract: A lot of money has been invested for predicting what the customer wants to buy next and what likely the customer is going to buy now. This opens up another branch called Customer Segmentation. Customer Segmentation is the division of customers into groups depending upon their shopping patterns and trends. Identifying different groups of customers based on several indicators like behavioural, demographic and others is the main goal of customer segmentation. This acts as a powerful tool to satisfy the unmet customer requirements. Every customer base requires a new selling strategy which depends on their buying potential. Verticals of all the industries aim at increasing their customer conversion rate. Our proposed model uses the above concepts along with the data mining algorithms. The ultimate aim of our system is to induce the customer to buy more products and also maintain accuracy in the predictions of what the customer wants to buy.The proposed model is a web based application which helps people to find and buy latest clothes and accessories. Our recommendation system facilitates them in selecting their products faster. The recommendation system is based on the concept of data mining. In this application we have two modules: Customer module and Admin module. The admin module contains the access of admin on the application. The admin can change everything in the application. He has the ability to add, delete, and update any information on the website. The customer can login into the website or can access the website without login also. He can view details of the clothes and accessories on the website. He can also add or remove products from the cart. After buying the product the customer can make payment through cash only.

Keywords: Customer conversion rate, potential to up sell, clustering, data mining, analytics, recommendations.