Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Abstract

Recommender system is a computer-based intelligent technique which facilitates the customers to fulfill their purchase requirements. In addition to this, it also helps retailers to manage the supply chain of their business and to develop different business strategies keeping in pace with the current market. Supply chain management (SCM) involves the streamlining of a business’s supply-side activities to remain competitive in the business landscape. Maximizing the customer value is another important activity of SCM to gain an advantage in the market. In this work, the K-Means clustering algorithm has been used for the effective segmentation of customers who have bought apparel items. PCA has been used for dimensionality reduction of different features of products and customers. The main focus of this work is to determine the different possible associations of customers in terms of brand, product, and price from their purchase habits. The result shows that the clusters made by the algorithm based on PCA and K-Means are similar and the results are acceptable on the basis of feedback received from existing customers and satisfies the customers’ requirements based on the amount of money (price range) the customers want to spend while doing online shopping. The features of products purchased by customers were combined together to generate a unique product key for business, and a model was prepared to segment products based on the volume of products sold and revenue generated, and the price of products sold and revenue generated. This work, in the long run, will help business houses to build a sustainable, profitable, and scalable e-commerce business. Environmental, social, and economic aspects are important to make e-commerce more sustainable for the benefit of the society.