A Systematic Approach to Customer Segmentation and Buyer Targeting for Profit Maximization

摘要

Nowadays, maintaining customer loyalty and attention span of the customers are major challenges faced by the retail industry. This leads to the need for reinforcement of marketing strategies from time to time. This paper proposes a systematic approach for targeting customers and providing maximum profit to the organizations. An important initial step is to analyze the data of sales acquired from the purchase history and determine the parameters that have the maximum correlation. Based on respective clusters, proper resources can be channeled towards profitable customers using machine learning algorithms. K-Means clustering is used for customer segmentation and Singular Value Decomposition is used for providing appropriate recommendations to the customers. This paper also deals with the drawbacks of the recommender system like cold start problem, sparsity, etc and how they can be overcome.