Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Abstract

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The segmentation could be on various aspects such as demographics, historic behavior or preferences based on the use cases. In this paper, historic retail transactional data is used to segment the customers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm. This helps in calculating the futuristic value a segment or a customer brings to the business. Strategic marketing designs and budgeting can be implemented using these results. The study is specifically useful for large scale marketing in domains such as e-commerce, insurance or retailers to segment, profile and measure the customer lifecycle value over a short period of time.