Consumer Segmentation Based on Location and Timing Dimensions Using Big Data from Business-to-Customer Retailing Marketplaces.

Abstract

Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention. In this study, we use the recency, frequency, monetary, and tenure (RFMT) method to segment consumers into 10 groups based on their time and geographical features. To explore location, we investigate market distribution, revenue distribution, and consumer distribution. Geographical coordinates and peculiarities are estimated based on consumer density. Regarding time exploration, we evaluate the accuracy of product delivery and the timing of promotions. To pinpoint the target consumers, we display the main hotspots on the distribution heatmap. Furthermore, we identify the optimal time for purchase and the most densely populated locations of beneficial consumers. In addition, we evaluate product distribution to determine the most popular product categories. Based on the RFMT segmentation and product popularity, we have developed a product recommender system to assist marketers in attracting and engaging potential consumers. Through a case study using data from massive B2C retailing, we conclude that the proposed segmentation provides superior insights into consumer behavior and improves product recommendation performance.