A0959
Title: A data-driven new location recommendation system for sustained revenue growth in retail business
Authors: Subin Jeong - Chungnam National University (Korea, South) [presenting]
Minsu Park - Chungnam National University (Korea, South)
Yong Hyun Um - Chungnam National University (Korea, South)
Mingyu Go - Chungnam National University (Korea, South)
Abstract: The decision-making process regarding new location placement in retail franchising is a pivotal economic strategy where optimal placement influences revenue growth for businesses. Particularly in light of recent economic uncertainties and shifts in consumer behavior, these decisions have become increasingly complex. Consequently, the development of data-driven recommendation systems for new location placements has emerged as a crucial aspect in enhancing economic efficiency and strengthening market competitiveness within the retail sector. The aim is to construct a recommendation system incorporating customer characteristics using panel big data collected through digital platforms. To achieve this, a comprehensive model is proposed that simultaneously considers regional models based on local characteristics and entity-level models utilizing individual customer attributes and behavioral patterns. Given the nature of digital platform data, which encompasses longitudinal data on economic activities from the same entities over time, a longitudinal model reflecting intra-entity correlations is employed at the entity level. This is anticipated to contribute significantly to the formulation of strategies for new location selection and the enhancement of success rates within the retail business.
