In electronic retailing market and/or supply chain, upstream sellers need to carefully plan and offer suitable returns policies to their downstream customers in order for ensuring product qualities, promoting product sales, maintaining customer satisfaction, lowering handling costs, and ultimately maximizing total profits. This paper aims at exploring the feasibility of using a hybrid data mining approach to support the coordination of returns policies and marketing plans for profit optimization in the e-business domain. A multi-dimensional data model and an integrated data mining process are provided to facilitate the three-staged clustering, classification, and association mining functions. Through this knowledge discovery process, customer and product classes identified in terms of the level of returns ratios are associated with the returns policies and marketing plans to generate rules for optimizing market profits. Also presented is a simulation example with embedded scenarios to test and validate the proposed data model and data mining process.
Lecture Notes in Artificial Intelligence, 4827, 507-517