The success of any business depends on the ability to understand its customers. As any other business so do retailers, understanding the reasons consumers enter their stores is playing a key role in achieving competitive advantage and retaining their market shares. Nowadays, the advent of Business Analytics has created new ways for retailers to metamorphose the vast amount of data they have into valuable knowledge, in order to gain customers’ insights, and improve customer relationships. However, not enough research has been conducted to analyze point-of-sale (POS) retail data in order to investigate consumers’ behavior and understand the reasons they visit retail stores. This study presents an effort to fill this gap by introducing a Data Mining-based Framework, which could be used to discover patterns in customers' visits in a supermarket, and identify their Shopping Missions. The utility of this framework is been demonstrated by applying it in real data of eight representative stores of a Greek retailer. The proposed approach is useful for both academia and retail industry. As it gives the retailers the opportunity to extract consumers’ shopping missions when they visit their supermarkets, it could be used to support several decisions in the retail domain, and improve the relationships between retailers and consumers.
Griva, Anastasia; Bardaki, Cleopatra; Panagiotis,, Sarantopoulos; and Papakiriakopoulos, Dimitris,, "A DATA MINING-BASED FRAMEWORK TO IDENTIFY SHOPPING MISSIONS" in Mola, L., Carugati, A,. Kokkinaki, A., Pouloudi, N., (eds) (2014) Proceedings of the 8th Mediterranean Conference on Information Systems, Verona, Italy, September 03-05. CD-ROM. ISBN: 978-88-6787-273-2.