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A data mining-based framework to identify shoppers’ missions
(2014-02) Griva, Anastasia; Γρίβα, Αναστασία; Athens University of Economics and Business, Department of Informatics; Πραματάρη, Αικατερίνη; Μπαρδάκη, Κλεοπάτρα; Μηλιώτης, Παναγιώτης
Consumers' behavior and expectations for service have changed dramatically in recent years, as they have become more demanding. Many organizations have identified the need to become more customer centric, facing increased global competition (Bull, 2003; Phan & Vogel, 2010). Therefore, in order to respond to the ever increasing demands of consumers, they are trying to develop innovative methods for managing their customers (Anderson et al., 2007). At the same time computers have become far more powerful, and new technological trends have been developed, such as Big Data, Business Intelligence (BI), Data Mining (DM) (Provost & Fawcett, 2013). These new trends give us the opportunity to process large volumes of data and extract valuable information. Like any other business, so do retailers have realized the importance of applying these new technological trends to support decision making and satisfy their customers (Bertino, 2011). However, there is only sparse research in the context of retailing in order to discover patterns in customers' behavior, to empower decision making, and to satisfy the demanding consumers (Wang & Zhou, 2013).
Data-driven innovation in shopper marketing: a business analytics approach for visit segmentation in the retail industry
Γρίβα, Αναστασία; Griva, Anastasia; Athens University of Economics and Business, Department of Management Science and Technology; Δουκίδης, Γεώργιος; Παπακυριακόπουλος, Δημήτρης; Θεοτόκης, Άρης; Σταθακόπουλος, Βλάσης; Λεκάκος, Γεώργιος; Χατζηαντωνίου, Δαμιανός; Πραματάρη, Κατερίνα
The abundance of data reflecting customer behavior and the continuous changes in modern shopper behavior revives segmentation literature in contemporary retail. Motivated by this fact, this research proposes a visit segmentation approach that reveals the shopping intentions/ missions that induced consumers’ visits. By examining shopper behavioral data, in visit level we identify the underlying needs that boosted a customer to visit a store e.g. to procure materials to renovate the bathroom, to buy professional clothes, to prepare a dinner etc.. We applied and evaluated the proposed approach in three heterogeneous retailers with different sale channels and product types. During the application of the approach, we identified several shopper, retailer and data-related factors that affect the segmentation. Thus, this research also provides a set of factors that managers in the retail industry, as well as marketeers and data scientists should consider when designing segmentation systems and approaches. These factors not only affect the segmentation process, but also the shopper marketing decisions that our approach enables. Hence, we further present data-driven innovations in shopper marketing that the resulting visit segments could support ranging from marketing campaigns per visit segment and redesign of a store’s layout to cross-selling strategies and product recommendations.