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Collections :	Ιδρυματικό Αποθετήριο ΟΠΑ / AUEB Institutional Repository Σχολή Επιστημών και Τεχνολογίας της Πληροφορίας / School of Informatics Τμήμα Πληροφορικής / Department of Informatics Μεταπτυχιακές Εργασίες / Postgraduate dissertations

Title :	A data mining-based framework to identify shoppers’ missions

Alternative Title :	Εφαρμογή τεχνικών εξόρυξης γνώσης για την αναγνώριση των αγοραστικών αποστολών των καταναλωτών

Creator :	Griva, Anastasia Γρίβα, Αναστασία

Contributor :	Μηλιώτης, Παναγιώτης (Επιβλέπων καθηγητής) Πραματάρη, Αικατερίνη (Εξεταστής) Μπαρδάκη, Κλεοπάτρα (Εξεταστής) Athens University of Economics and Business, Department of Informatics (Degree granting institution)

Type :	Text

Extent :	80p.

Language :	en

Bibliographic Citation :	Βιβλιογραφία : σ. 73-76

Abstract :	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).

Abstract :

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).

Subject :	Data mining Consumer behaviour Electronic data processing Consumer motives Consumer spending Εξόρυξη δεδομένων Συμπεριφορά καταναλωτή Επεξεργασία δεδομένων Καταναλωτικά κίνητρα Καταναλωτική δαπάνη

Date Issued :	02-2014

Licence :

File: Griva_2014.pdf

Type: application/pdf

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