Received: 2016-02-20  |  Accepted: 2016-05-10


Strategy of sustainable competitiveness:methodology of real-time customers’ segmentation for retail shops


The objective of this article is to develop a methodology in order to implement real-time customers segmentation analysis in the decision making process of the enterprise. A review of big data usage in retail stores was conducted along with a document-based descriptive analysis of secondary data and further critical literature analysis. Decision making strategies and flow charts were used for the development of competitiveness methodology by referring to a case of a supermarket chain. Customer segmentation researchers analyse mainly the algorithms or behaviour pattern behind the clustering process; however, neither of them offers a proper strategy for implementing a realtime customer segmentation process inside the enterprise. Sustainable competitiveness advantage may be achieved by implementing the segmentation theory with concepts of data mining and internet of things (Iot). The process of developed data mining shows many ways for the enterprise to maximize competitiveness. However, time and large investments may be required to develop proper methods for unique solutions. A concrete case study of the selected retail store should be analysed before implementing the real-time customer segmentation methodology inside the enterprise. There is a multicultural population in every market that has different culture, beliefs, preferences and shopping patterns; therefore, constant analysis is essential for efficient usage of customer segmentation. Practically none of the prior research results carried out by other authors offered a concrete methodology how to implement real-time customer’s segmentation inside the enterprise. The authors created such a methodology that can provide sustainable long-term competitiveness advantage.


big data concept, customer segmentation, marketing, retail sales, competitiveness

JEL classifications

L81 , C81 , M31





Vojtovič, Sergej
Alexander Dubček University of Trenčín
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Navickas, Valentinas
Kaunas University of Technology
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Gruzauskas, Valentas
Kaunas University of Technology
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Journal of Security and Sustainability Issues

Issue date



ISSN 2029-7017 (print)
ISSN 2029-7025 (online)


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