Generolo Jono Žemaičio Lietuvos karo akademija

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.

Sustainable development process is affected by contemporary phenomena. Big Data processing inefficiency is detrimental for banks’ activity excellence. The software used for running and handling the interbank network framework provides services with extremely strict uptime (above 99.98 percent) and quality requirements, thus tools to trace and manage changes as well as metrics to measure process quality are essential. Having conducted a two year long campaign of data collection and activity monitoring it has been possible to analyze a huge amount of process data from which many aggregated indicators were derived, selected and evaluated for providing a managerial dash-board to monitor software development. The paper provides insights about the issues related to Big Data processing inefficiencies. Context of sustainable development is being taken into account.