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A Cloud-Based System for Improving Retention Marketing Loyalty Programs in Industry 4.0: a Study on Big Data Storage Implications

Articolo
Data di Pubblicazione:
2018
Abstract:
Nowadays, the growing global economy and demand for customized products are bringing the manufacturing industry from a sellers’ market towards a buyers’ market. In this context, the smart manufacturing enabled by Industry 4.0 is changing the whole production cycle of companies specialised on different kinds of products. On one hand, the advent of Cloud computing and social media makes the customers’ experience more and more inclusive, whereas on the other hand Cyber-Physical System (CPS) technologies help industries to change in real time the cycle of production according to customers’ needs. In this context, “retention” marketing strategies aimed not only at the acquisition of new customers but also at the profitability of existing ones allow industries to apply specific production strategies so as to maximize their revenues. This is possible by means of the analysis of various kinds of information coming from customers, products, purchases and so on. In this paper, we focus on customer loyalty programs. In particular, we propose a Cloud-based Software as a Service (SaaS) architecture that store and analyses big data related to purchases and products’ ranks in order to provide customers a list of recommended products. Experiments focus on a prototype of Human to Machine (H2M) workflow for the pre-selection of customers deployed on both private and hybrid Cloud scenarios.
Tipologia CRIS:
14.a.1 Articolo su rivista
Keywords:
Industry applications, product life cycle management, marketing and sales, clouds, data analysis
Elenco autori:
Galletta, Antonino; Carnevale, Lorenzo; Celesti, Antonio; Fazio, Maria; Villari, Massimo
Autori di Ateneo:
CARNEVALE Lorenzo
CELESTI Antonio
FAZIO Maria
GALLETTA Antonino
VILLARI Massimo
Link alla scheda completa:
https://iris.unime.it/handle/11570/3116920
Link al Full Text:
https://iris.unime.it//retrieve/handle/11570/3116920/178844/08118079.pdf
https://iris.unime.it//retrieve/handle/11570/3116920/186522/08118079.pdf
Pubblicato in:
IEEE ACCESS
Journal
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URL

http://ieeexplore.ieee.org/document/8118079/
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