Using Twitter network to detect market segments in the airline industry

Aymeric Punel and Alireza Ermagun

Journal of Air Transport Management, 2018, vol. 73, issue C, 67-76

Abstract: The use of social media is increasingly considered a state-of-the-art practice for marketing campaigns. Researchers have investigated various social networks and media platforms to capture the behavior and characteristics of customers. This paper analyzes the official Twitter account of an airline company, Air New Zealand, to explore its market segments. To detect the communities of customers, we develop a network clustering method, which reveals the community classes of the network, along with a text mining analysis on each community detected by the cluster analysis. The results of the network analysis demonstrate that the social network of customers in the airline industry follows Pareto's principle that is similar to scale free networks. The findings of network clustering indicate Air New Zealand is essentially followed by New Zealand citizens. The local accounts are categorized into four communities: (1) lambda New Zealand citizens, (2) management, marketing, and digital media companies, (3) tourism and dining sectors, and (4) New Zealand sport players; while the global accounts fall into two communities: (1) worldwide celebrities and (2) the travel and aviation industry. The community detection method developed in this research is beneficial for marketing and customer strategy purposes as it enables airline companies to detect the categories of passengers interested into the brand. It also allows them to identify the potential sources for advertising by seeking out exceptionally connected customers who have high degrees of centrality.

Keywords: Air New Zealand; Customers' behavior; Clustering; Social media; Social network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:73:y:2018:i:c:p:67-76

DOI: 10.1016/j.jairtraman.2018.08.004

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