Analysing Customer Satisfaction of Civil Aviation Companies of Turkey with Text Mining

Etı, Serkan; Mızrak, Filiz

doi:10.1007/978-3-030-50131-0_2

Cited by 7 publications

(2 citation statements)

References 81 publications

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“…Other examples of works that have tackled the application of DM techniques to aviation data are: ( Ayhan et al, 2013 ) developing predictive models based on flight data; ( Liau and Tan, 2014 ) and ( Eti and Mızrak, 2020 ) which mine tweets or text in order to analyze customer's perception over carriers; or ( Gorripaty et al, 2017 ) where weather and traffic are analyzed for easing Air Traffic Management (ATM) decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Pérez-Campuzano

Andrada

Ortega

et al. 2022

Journal of Air Transport Management

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Section: Literature Reviewmentioning

confidence: 99%

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Pérez-Campuzano

Andrada

Ortega

et al. 2022

Journal of Air Transport Management

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“…The airline industry is an extremely competitive industry with razor-thin profit margins and travel dependence on various factors, such as destinations (Hlee et al, 2020;Song & Lee, 2020). There have been several studies on understanding customer satisfaction based on service quality (Nicolini & Salini, 2006;Zins, 2001;Eti & Mızrak, 2020). However, these works do not explicitly consider the problem of upselling (Mayer et al, 2020), which is a notable gap in the research given the importance of upselling to businesses in the travel sector (Denizci Guillet, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Will You Buy It Now?: Predicting Passengers that Purchase Premium Promotions Using the PAX Model

Emadi¹,

Thirumuruganathan²,

Robillos³

et al. 2021

Journal of Smart Tourism

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Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

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The New Wave in Business Management

Kalender,

Zilka

2024

Trends, Challenges, and Practices in Contemporary Strategic Management

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Companies use strategic management models more than ever to maintain a competitive edge. One of the major problems is how to create and maintain high performance, thus ensuring long-term profitability and sustainability. The business excellence models (BEMs) provide the definition of basic strategic competencies that will provide sustainable competitive advantage through self-assessment. However, considering today's circumstances, re-evaluation of these models becomes a must. In this study, firstly development of BEMs and their changes over the years are examined. The main purpose of this comprehensive analysis is not only to provide an updated overview of existing studies but above all to emphasize what type of changes are necessary considering new trends. In this respect, models are compared both among their different versions based on criteria weights and with each other based on emerging trends. Then, in accordance with the literature review, collected metadata, and comparisons, new requirements are presented.

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Analysing Customer Satisfaction of Civil Aviation Companies of Turkey with Text Mining

Cited by 7 publications

References 81 publications

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Will You Buy It Now?: Predicting Passengers that Purchase Premium Promotions Using the PAX Model

The New Wave in Business Management

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