Abstract
Recently, the general trend in the airline industry has been to generate ancillary revenue by offering additional services. Instead of completely separating ancillary services from tickets as optional components, most of the traditional airlines offer the so-called branded fares which bundle some of the ancillary components to an inclusive fare preventing a possible negative impact on the customers’ perception and brand image (mixed bundling). For instance, seat reservation and baggage transportation are often already included in the default fare. In this study, we analyse data to evaluate different bundle-pricing policies within the mixed bundling context. We use statistical regression methods to infer individual behaviour by analysing aggregated data on market level from a major European airline. We tackle the question of how to optimally price bundled fares. With the General Data Protection Regulation in place today, such high-level models which only require aggregated market data and no individual personal data are becoming more relevant for business analytics. We demonstrate how aggregate data still allow to investigate individual behaviour and our data analysis reveals the existence and variability of price elasticity. The results can help companies to segment their markets based on price elasticity and optimise their ancillary offerings accordingly.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Mathematik, Informatik und Statistik > Statistik
Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Lehrstuhl für Statistik und ihre Anwendungen in Wirtschafts- und Sozialwissenschaften |
| Themengebiete: | 500 Naturwissenschaften und Mathematik > 510 Mathematik |
| ISSN: | 1476-6930; 1477-657X |
| Bemerkung: | First published 21 January 2019 |
| Sprache: | Englisch |
| Dokumenten ID: | 60257 |
| Datum der Veröffentlichung auf Open Access LMU: | 30. Jan. 2019, 09:32 |
| Letzte Änderungen: | 14. Sep. 2023, 13:46 |
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