dc.contributor |
Rachinger, Heiko Jürgen |
|
dc.contributor.author |
Fortuny Borysiewicz, Robert |
|
dc.date |
2021 |
|
dc.date.accessioned |
2022-04-08T09:30:18Z |
|
dc.date.available |
2022-04-08T09:30:18Z |
|
dc.date.issued |
2021-10-07 |
|
dc.identifier.uri |
http://hdl.handle.net/11201/158659 |
|
dc.description.abstract |
[eng] Business Rules is one out of many tools
AirRM system provides to deal with revenue
management strategies, however, it is a recommended tool to set a solid pricing strategy by using historical data.
In this paper I will discuss, the first step in
using AirRM system in an airline I currently
work in as a revenue manager. The main topic is
the implementation of AirRM’s tool Business
Rule, its value and the analysis of how many
rules are necessary to optimize revenue strategy
in a leg, using cluster analysis.
The results, through statistical evidence,
show an efficient grouping of flights, according
to its booking behaviour and total revenue; and
through Business Rule assignment, a useful
booking forecasting method for our department. |
ca |
dc.format |
application/pdf |
|
dc.language.iso |
eng |
ca |
dc.publisher |
Universitat de les Illes Balears |
|
dc.rights |
all rights reserved |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.subject |
65 - Gestió i organització. Administració i direcció d'empreses. Publicitat. Relacions públiques. Mitjans de comunicació de masses |
ca |
dc.subject.other |
AirRM |
ca |
dc.subject.other |
revenue management |
ca |
dc.subject.other |
airline |
ca |
dc.subject.other |
clustering |
ca |
dc.subject.other |
price strategy |
ca |
dc.title |
AirRM system, implementation of Business Rules under cluster analysis |
ca |
dc.type |
info:eu-repo/semantics/masterThesis |
ca |
dc.type |
info:eu-repo/semantics/publishedVersion |
|
dc.date.updated |
2022-02-01T07:21:12Z |
|