AE6601A Airline Ticket Price Analysis

This assignment requires students to conduct a detailed analysis of airline ticket prices. Students must collect data on four one-way routes (two leisure and two business) over six weeks, monitoring prices daily from airline websites. The collected data should be statistically analyzed, presented graphically, and discussed in a technical report. The report should identify trends, correlations, and possible reasons for price fluctuations, referencing revenue management literature. The assignment assesses the student's ability to analyze data, interpret trends, and apply economic principles to airline pricing strategies. The report should be structured as a technical report and must be less than 2000 words.

Faculty of Science, Engineering and Computing
Assessment Brief
Module: AE6601A – Air Transport Economics Setter: Dr Anil Padhra
Title of Assignment: Airline Ticket Price Analysis Deadline: Friday 10th February 2017
Module weighting: 25%
Submission details: You must submit the assignment electronically through Turnitin on
Studyspace and the file will be subsequently reviewed through plagiarism detection
software. In accordance with Section 5 of the Undergraduate Academic Regulations
document you are permitted to submit the assessment after the deadline though you will be
penalised. If the assessment is submitted after the deadline and within five working days
after the deadline your mark will be capped at 40%. If the assessment is submitted after five
working days after the deadline it will be counted as an attempt and awarded a mark of 0%.
Module Learning Outcomes assessed in this piece of coursework
i) Assess the potential impact of an Airline’s business strategy.
ii) Discuss the major determinants of air transport supply, demand and cost.
Assignment Brief and assessment criteria
A key element of a profitable global aviation industry is the ability of airlines to generate a
sufficient amount of revenue from passengers. Airlines rely on a team of revenue
management analysts to ensure that every seat sold is done so at the maximum price
possible. When revenue management get it wrong, the airline will either not fill enough
seats due to high prices or fill too many seats at low prices. Both scenarios will result in lost
revenue and lower profits. For this reason airlines incorporate price discrimination whereby
similar seats are sold at different prices depending of passenger demand and time of
booking. The general perception amongst passengers is that the earlier you buy the ticket
the cheaper it is but this may not always be the case.
This assessment is conducted in two stages. The purpose of the first stage of this assignment
is to collect data of airline ticket prices on a daily basis to understand the trend in ticket
price fluctuations and over what time period.
Take time to plan the data you collect. Poor planning will result in poor data making it
difficult for you to achieve a good mark.
 Choose four one-way routes (Two Leisure and Two Business).
 Choose direct flights only.
 Choose a departure date in early December.

 Monitor prices in economy class for at least three airlines per route.
 Monitor prices everyday for at least six weeks before the departure date.
 Identify the aircraft type operating the route to estimate the seat capacity.
To be able to compare, you should try to collect data as fairly and consistently as possible
by:
 Choosing flights departing and arriving at the same airport.
 Choosing similar departure and arrival times.
 Monitoring prices at the same time everyday.
 Only use the airlines official website to obtain prices (Avoid flight comparison
websites such as Skyscanner, Expedia, Ebookers, Momondo, Hipmunk etc.
 Delete internet cookies before requesting prices.
 Be careful not to choose two airlines operating a codeshare.
Typical leisure routes are: Spanish and Greek islands, North African coast, Las Vegas,
Florida, Caribbean islands, Indian Ocean, Thailand.
Typical business routes are: Major European capital cities, German cities, New York, Hong
Kong, Tokyo, Singapore, Seoul, Toronto, Seattle, Beijing,
Shanghai.
Tip! Feel free to use flight comparison websites to identify routes and the airlines serving
those routes. Then use the airlines official website to track prices.
Once you have completed your data collection, the next stage is to statistically analyse the
data you have collected, present it in a graphical format and discuss what the data suggests
about the airlines pricing strategy. You should
(i) Explain briefly which routes, airlines and flights you chose to collect data for
and the reasons for doing so. Identify any situation where the data collected
was outside the norm such as sold out flights etc.
(ii) Present the data using clear and appropriate graphical methods. Price
fluctuations with time should be clear.
(iii) Apply data analysis to identify any trends and correlations in the fluctuations.
You can use any mathematical method that will identify patterns. It is
important that any analytical method and calculation used is clearly
described.
(iv) Compare and discuss the ticket price trends between different routes and
airlines and suggest possible reasons for the trends. Can you identify the best
time to buy a ticket? Is it possible to forecast the fluctuations for similar
flights?
(v) As an additional activity you should collect ticket price data for a single route,
served by an airline for multiple ticket numbers increased incrementally. You
should then record the ticket price per ticket and comment on the results
observed. Some good airlines to use for this task are EasyJet, Ryanair,
Monarch and Scoot.

You must only use data that you have collected. In addition to your own data you may also
use the data collected by Dr Anil Padhra which is available on Studyspace.
The submitted assignment should be structured as a technical report and must be less than
2000 words. You should include a word count at the end of your assignment. You may use
diagrams wherever appropriate to clarify your explanations. It is not necessary for you to
include the raw data collected in the report.
Tip! Use your word count to explain reasons for price trends. Avoid a description of the
trends. Let your graphs describe the trends.
You may find it useful to read airline revenue management books and articles to explain
your data. Please remember to reference any sources used.
Feedback
You will receive detailed feedback including a breakdown of marks within three working
weeks after the submission deadline date. Marks will be awarded in accordance with the
marking scheme below.
Marking scheme
Assessment Criteria Max
mark
Quality of the data collected
Marks are awarded for a good mix of routes which include two very obvious
leisure destinations and two strong business destinations.
Routes originating in any region of the world are acceptable providing the
routes are served by multiple airlines. Every route should be served by at
least three airlines. A route served by two airlines is acceptable but should
not be the case for all routes chosen. No marks to be given for single airline
routes. The airlines chosen should be a good mix of low-cost carriers and
legacy carriers.
For the top marks, the departure and destination airports must be the same,
the approximate times of departure and arrival should be within 2-3 hours of
each other.
20%
Presentation of the data using appropriate graphs
The data collected must be presented using suitable graphical methods. The
main data is continuous and not discrete and therefore should be plotted
accordingly. The axes of all graphs should be correctly labelled. A key of the
data should be presented on the graph and the use of colour is
recommended.
15%

Statistical analysis of the data will usually lead to a graph of discrete data
which should be presented accordingly.
Graphs should demonstrate comparison between routes and airlines and
show several parameters in a clear format.
For the top marks some of the discussion should link to annotations on the
graphs to highlight key features.
Relevance of the data analysis and the methods used
This element of the report is fairly open and encourages students to think
about the various statistical methods they could employ to summarise the
data trends. The analysis of the data should focus on the relationship
between airline ticket prices and routes using appropriate statistical
parameters.
The statistical analysis should also focus on quantifying the ticket price
fluctuations. Normalisation of the parameters enables comparison and
demonstrates that the student has a good understanding of data
interpretation. Often such practice will attract the top marks.
Analysis of the absolute values of the ticket price should be avoided as this is
not a like for like comparison. For example some airlines lump all ancillary
costs into the ticket price where as others charge for baggage, food etc.
A simple linear trend line added to a graph is not demonstration of statistical
analysis.
The most important aspect of this part of the report is to enable firm
quantitative conclusions to be made. Marks will not be awarded for analysis
that is done for the sake of analysis. The analysis must be relevant and lead
to justified discussion.
20%
Discussion of the patterns and trends identified
A large proportion of the marks are given for the discussion of the data
trends and the interpretation of the statistics derived.
Students should avoid describe the trends. For example, ‘Ticket price went up
by £30 six weeks before the flight, went down by 25% 3 weeks later and
increased to £250 a day before departure.’ This description is irrelevant as
this can be seen visually determined from the graphs plotted. Instead the
discussion should focus on the possible reasons for the trends and justified
explanations of why such trends occurred. Much of the plausible explanation
for trends will come from reading revenue management literature. Reference
to revenue terminology often demonstrates that the student has read around
the subject.
25%

The discussion should cross-compare trends between airline and route types,
aircraft capacity etc. Identification of patterns is strongly encouraged.
Firm conclusions must be drawn from the data. Students should avoid
statements claiming that conclusions cannot be drawn due to the small
volume of flights analysed.
Clarity of Presentation and Readability
Mark will be awarded for:
- Correct use of professional and technical English.
- Correct spelling, grammar and punctuation.
- Correct labelling of figures and tables.
- Correct referencing of additional sources of information.
- A clearly worded and presented report.
- A well-organised and structured report.
Since this is a Level 6 assessment, students will be heavily penalised for even
minor errors as all students have had at least 20 years to perfect a well
written, error-free report.
20%