MBA504 Data Analysis and Digital Operations: FCTG Data Analytics Case

This case study examines the application of data analytics within the Flight Centre Travel Group (FCTG), a global travel agency, to address inefficiencies and enhance decision-making. It identifies the challenge of predicting consumer behavior due to readily available information and rapidly changing trends. The study proposes using cluster and regression analysis on data variables such as age, gender, country, career, marital status, income, number of travels, and destinations to understand consumer patterns and predict travel trends. Cluster analysis will group categorical data to identify similarities, while regression analysis will build predictive models for the number of travels and preferred destinations. Key stakeholders include FCTG's management, IT department, marketing team, and customers. The anticipated benefits include improved understanding of clients, better resource allocation, reduced operational costs, and increased revenue. Challenges to implementation involve infrastructural improvement costs and addressing ethical concerns regarding data usage. The study concludes that the application of big data analytics can significantly benefit FCTG by enabling proactive decision-making and improved efficiency.

APPLICATION OF DATA: CASE
STUDY OF FLIGHT CENTRE
TRAVEL GROUP (FCTG)

APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
Table of Contents
INTRODUCTION.......................................................................................................................................3
POSSIBLE INEFFICIENCIES...................................................................................................................5
APPLICATION OF DATA.........................................................................................................................7
DATA DESCRIPTION AND AVAILABILITY.....................................................................................7
DATA ANALYSIS METHODOLOGY................................................................................................11
CLUSTER ANALYSIS.....................................................................................................................11
REGRESSION ANALYSIS..............................................................................................................12
STAKEHOLDERS....................................................................................................................................15
BENEFITS AND CONSEQUENCES.......................................................................................................15
CHALLENGES TO IMPELEMENTATION............................................................................................16
CONCLUSION.........................................................................................................................................17
APPENDIX...............................................................................................................................................18
REFERENCES..........................................................................................................................................19
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
INTRODUCTION
Data can be broadly defined as a piece or unit of information (O'Neil & Schutt, 2013). In the
past, limitations in technology have restricted the capabilities of data across many fields and
disciplines. The technological limitations meant that only a limited amount of data could be
stored at a time (Vicenc, 2017). This in turn restricted the extent to which data analysis could be
applied. Data analysis refers to the application of mathematical and statistical techniques and
methodologies to data in order to derive inference about aspects of the data (Kirk, 2016).
However, with the advancements that have been made in the field of technology, the storage and
usage of large amounts of data has been made possible. The technological advancements have
resulted in storage devices with huge storage capacities as well as machines with high processing
power (Laudon & Guercio, 2014). This has made the analysis of large amounts possible and
accurate. Big data refers to significantly large amounts of data that has the ability to provide
highly accurate and reliable inferences (Ulf-Dietrich & Uwe, 2014).
The analysis of big data assists in better understanding of the subject of interest. This property of
big data makes it very important in the field of business. In terms of business, big data analysis is
instrumental in cost reduction, marketing, product development, sales improvement and profit
improvement (Galit, et al., 2018).
Big data analysis can be applied in companies in the travel industry such as the Flight Centre
Travel Group (FCTG). The Flight Centre Travel Group (FCTG) is global travel agency that
focuses on providing leisure travel arrangements for consumers (Flight Centre Travel Group,
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
2018). Flight Centre Travel Group (FCTG) has its headquarters in the city of Brisbane in
Australia, where it was founded in the year 1982 (Flight Centre Travel Group, 2018).
Apart from Australia, the company being a global travel agency, it operates in 11 other countries
around the world; New Zealand, China, Hong Kong, Singapore, India, United Arab Emirate
(UAE), South Africa, Mexico, United Kingdom (UK), Canada and United States of America
(USA) (Flight Centre Travel Group, 2018).
The Flight Centre Travel Group (FCTG) offers travel option in flights, boat cruises and road
trips. These travel options are spread across the 15 brands that operate under it. These brands are
Travel Managers, Travel Smart, Round the World Experts, BYO Jet, Aunt Betty, Top Deck,
Flight Center, Student Flights, Travel Associates, My Adventure Travel, Liberty Travel,
Journeys Are Made, Back Roads, Flight Centre (Sports and Events) and Student Universe (Flight
Centre Travel Group, 2018).
This research study is aimed at observing the possibility of application of big data analysis in the
travel industry. The Flight Centre Travel Group (FCTG) will be used as a case study of this
application in the travel industry.
This study is going to explore the possible inefficiencies in the travel industry then identify the
appropriate application of big data for the inefficiencies. The type of data required, its
availability and the analysis methodologies are also going to be evaluated with a sample
expected output presented. Finally, the relevant stakeholders, benefits and consequences of the
proposed application of data, and the challenges of implementing the application of data for
Flight Centre Travel Group (FCTG) will be considered.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
POSSIBLE INEFFICIENCIES
The biggest challenge facing businesses currently is the ability to predict consumer behavior
(Aalst & Wil, 2011). Technological advancements, especially the social media, has ensured that
consumers are continuously provided with information. This vast amount of information
available to the consumers implies that the consumer is likely to change their patterns within a
significantly short period of time (Anthony & Johnson, 2008).
This is especially true for companies that provide leisure services or produce products deemed as
luxury. Social media, and by extension technology, plays a huge role in determining what social
activities are most current and most desirable (Piskorski, 2014). This can in other terms be
referred to as most ‘trendy’ social activities.
Given that leisure falls in the category of social activities, then social media can be said to have
influence over which leisure activities are most ‘trendy’. Hence, customers of companies that
provide leisure services or produce products deemed as luxury can also easily change their
consuming patterns.
The Flight Centre Travel Group (FCTG) provides leisure travel arrangements for its consumers.
Thus, its customers are likely to change their consuming patterns due to the effect of the ease of
access of information. The Flight Centre Travel Group (FCTG) possibility has a problem of
predicting consumer behavior.
Although the ‘trends’ are spread and encouraged through social media, for the case of leisure
travel the ‘trend’ has to be in a real life situation before it becomes a subject of social media.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
This is to mean, that the originator of the ‘trend’ must first experience the leisure travel first
before it becomes ‘trendy’. Considering this, then analysis can be done on the leisure travels and
the most ‘trendy’ patterns identified even before they become ‘trendy’. This will enable Flight
Centre Travel Group (FCTG) to effectively make the most out of the ‘trendy’ pattern by
reallocating resources.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
APPLICATION OF DATA
DATA DESCRIPTION AND AVAILABILITY
Table 1: Data Variables Summary gives a summary of the type of data variables that will be
required for the application of data for assisting Flight Centre Travel Group (FCTG) in
predicting consumer behavior.
Table 1: Data Variables Summary
Variable Name Variable
Description
Variable Type Measurement
Scale
Data
Availability
Age This is a
numerical
variable of the
age of the client
procuring the
services of Flight
Centre Travel
Group (FCTG).
Independent
Variable
Ratio Scale Available
from
company
data.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
Gender This is a
categorical
variable of the
gender of the
client procuring
the services of
Flight Centre
Travel Group
(FCTG). 0 for
Male and 1 for
Female.
Independent
Variable
Nominal Scale Available
from
company
data.
Country This is a
categorical
variable of the
country from
which client
procuring the
services of Flight
Centre Travel
Group (FCTG)
comes from. 0 to
10, each digit
representing a
Independent
Variable
Nominal Available
from
company
data.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
country from the
11 countries that
the company
operates from
Career This is a
categorical
variable of the
career of the
client procuring
the services of
Flight Centre
Travel Group
(FCTG).
Independent
Variable
Nominal Scale Available
from
company
data.
Marital Status This is a
categorical
variable of the
marital status of
the client
procuring the
services of Flight
Centre Travel
Group (FCTG). 0
for Not Married
Independent
Variable
Nominal Scale Available
from
company
data.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
and 1 for Married
Income This is a
numerical
variable of the
estimated average
income for an
individual in the
career of the
client procuring
the services of
Flight Centre
Travel Group
(FCTG).
Independent
Variable
Ratio Scale This
information
can be
obtained
from the
government
labor
website for
the
respective
countries.
Number of
Travels
This is a
numerical
variable of the
number of trips
that client
procuring the
services of Flight
Centre Travel
Group (FCTG)
takes annually.
Dependent
Variable
Ratio Scale Available
from
company
data.
Destinations This is a Dependent Nominal Scale Available
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
categorical
variable of the
country which
client procuring
the services of
Flight Centre
Travel Group
(FCTG) travels
to. 0 to 27, each
digit representing
a country from
the 28 countries
that are
destinations for
the company
Variable from
company
data.
DATA ANALYSIS METHODOLOGY
The application of data for Flight Centre Travel Group (FCTG) is going to utilize to data analysis
techniques. These techniques are cluster analysis and regression analysis.
CLUSTER ANALYSIS
Cluster analysis refers to a form of statistical analysis that groups categorical data variables with
respect to a numerical data variable (Malki & Rizk, 2016). Cluster analysis is aimed at
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
determining the existence of similarities among the categories of the categorical data variable.
The categories are grouped into clusters depending on the level/intensity of similarity displayed
in terms of the magnitude of the numerical data variable (Yu, et al., 2011).
In the case of Flight Centre Travel Group (FCTG), the numerical data variable will be the
Number of Travels. The categorical data variables that will be grouped each will be; Career and
Country.
The clusters will be displayed graphically as shown below:
Figure 1: Sample Output for Cluster Analysis
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
REGRESSION ANALYSIS
Regression analysis is a big data analytics method that produces a predictive model for a variable
in a dataset, known as the dependent variable, using other variables in the dataset, known as
independent variables (Jaulin, 2010).
The case for Flight Centre Travel Group will require two regression analyses to be carried out:
1. Multiple Linear Regression Modelling: This is a regression model with a single and
numerical dependent variable, but multiple independent variable (Jaulin, 2010). Here
the dependent variable will be Number of Travels while the independent variables will
be Age, Marital Status, Gender, Career, Country and Income.
The sample output for this analysis is as shown below:
Figure 2: Sample Output for Multiple Linear Regression
2. Logistic Regression Modelling: This is a regression model with a single and categorical
dependent variable, but multiple independent variable (Hosmer, 2013). Here the
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
dependent variable will be Destinations while the independent variables will be Age,
Marital Status, Gender, Career, Country and Income.
The sample output for this analysis is as shown below:
Figure 3: Sample Output for Logistic Regression
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
STAKEHOLDERS
The chart below provides a summary of the stakeholders that will be involved in the application
of data for Flight Centre Travel Group (FCTG).
Figure 4: Stakeholders Chart
BENEFITS AND CONSEQUENCES
The benefits of this application of data for Flight Centre Travel Group (FCTG) include:
1. Gaining a better understanding of its clients enables the company to predict the consumer
behavior.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
2. Allows for better planning and reallocation of resources depending on prevailing demand.
3. Reduction of operation costs for the company
4. Increase in revenue and subsequently profits for the company.
The main consequence of the application of data for Flight Centre Travel Group (FCTG) will be
the concern on data usage and the ethics of using consumer data regardless of granting of
permission. The question of; “to what extent data will be used” may also arise and internal
company guidelines have to be developed to regulate the extent of data use.
CHALLENGES TO IMPELEMENTATION
The Flight Centre Travel Group (FCTG) is going to face two main challenges in the
implementation of the application of data for the company.
The first will be the infrastructural improvement costs. The company will have to improve its
technological infrastructure to enable for the storage and processing of big data. The
technological infrastructure of the company’s various brands and branches across the world will
also need to employ or improve security features to prevent data breaches. This process requires
a substantial amount of investment to achieve the best results.
The second challenge will be the data ethics. Several guidelines have been set to regulate the
obtaining and subsequent usage of data depending on the country in question (Lenca & Ferretti,
2018). Given that Flight Centre Travel Group (FCTG) operates in 11 countries, there will be
need for the company to familiarize and adhere to the data regulations in each of the countries.
This may be a long process that may delay the implementation of data for the company.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
CONCLUSION
Big data analysis is essential in the field of business. The benefits of its application can be
summarized with the increase in profits for the company in question. The Flight Centre Travel
Group would therefore benefit massively from the application of data using the cluster and
regression analyses approaches.
Although the cost of technological infrastructure improvements may be costly at the beginning,
the gains from the application of data in the long term will prove worthy. Data ethics should also
be put into consideration when implementing the application of data for any given company. It is
important that the use of data does not conflict with the set regulations that are meant to protect
the owner of the data.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
APPENDIX
The simulated data below for 20 observations was used to generate the sample output for Figure
1: Sample Output for Cluster Analysis, Figure 2: Sample Output for Multiple Linear Regression
and Figure 2: Sample Output for Multiple Linear Regression. The R Software was used for the
simulation of the data and the generation of the outputs.
Figure 5: Simulated Data
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
REFERENCES
Aalst, VD & Wil, MP 2011, Process Mining: Discovery, Conformance and Enhancement of
Business Processes, 1st edn, Springer, New York.
Anthony, SD & Johnson, MW 2008, Innovator's Guide to Growth, 1st edn, Havard Business
School Press, New York.
Flight Centre Travel Group, 2018. Homepage, viewed 3 January 2019, www.fctgl.com
Flight Centre Travel Group, 2018. Leisure, viewed 3 January 2019,
www.fctgl.com/our-brands/leisure/
Galit, S. et al. 2018, Data Mining for Business Analytics, 1st edn, John Wiley & Sons, Inc., New
Delhi.
Hosmer, D 2013, Applied Logistic Regression 1st edn, Wiley, Hoboken, New Jersey
Jaulin, L 2010, 'Probabilistic set-membership approach for robust regression', Journal of
Statistical Theory and Practice, Vol.5, No.1, pp. 1-14.
Kirk, A 2016, Data Visualization: A Handbook for Data Driven Design, 2nd edn, Sage
Publications, Ltd, Thousand Oaks, CA.
Laudon, KC & Guercio, TC 2014, E-commerce. Business. Technology. Society, 1st edn, Pearson,
Chicago.
Lenca, M & Ferretti, A 2018, 'Considerations for Ethics Review of Big Data Health Research: A
Scoping Review'. PLoS ONE, Vol.13, No.10, pp. 23-25.
Malki, AA & Rizk, MA 2016, 'Hybrid Genetic Algorithm with K-Means for Clustering
Problems', Open Journal of Optimization, Vol.5, No.2, pp. 1-4.
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APPLICATION OF DATA: CASE STUDY OF FLIGHT CENTRE TRAVEL GROUP (FCTG)
O'Neil, C & Schutt, R 2013, Doing Data Science, 3rd edn, O'Reily, London.
Piskorski, MJ 2014, A Social Strategy: How We Profit from Social Media. Princeton, Princeton
University Press, New Jersey.
Ulf-Dietrich, R & Uwe, M 2014, 'Mining "Big Data" Using Big Data Services', International
Journal of Internet Science, Vol.1, No.1, pp. 1-8.
Vicenc, T 2017, Studies in Big Data, 1st edn, Springer International Publishing, Chicago.
Yu, YP et al. 2011, 'Pattern Clustering of Forest Fires Based on Meteorological Variables and its
Classification Using Hybrid Data Mining Methods', Journal of Computational Biology and
Bioinformatics Research, Vol.3, No.1, pp. 47-52.
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