Improving Booking Process of Aileron with Intelligent Process Automation

This article discusses the problem areas in the booking process of Aileron and suggests the use of intelligent process automation to improve it. It includes data cleaning, analysis approach, problem areas in the booking process, and recommendations for implementation of IPA.

MKTG 1050 BUYER
BEHAVIOUR

TABLE OF CONTENT
PART-A...........................................................................................................................................4
a.4 Data cleaning.........................................................................................................................4
a.5 Analysis approach..................................................................................................................4
a.6 Discussion of the results........................................................................................................4
a.7 screenshot...............................................................................................................................5
Part B...............................................................................................................................................6
B.1 Booking process flow diagram.............................................................................................6
B.2 Evaluation of relevant problem areas applying computational thinking principles..............7
B.3 Identification of problem areas in booking process flow diagram........................................8
PART C...........................................................................................................................................9
Justification of how IPA can improve the problem areas in booking process Aileron’..............9
Three potential ethical issues of IPA implementation.................................................................9
Update booking process and ways in which IPA can solve problems of customers.................10
Implementation of Intelligent process automation....................................................................10
Automated updated information on official website.................................................................10
Identification of seats availability on the basis of updated information information===.........10
Recommendation to Aileron as whether it should implement IPA or not.................................11
REFERENCES..............................................................................................................................12

PART-A
a.4 Data cleaning
Data cleaning process refers to identification of the irrelevant data and from the data set in order
to raise the information and outcome in association with the dataset. With this process the
irrelevant and inaccurate information of the dataset would be determined (Ilyas and Chu, 2019).
The data cleaning process that is being adopted in the data set is related with the removal
of the irrelevant data from the dataset followed by structural errors. This is further cleaned with
the identification of the relevant data in accordance with the test followed by the performance of
the analysis.
The data cleaning methods include the validation of the data followed by the cleanliness of
duplicate and removal of irrelevant data.
The cleaning was applied with the identification of the relevant data related with the
customer satisfaction and booking and performing its analysis. Through the mode of data
cleaning the most relevant data in relation to the aspect of the case study would be able to get
identified that would be used for the drawing of the interferences. With the aspect of data
cleaning the analysis process would become easy that will lead to have a determination of the
result regarding the dataset.
a.5 Analysis approach
The analysis approach that is being used include the use of both the descriptive and
inferential statistics. This is because with the mode of descriptive analysis the frequency as well
as average and majority of reviews regarding the data would be able to get identified. In the same
way the characteristics of the data would be identified with descriptive statistics (Mishra and
et.al., 2019).
With the mode of inferential statistics, the generalizations of the data would be possible. This is
because the taking of data from the dataset and making its generalization would be made with the
inferential statistics. This would include the correlation through which the relationship between
the variables would be able to get determined. This would lead to have an analysis of the result
that how the variables are related with each other.

a.6 Discussion of the results
Descriptive statistics:
It refers to the statistics that would involve the determination and analysis of the frequency along
with mean, median and mode. With the help of this analysis the interpretation regarding the
average number of responded response would be able to get determined. This would include the
analysis of the mean, median and the mode. This also involve the analysis of the frequency table
that shows the response of the responded.
While making an analysis of the customer rating it can be analysed that out of a sample
of 1000 customer, the mean of the data is 4.85, while the median and mode is 5. This means the
satisfaction of the customer with regard to the service of the app is moderate to high satisfied. As
an average number of customer states the rating of 4.85 which denoted to moderate level of
satisfaction. In the same way with the analysis of the mode and median the results come to 5
which means that majority of customer said that they are moderately satisfied.
While making an analysis of the inferential statistics it can be interpreted that the correlation
between the satisfaction rating and refund time is 44%. This means it is moderately related with
each other and need to be improvised.
Inferential statistics:
In the same way the correlation between the customer rating and word of mouth is 61%
which shows a good correlation. This means that the customer is highly making a word of mouth
presentation of the app.
In case of making an analysis of one way annova it can be interpreted that the p value is
1.22 which means that the null hypothesis is accepted. This shows that there is no significant
relationship between the variable. This means that the customer rating can’t be related with the
word of mouth. They do not share significant relationship with each other.
Thus, it would be right to interpret that the rating of the customers are not directly related
with the aspect of the word of mouth. This is because it would include the negative as well as
positive reviews of the customers. In the same way with the persistence of moderate rating and
the results of the customers would suggest the company and the app that they need to make
improvisation in the app in terms of reduction of the time taken with respect to making of
payment of the refunds.

a.7 screenshot
Part B

B.1 Booking process flow diagram
B.2 Evaluation of relevant problem areas applying computational thinking principles
According to the given case study it has been identified that the consumers are able to
enter into the search parameters through the online booking platform on Aileron's website. The
Enter search parameter
Identification of seats availability
Selection of flight and additional
Services
Login after seeking flight
Entering personal details
Travel Insurance
Selection of local deals
Selection of payment method
Make up payment
Checking card holder details
Receiving automated notification
Check-in before 48 hours

users are also able to identify the seats availability and can book the seats by completing the
payment methods. But there is one problem arises which is related to the updated data of the
airline. Sometimes the booked tickets might not be available and is cancelled by the airline. This
usually occurs when the flight is already booked but the airline is unable to update the booking
status or details into the system (van Ostaijen, Santos and Mitici, 2017). In order to resolve this
critical issue, computational thinking principles can help the firm to overlook their problems and
provide solutions accordingly. It has certain principles, among all them decomposition and
abstraction considers for this particular issue. Decomposition refers to the procedure of breaking
down the complex problem of automated updation into smaller parts or more manageable
portions. With the help of this, the company observes the pattern within small decomposed
issues.
On the other hand with the help of abstraction, Aileron will able to focus on the ideas or
key informations of the problems of automatic updation of software by ignoring irrelevant
details. With the help of inclusion of above principles the company can identify solution in order
to get automatic updation of the data so that their customers can see updated version of data
while booking tickets for them.
B.3 Identification of problem areas in booking process flow diagram
As per the flow chart that has been created in the above solution states that there is
certain procedure that needs to be followed by consumers or passengers in order to get travel
through their destination. The company shares the whole effective procedure of booking the
flight tickets but there is one problem that arises after booking the tickets (Shihab and et.al.
2019). The problem arises at the second stage just after entering into the search parameters, the
consumers move forward towards the availability of seats, and then they continue to book the
seats for them. But the problem arises at the end of company that they are unable to update the
data in automatic manner. Because the firm do not utilize any kind of software for automatic
updation. Hence, with the effective principles, it has been identified that appropriate problems
have been identified with the help of certain principles which enables them to concentrate over
their issues, and they think specifically over the problem such as automatic updation of seats
availability. Thus, the problem identified at very initial stage of the overall flow chart of the

complete procedure of booking tickets. The problem has been highlighted above which
illustrates the exact stage at which the issue arises and requires proper attention of the Aileron
airline so that they are also able to sustain their brand image in front of their clients or
passengers. After resolution of this drastic issue the company also able to fasten their system so
that they are able to book the ticket first rather than other booking websites (Pasupa and
Cheramakara, 2019). Hence, they do not have to cancel the ticket and the problem of
cancellation of the ticket also gets resolved.
PART C
Justification of how IPA can improve the problem areas in booking process Aileron’
On the basis of above booking process, it can be said that customers are facing the main
problem regarding available seats. Customers do not get updated information regarding
availability of seats and after completing booking Aileron cancel their booking because of not
available seat (Alshurideh, Alsharari and Al Kurdi, 2019). So, in this context, it can be said that
intelligent process automation or implementation of artificial intelligence, computer vision and
machine learning can make this company able in providing updated information to customers.
Delta is one of the best examples that provide accurate information of customers to companies
and on the basis of customers’ data this software can update seat availability information
automatically that will be visible to both customers and employees. So, in this regard, it can be
said that this implementation can provide accurate information to customers by which they can
book their tickets.
Three potential ethical issues of IPA implementation
Inaccuracy or lack of transparency: There is no doubt that artificial intelligence and IPA
has changed the way of performing functions but software and technologies have also been
developed by human. So, technology may also create problems that human do. So, there is lack
of transparency and this software or application has an ability to take decision automatically but
AI based decisions are susceptible to inaccuracies that may create same problem that customers
of Aileron face.

Cost of implementation and maintenance: Implementation of automated software and
application is not an easy task as it may increase overall cost of this company. Due to this,
company may face problem regarding lower profit margin (Arthur, 2017).
Security issues: With AI, attackers or hackers can easily spot opening because there is
lack of protection in this type of software. IPA enables vulnerabilities and it commit mistakes
that humans cannot find easily and it may affect customers’ experience.
Update booking process and ways in which IPA can solve problems of customers
Enter search parameter
Identification of seats availability
on the basis of updated
information
Selection of flight and additional
services
Login after seeking flight
Entering personal details
Travel Insurance
Selection of local deals
Selection of payment method
Make up paymentChecking card holder detailsReceiving automated notificationCheck-in before 48 hours
Implementation of Intelligent
process automation
Automated updated
information on official
website

On the basis of updated booking process, it can be said, customers can pay after
confirming their booking on the basis of updated information by implementation of IPA software
or application. After implementation of this software, information will be updated automatically
on its official website. Customers will find easier in booking their tickets and selecting seats as
per the available seats. It can improve image of this company and it can increase customers’
satisfaction by implementing this technology in booking process.
Recommendation to Aileron as whether it should implement IPA or not
In regard to implementation of IPA, it can be suggested that Aileron should implement this
software or application in order to make booking process easier and effective. By implementing
or making use of this software, this company can solve problems that customers are facing as
this company cancel bookings of customers due to lack of availability of seats or poor updated
information (Ali and et.al., 2020). This software can enable employees of this company in
providing automated updated information regarding seats availability. Another issues that can

also be solved out by implantation of this software is increasing speed of booking system as it is
found that company cancels booking when users use another booking system at the same time
which means, it has slow booking process. So, by implementation of this software, it can solve
both speed related and updated information related problems.

REFERENCES
Books and Journals
Ali, S.R.O. and et.al., 2020, April. The Relationship between Service Failure and Service
Recovery with Airline Passenger Satisfaction. In Journal of Physics: Conference
Series (Vol. 1529, No. 2, p. 022062). IOP Publishing.
Alshurideh, M., Alsharari, N.M. and Al Kurdi, B., 2019. Supply chain integration and customer
relationship management in the airline logistics. Theoretical Economics Letters, 9(02),
p.392.
Arthur, W.B., 2017. Where is technology taking the economy. McKinsey Quarterly, 697.
Ilyas, I.F. and Chu, X., 2019. Data cleaning. Morgan & Claypool.
Mishra, and et.al., 2019. Descriptive statistics and normality tests for statistical data. Annals of
cardiac anaesthesia. 22(1). p.67.
Pasupa, S. and Cheramakara, N., 2019. Airline E-commerce user experience experiment: An
investigation of Thai LCCs passengers' purchasing behaviour among different online
platforms. Journal of Airline and Airport Management. 9(2). pp.46-55.
Shihab, S.A.M. and et.al. 2019. Autonomous airline revenue management: A deep reinforcement
learning approach to seat inventory control and overbooking. arXiv preprint
arXiv:1902.06824.
van Ostaijen, T., Santos, B.F. and Mitici, M., 2017. Dynamic Airline Booking
Forecasting. proceedings of “Air Transport Research.