indian airport reviews

Service Quality Analysis of Indian Metro Airports using Passenger Review Mining

2nd IEEE Conference - IHCI

Saveetha University

Chennai

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Hari Bhaskar S & Viral Rathod, Amadeus Labs

10-11th Mar 2016

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Agenda

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_Background

_Problem Statement

_Related Work

_Approach

_Experiment

_Results

_Limitations

_Future Work

_References

_ Indian airports passenger traffic growing annually at the pace of 18.3% in 2015.

_ International Air Transport Association (IATA) predicts India to be the third largest air travel market by 2026.

_ Low-Cost Carriers (LCC) offering affordable prices, increasing disposable income, and growing air travel propensity to save time among Indian passengers.

_ Passengers expect best-in-class service and hassle-free travel experience for air travel.

_ The growing list of expectations includes minimal wait times, high levels of comfort like stress-free travel, shopping, entertainment, meals, cleanliness, and courtesy during interactions ©

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Background

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Problem Statement

• Objective is to evaluate customer satisfaction levels of Indian Airports

• Online Reviews from Skytrax – a popular rating agency• Bangalore, Kolkata, Chennai, Mumbai and New Delhi

Airports

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Skytrax Data Set

Airport # Reviews

Bangalore 67

Chennai 62

Kolkata 64

Mumbai 163

New Delhi 146

AirportNumber of Passengers (in millions)

Bangalore 9.061

Chennai 7.614

Kolkata 5.937

Mumbai 20.018

New Delhi 22.763

Country # Reviews

Australia 32

Canada 26

India 97

United Kingdom 96

United States 93

Traveler Location

# Reviews

India 97

International 353

Not Provided 71

_ Developing service quality models UAE airports, Australian Airports

_ Fodness & Murray model is used in assessing service quality for South Africa, Bangkok and Swedish airports.

_ A research study on using twitter feedback to assess customer satisfaction of airline industry using sentiment analysis technique is done

_ Skytrax data for airlines(Lufthansa, United…) and processing reviews using sentiment analysis techniques

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Related Work

_Choose negatively rated comments (< 6)

_No sentiment analysis needed

_Process text using NLP (Stanford library)

_Part of Speech Tagger

_Create a corpus of nouns and map them using a program

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Approach

Categories Mapped Terms

Staff

Rude, Rudeness, Slow, Slowness, Impolite, Callous, Staff,Polite

ProductivityConference, Business, Meetings, Center, Quite

DecorAesthetic, Art, Music,Culture, Prayer, Lounge

Maintenance

Duty-Free, Duty,Shops, Shopping, Restaurant, Food,Beverage, Cuisine, Variety, Toilet, Toilets, Smell, Clean

EfficiencyWaiting, Speed, Time,Checkin, Slow, Inefficient

Effectiveness

Facility, Facilities, Layout, Access, Accessibility, Baggage, Connecting,Transportation

Immigration ImmigrationSecurity Security

Airport Service

Quality

Interaction

Function Diversion

Efficiency Effectiveness

Security

ChecksStaff Immigration

Maintenance Decor Productivity

Fodness & Murray Model

Use association rules or apriori to find frequent complaint items/words

Association helps to identify what traveler commonly need in an airport

Retail/Super market use them for “basket analysis

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Apriori Algorithm

Airport Service

Quality

Interaction

Function Diversion


Security



9

27

123 23

14 16

26

Results – Bangalore Airport

Associated Items Instances/Total

Effectiveness, Maintenance 10/46

Effectiveness, Security, Maintenance

5/46

Airport Service

Quality

Interaction

Function Diversion


Security



3

27

112 36

20 26

29

Results – Chennai Airport

Associated ItemsInstances/Total

Effectiveness, Maintenance22/56

Effectiveness, Security, Maintenance

8/46

Airport Service

Quality

Interaction

Function Diversion


Security



2

21

9 25

18 19

22 2

Results – Kolkata Airport



Immigration, Maintenance, Effectiveness

6/40

Airport Service

Quality

Interaction

Function Diversion


Security



12

68

230 43

52 46

40

Results – Mumbai Airport


Effectiveness, Maintenance 22/106

Effectiveness, Immigration, Maintenance

11/106

Airport Service

Quality

Interaction

Function Diversion


Security



4

51

129 27

29 24

36

Results – New Delhi Airport



_ Accuracy of NLP

_ Negative rated reviews might have positive comments

_ Corpus of nouns can be improved further

Limitations

Twitter handles and Facebook pages for passenger reviews.

Benchmarking across Airports

Scope of refining service quality models and developing new ones based on review mining terms.

Future Work

_ Delhi shows promise on the joint venture

_ Bangalore, Mumbai has concerns

_ Airports run by AAI like Kolkata and Chennai still going through modernization phase

_ Interaction aspects like the staff, immigration and security checks comes as a recurring theme of concern across all airports.

_ Mitigated through training and awareness initiatives

_ Interestingly queue wait time and delays are ranked relatively less.

_ Good airport facility, maintenance are basic necessities that passenger expects which takes precedence over the rest of categories.

_ This research study may be useful to devise a further course of actions

Conclusion

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_ India dominates air traffic growth: IATA, Business Standard, 3rd Oct 2015, http://www.business-standard.com/article/companies/india-dominates-air-traffic-growth-iata-115100200671_1.html

_ IATA Air Passenger Forecast Shows Dip in Long-Term Demand, IATA, 26th Nov 2015, https://www.iata.org/pressroom/pr/Pages/2015-11-26-01.aspx

_ Case Analysis: Manage Expectations, Business World, 17th Nov 2015, http://businessworld.in/article/Case-Analysis-Manage-Expectations/17-11-2015-87260/

_ Charges for Airports and Air navigation, AAI, http://www.aai.aero/misc/AirportCharges2014-15-221114.pdf

_ Annexure III- A , AAI, http://www.aai.aero/traffic_news/Sep2k15annex3.pdf

_ Kramer, Lois S., Aaron Bothner, and Max Spiro. How Airports Measure Customer Service Performance. Vol. 48. Transportation Research Board, 2013.

_ Fodness, D., Murray, B., 2007. Passengers’ expectations of airport service quality. Journal of Services Marketing 21, 492e506

_ Gupta, A., Arif, M., & Williams, A. (2013). Customer Service in Aviation Industry – An Exploratory Analysis of UAE Airports. Journal of Air Transport Management, 32(September 2013)

_ Park, Jin-Woo, Rodger Robertson, and Cheng-Lung Wu. "The effects of individual dimensions of airline service quality: Findings from Australian domestic air passengers." Journal of hospitality and tourism management13.02 (2006): 161-176.

_ Clemes, Michael D., et al. "An empirical analysis of customer satisfaction in international air travel." Innovative Marketing 4.2 (2008): 50-62.

_ Lubbe, B., et al., “An application of the airport service quality model in South Africa”, Journal of Air TransportManagement (2010), doi:10.1016/j.jairtraman.2010.08.001

_ Farmahini Farahani, Aliakbar, and Emil Törmä. "Assessment of customers' service quality expectations: Testing the'Hierarchical Structure for Airport Service Quality Expectations' in a Swedish context." (2010).

_ Seyanont, Arisara. "Passengers’ Perspective toward Airport Service Quality at Suvarnabhumi International Airport." (2011).

_ Misopoulos, Fotis, et al. "Uncovering customer service experiences with Twitter: the case of the airline industry." Management Decision 52.4 (2014): 705-723.

_ Suzuki, Takayuki, Kiminori Gemba, and Atsushi Aoyama. "Identifying customer satisfaction estimators using review mining." International Journal of Technology Marketing 5 9.2 (2014): 187-210.

_ Archak, Nikolay, Anindya Ghose, and Panagiotis G. Ipeirotis. "Show me the money!: deriving the pricing power of product features by mining consumer reviews." Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007.

_ Hu, Minqing, and Bing Liu. "Mining and summarizing customer reviews."Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004.

_ Liu, Bing, Minqing Hu, and Junsheng Cheng. "Opinion observer: analyzing and comparing opinions on the web." Proceedings of the 14th international conference on World Wide Web. ACM, 2005.

_ Zhang, Haiping, et al. "Feature-level sentiment analysis for Chinese product reviews." Computer Research and Development (ICCRD), 2011 3rd International Conference on. Vol. 2. IEEE, 2011.

_ Polpinij, Jantima, and Aditya K. Ghose. "An ontology-based sentiment classification methodology for online consumer reviews." Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 2008.

References

http://www.business-standard.com/article/companies/india-dominates-air-traffic-growth-iata-115100200671_1.html

https://www.iata.org/pressroom/pr/Pages/2015-11-26-01.aspx

http://businessworld.in/article/Case-Analysis-Manage-Expectations/17-11-2015-87260/

http://www.aai.aero/misc/AirportCharges2014-15-221114.pdf

http://www.aai.aero/traffic_news/Sep2k15annex3.pdf