Gaining customer knowledge in low cost airlines through text mining
| DOI | https://doi.org/10.1108/IMDS-07-2014-0225 |
| Date | 07 October 2014 |
| Pages | 1344-1359 |
| Published date | 07 October 2014 |
| Author | Bee Yee Liau,Pei Pei Tan |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
Gaining customer knowledge
in low cost airlines through
text mining
Bee Yee Liau and Pei Pei Tan
Department of Applied Statistics, University of Malaya,
Kuala Lumpur, Malaysia
Abstract
Purpose – The purpose of this paper is to study the consumer opinion towards the low-cost airlines or
low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better
understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in
revealing current customers’ satisfaction level towards low-cost airlines.
Design/methodology/approach – About 10,895 tweets (data collected for two and a half months)
are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical
techniques are used to segment the customers’ opinion.
Findings – The results with two different sentiment algorithms show that there ismo re positive than
negative polarity across the different algorithms. Clustering results show that both K-Means and
spherical K-Means algorithms delivered similar results and the four main topics that are discussed by
the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and
delays and post-booking management.
Practical implications – Gaining knowledge of customer sentiments as well as improvements on
the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight
cancellations or delays and post-booking management will help LCCs to attract more customers and
generate more profits.
Originality/value – This paper provides useful insights on customers’ sentiments and opinions
towards LCCs by utilizing social media information.
Keywords Customer relationship management, Malaysia, Clustering, Airlines, Sentiment analysis,
Text mining
Paper type Research paper
1. Introduction
Low-cost carriers (LCCs) improves consumer welfare following the airline deregulation
(refer to Borenstein, 1992 and the references therein). A LCC or commonly known as
budget airline provides its services to the public at a relatively lower price with
fewer comforts compared to the traditional airlines. In 1978, the USA implemented
The Airline Deregulation Act, by removing government control over the entry of new
airlines into market including their pricing and flight routes, and allowing airlines to
reconfigure their flight routes to maximize the utilization of their capacity. This has
resulted in the dramatic drop of airfares. With the increasing of middle class
population in China, Southeast Asia and India, the demand for air travel has increased
tremendously.Acco rding to the Boeing Company,the total number of airlines in Asia is
estimated to increase by 65 per cent (14,750 in number) by year 2032, and nearly half of
the world’s air traffic growth will be driven by travels to, from or within the Asian
region for the next 20 years (The Asia Foundation, 2014). Therefore, it is important
for a low-cost airline to strengthen its customer relationship management (CRM)
in sustaining its industrial competitiveness. Social media can aid in this aspect by
providing useful information such as customer reviews quickly. In this 21st
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Industrial Management & Data
Systems
Vol. 114 No.9, 2014
pp. 1344-1359
rEmeraldGroup PublishingLimited
0263-5577
DOI 10.1108/IMDS-07-2014-0225
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IMDS
114,9
Information Age, social media is by far the most powerf ul form of marketing. For that
reason, information communication technology service providers are changing
strategies to attract customer and to reduce customer churn rate (see Lee et al., 2013,
2014). There will be no exceptions for airlines industry too. The utilization of social
media information of social media in text analytics has several business advantages.
First, it can serves as a marketing tool as reported in Sita (2013), where 70 per cent
of the airlines surveyed will make use of social media for marketing purposes,
reservations, on-line check-in and CRM by year 2016. Second, it improves the brand
awareness, loyalty and recognition through text mining. Gilbert et al. (2001) reported
the need of branding in LCCs in this competitive environment. Third, it helps to gain
competitive advantage. A survey carried out by the Kelsey Group concluded that
review sites are gre atly influencing consumers’ shopping behaviour and that online
reviews are not only impacting the online sales but also the offline purchase decisions
(ComScore, 2007)[1].
With the increasing competition in the LCCs industry, this study fill the gap in the
literature by focusing on the customer reviews of LCCs in Malaysia, in which AirAsia,
the best low-cost airline since 2009 is included in the study. A survey has been
conducted by O’Connell and Williams (2005) on passengers’ perceptions of LC C and
full service carriers. Two major airlines in Malaysia, Malaysia Airlines (MAS) and
AirAsia were chosen to be studied. The stud’s results showed that young people are
more likely to be attracted by the LCCs. Compared with MAS, AirAsia crew
productivity level is three times higher and the airplane utilization rate is five hours
more a day. They also found that MAS and AirAsia customer segments are different
with the former used for business trip pu rposes and the latter is more likely to be
chosen for recreation purposes. Sentiment analysis on Croatia Airlines by Jakopovic
´
and Preradovic (2014) showed that the airline perceived more positively than
negatively despite its poor financial performance and employees strike in 2010.
Adeborna and Siau (2014) and Sreenivasan et al. (2012) both studied the airline using
microblogging data. The former discussed about the airline quality using sentiment
analysis and the latter discussed on the types of communication exchang e between
airlines and their consumers. Saha and Theingi (2009) conducted a survey on low-cost
airlines in Thailand and concluded that passenger satisfaction was an important driver
of behavioural intentions. Dobruszkes (2006) concluded that European LCCs have
acquired a significant place in Western Europe but the markets have yet to reach the
healthy level. Liberalization and point-to-point routes have boosted the creation of new
routes in the industry (Dobruszkes, 2006). Dresner et al. (1996) studied the impact of
LCCs on airport and route competition and concluded that the presence of LCCs on
both new and competitive routes have led to a decrease in airfare and increase in air
traffic. The spillover on competitive routes caused by the entrance of LCCs into the
markets has indirectly forced LCC to pay more attention to customers’ welfare.
The immense data available in textual form in databases and the World Wide Web,
manual analysis and extraction of useful information are not p ossible. Text mining,
also known as intelligent text analysis is a computer-driven automated technique used
to discover significant and non-trivial patterns of information from the unstr uctured
texts. This technique has created a strong industrial impact in decision making
especially in customer-focused companies such as those in the retail, financial,
communications and marketing industries. Businesses use text mining applications to
analyse customer demographics, to predict future trends, to gain knowledge of
competitors’ developments and to make proactive and knowledge-driven decisions.
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Customer
knowledge in
low cost airlines
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