A data mining approach to developing the profiles of hotel customers

Min, Hokey; Min, Hyesung; Emam, Ahmed

doi:10.1108/09596110210436814

Cited by 67 publications

(36 citation statements)

References 7 publications

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“…Decision trees offer a nonlinear classification method that can avoid ineffective clustering resulting from the linear method (Moshkovich et al, 2002). Previous studies have applied such trees to solve customer problems in the service industry; for example, Min et al (2002) applied them to segment customers from a hotel database, and then analyzed customer characteristics by selecting hotels, room settings, payment options, and demographic profiles. Here we use a decision tree to segment valuable and non-valuable passengers based on demographic profiles, travel behaviors, and perceptions of service quality.…”

Section: Kdd Process and Dm Approachmentioning

confidence: 99%

Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques

Wong

Chung

2007

Journal of Air Transport Management

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Section: Kdd Process and Dm Approachmentioning

confidence: 99%

Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques

Wong

Chung

2007

Journal of Air Transport Management

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“…Min et al (2002) studied the application of data 154 mining, more specifically using decision tree modeling in order to develop the profile of a certain 155 group of customers within different hotels. In another paper, data mining has also been studied …”

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confidence: 99%

Stripping customers' feedback on hotels through data mining: The case of Las Vegas Strip

Moro

Rita

Coelho

2017

Tourism Management Perspectives

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3This study presents a data mining approach for modeling TripAdvisor score using 504 reviews 4 published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features 5 characterizing the reviews, hotels and the users were prepared and used for feeding a support 6 vector machine for modeling the score. The results achieved reveal the model demonstrated 7 adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for 8 extracting useful knowledge translated into features' relevance for the score. The findings 9 unveiled user features related to TripAdvisor membership experience play a key role in 10 influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the 11 day of the week were found to influence scores. Such knowledge may be helpful in directing 12 efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews 13 according to the member and review date. Keywords 16Customer feedback; customer reviews; online reviews; knowledge extraction; data mining; 17 modeling; sensitivity analysis; Las Vegas. Introduction 21The Online Travel Agencies (OTA) are now the most used tool of travel booking, both for the 22 means of transport and accommodation (Mauri & Minazzi, 2013) and, consequently, online 23 reviews have been exponentially increasing its use and impact in the hospitality industry over the 24 last years, due to the social media and technological evolution. In fact, nowadays potential hotel 25 customers search for online feedback before travelling and base their purchase decisions on 26 online reviews (Mauri & Minazzi, 2013). Therefore, electronic word-of-mouth (eWOM), which 27 according to Henning-Thurau et al. (2004, pp. 39) is defined as "any positive or negative 28 statement made by potential, actual or former customers about a product or company, which is 29 made available to a multitude of people and institutions via the internet", has become a huge 30 aspect when travelling, since currently every consumer has access to the internet and can easily 31 express either positive or negative feedback. Most importantly, it is an online tool to be used 32 when others seek for advice as part of the decision-making process, such as where to stay, 33 especially in hospitality industry, as consumers are purchasing an experience and cannot predict 34 its evaluation (Sparks & Browning, 2011). Moreover, holidays can be considered as a high risk 35 and involvement purchase, due to its usual personal importance and also high value of money 36 (Papathanassis & Knolle, 2011). Service quality is a determinant of the customer's perceptions 37 and their feedback. The ideal would be that the target's expectations meet the perceptions, which 38 will directly influence a positive word of mouth, contributing for a development of reputation 39 and trust (Corbitt et al., 2003). Hence, research contributions that unveil and provide in-depth 40 understanding on the features that have the mo...

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“…We argue that knowledge creation and acquisition processes at (Min et al 2002;Magnini et al, 2003;Bloom 2004;Wong et al 2006;Palmer et al 2006;), while only few BI studies exist at the level of tourism destinations (Pyo et al 2002;Höpken et al 2011;Fuchs et al 2013;Höpken et al 2014). According to the knowledge destination framework, knowledge activities deal with extracting information from differing customer and supplier-based sources, and with the generation of relevant knowledge to be applied in the form of intelligent services for customers (Jannach et el.…”

Section: The Knowledge Destinationmentioning

confidence: 95%

Applying Business Intelligence for Knowledge Generation in Tourism Destinations – A Case Study from Sweden

Fuchs

Höpken

Lexhagen

2014

Tourism and Leisure

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The book chapter at hand presents a knowledge infrastructure recently implemented as genuine novelty at the leading Swedish tourism destination, Åre. By applying a Business Intelligence (BI) approach, the Destination Management Information System Åre (DMIS-Åre) drives knowledge creation and application as a precondition for organizational learning at tourism destinations. Schianetz et al. 's (2007) concept of the 'Learning Tourism Destination' and the 'Knowledge Destination Framework ) build the theoretical fundaments for the technical architecture of the presented BI application. After having briefly discussed the set of indicators measuring destination performance and tourist experience, the book chapter highlights how DMIS-Åre is used to gain new knowledge from customer-based destination processes, like 'WebNavigation', 'Booking' and 'Feedback'. The chapter ends by outlining future research, such as the application of real-time Business Intelligence for gaining knowledge on tourists' on-site behavior at the destination in real-time.H. Pechlaner, E. Smeral (Eds.), Tourism and Leisure,

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A data mining approach to developing the profiles of hotel customers

Abstract: The 281 participants in this study were hotel guests who had stayed at 11 different luxury hotels (Westin Chosun, Grand

Cited by 67 publications

References 7 publications

Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques

Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques

Stripping customers' feedback on hotels through data mining: The case of Las Vegas Strip

Applying Business Intelligence for Knowledge Generation in Tourism Destinations – A Case Study from Sweden

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