Alireza Naser Sadrabadi; razieh almasi sarvestani; Elham Ghobadi
Abstract
The purpose of this paper is to provide an integrated model of the Kano and associative rules for classifying customer needs and analyzing their behavior. The statistical population of this research includes customers of four and five-star hotels in Tehran. In this research, after collecting data, customers' ...
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The purpose of this paper is to provide an integrated model of the Kano and associative rules for classifying customer needs and analyzing their behavior. The statistical population of this research includes customers of four and five-star hotels in Tehran. In this research, after collecting data, customers' expectations were categorized from the website by the Kano model and then the relationship between customer demographic characteristics and the results of the Kano model was specified by associative rules. The result of the classification of services by the Kano model shows that booking online and tracking reservations are in the category of basic needs; booking by e-mail and free download are in the category of functional needs, and information websites and e-newsletters are in the category of exciting needs. Also, as an example, the results of exploring associative rules show that for men, having an online reservation on a website is a basic need with the probability of 93%, but this is a basic need for women with the probability of 89%.
Azarnoush Ansari; ali asadi
Abstract
The aim of this study is to evaluate the tourist loyalty through data miningapproach. The study has exemined 880 domestic tourists who have stayed inmore than one night in four and five star hotels of Isfahan in spring and summer2014 and 2015. SPSS and Clementine12 was used for data analysis.Also,Mixture ...
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The aim of this study is to evaluate the tourist loyalty through data miningapproach. The study has exemined 880 domestic tourists who have stayed inmore than one night in four and five star hotels of Isfahan in spring and summer2014 and 2015. SPSS and Clementine12 was used for data analysis.Also,Mixture Algorithm PSO-KM was applied for tourism clustering.The resultsshowed that tourists can be classified in two categories. The first category havea high average in length of communication with tourism and travel recency andthe cost and frequency of travel are less than average. Therefore, the customersare loyal and uncertain. The second category has a high average in travelrecency and the length of communication with tourism, cost and frequency oftravel is less than average. Therefore the customers are new and uncertain.
