shirin mahdavi; Mostafa Emadzadeh; azarnoush ansari
Abstract
The identification of tourism market segments helps to better develop travel packages and more efficient strategies. By identifying the expected values derived from the behavioral patterns and comparing them with the behavioral interests of the travel of elderly people, along with individual characteristics, ...
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The identification of tourism market segments helps to better develop travel packages and more efficient strategies. By identifying the expected values derived from the behavioral patterns and comparing them with the behavioral interests of the travel of elderly people, along with individual characteristics, the market for elderly tourists can be segmented and predicted. In terms of the purposes, this is an applied research and in terms of the data collection method, this is descriptive-survey. The qualitative approach and the Delphi method has been used to identify the expected values and, to predict market groups, a quantitative approach using neural networks has been used. This research has been conducted among 380 Esfahani and Tehrani elderly tourists using non-random and available sampling method. The results show that values are due to individual and personality traits, travel motives, travel experiences, travel style, and travel pleasure. The market for elderly tourists was identified and predicted based on the expected values, with the neural network technique in three conservative, prosperous and younger groups.
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.
