Abstract
Big Data and its importance in inferencing a value out of it is not hidden from anyone. Social networking sites like Twitter proved to be abundant source of information. Like any other sector tourism data can also be extracted out from tweets posted by people all around. Data available on twitter can be in form of text, photographs, Customer preferences can be identified using twitter analytics which can help service providers to offer personalized services. If tour operator are able to predict trends they can easily set optimized price and prepare well in advance to provide unforgettable trip to their customers. Tour operators adopt list pricing policy for deciding price of the tourism product and also there is no set model available for this. The tour operators set the price which helps them to gain high profit, but due to non- availability of any standard formula the decided price varies with the price offered by competitors. Prices are kept high when season is at the peak and more and more tourists are visiting the place or purchasing the tourist products, similarly price is kept low when season is low. In this chapter authors have proposed pricing model considering different factors that decides rates of the product in the tourism sector. Real time analytics performed on the data available on the web portals or social networking sites are used to get the most trending tourist destination and the tour operators functioning at different destination can set price of their products using the proposed model. Real time analytics will help tour operators to analyze the demand in coming season.
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Sinha, S., Bhatnagar, V., Bansal, A. (2018). Sifting Through Hashtags on Twitter for Enterprising Tourism and Hospitality Using Big Data Environment. In: Dey, N., Babo, R., Ashour, A., Bhatnagar, V., Bouhlel, M. (eds) Social Networks Science: Design, Implementation, Security, and Challenges . Springer, Cham. https://doi.org/10.1007/978-3-319-90059-9_3
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