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Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data

Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data

Sungchul Lee, Eunmin Hwang, Ju-Yeon Jo, Yoohwan Kim
Copyright: © 2016 |Volume: 4 |Issue: 3 |Pages: 21
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781466693845|DOI: 10.4018/IJSI.2016070101
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MLA

Lee, Sungchul, et al. "Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data." IJSI vol.4, no.3 2016: pp.1-21. http://doi.org/10.4018/IJSI.2016070101

APA

Lee, S., Hwang, E., Jo, J., & Kim, Y. (2016). Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data. International Journal of Software Innovation (IJSI), 4(3), 1-21. http://doi.org/10.4018/IJSI.2016070101

Chicago

Lee, Sungchul, et al. "Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data," International Journal of Software Innovation (IJSI) 4, no.3: 1-21. http://doi.org/10.4018/IJSI.2016070101

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Abstract

Due to the advancement of Information Technology (IT), the hospitality industry is seeing a great value in gathering various kinds of and a large amount of customers' data. However, many hotels are facing a challenge in analyzing customer data and using it as an effective tool to understand the hospitality customers better and, ultimately, to increase the revenue. The authors' research attempts to resolve the current challenges of analyzing customer data in hospitality by utilizing the big data analysis tools, especially Hadoop and R. Hadoop is a framework for processing large-scale data. With the integration of new approach, their study demonstrates the ways of aggregating and analyzing the hospitality customer data to find meaningful customer information. Multiple decision trees are constructed from the customer data sets with the intention of classifying customers' needs and customers' clusters. By analyzing the customer data, the study suggests three strategies to increase the total expenditure of the customers within a limited amount of time during their stay.

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