Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer

Travel and hospitality industry are adopting high end technology to reach out their customers. New levels of disruption are being introduced using Artificial Intelligence and Machine Learning techniques of research. In the realm of physical and tangible aspects of service, since this sector have vas...

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Autores principales:	Ritu Sharma, Arpit Kumar, Cindy Chuah
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Customer experience Artificial intelligence Machine learning Explainable AI Tourism Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/5f940ef69e7d4012b67be0e2d867977e
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spelling	oai:doaj.org-article:5f940ef69e7d4012b67be0e2d867977e2021-12-04T04:36:42ZTurning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer2667-096810.1016/j.jjimei.2021.100050https://doaj.org/article/5f940ef69e7d4012b67be0e2d867977e2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2667096821000434https://doaj.org/toc/2667-0968Travel and hospitality industry are adopting high end technology to reach out their customers. New levels of disruption are being introduced using Artificial Intelligence and Machine Learning techniques of research. In the realm of physical and tangible aspects of service, since this sector have vast amounts of data available application of these techniques are being tested to explore various aspect of the customer experience. Data analysis algorithms like machine learning have been used by firms across the world to analyze these data and understand various facets of customer experience. However, these algorithms suffer from blackbox issues where businesses do not know how the outcome was achieved and if the analysis is biased in some way. In this study, we utilize an explainable machine learning approach on Airbnb data to understand solve two challenges associated with large-scale marketing data. First, we identify and prioritize the customer experience factors like bedrooms, host status, host response rate etc. that have an impact on product pricing. Secondly, we build and test an explainable predictive model on the same. Our research has significant implications for academia and industry in identifying applications of explainable algorithms in decision-making.Ritu SharmaArpit KumarCindy ChuahElsevierarticleCustomer experienceArtificial intelligenceMachine learningExplainable AITourismInformation technologyT58.5-58.64ENInternational Journal of Information Management Data Insights, Vol 1, Iss 2, Pp 100050- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Customer experience Artificial intelligence Machine learning Explainable AI Tourism Information technology T58.5-58.64
spellingShingle	Customer experience Artificial intelligence Machine learning Explainable AI Tourism Information technology T58.5-58.64 Ritu Sharma Arpit Kumar Cindy Chuah Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
description	Travel and hospitality industry are adopting high end technology to reach out their customers. New levels of disruption are being introduced using Artificial Intelligence and Machine Learning techniques of research. In the realm of physical and tangible aspects of service, since this sector have vast amounts of data available application of these techniques are being tested to explore various aspect of the customer experience. Data analysis algorithms like machine learning have been used by firms across the world to analyze these data and understand various facets of customer experience. However, these algorithms suffer from blackbox issues where businesses do not know how the outcome was achieved and if the analysis is biased in some way. In this study, we utilize an explainable machine learning approach on Airbnb data to understand solve two challenges associated with large-scale marketing data. First, we identify and prioritize the customer experience factors like bedrooms, host status, host response rate etc. that have an impact on product pricing. Secondly, we build and test an explainable predictive model on the same. Our research has significant implications for academia and industry in identifying applications of explainable algorithms in decision-making.
format	article
author	Ritu Sharma Arpit Kumar Cindy Chuah
author_facet	Ritu Sharma Arpit Kumar Cindy Chuah
author_sort	Ritu Sharma
title	Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
title_short	Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
title_full	Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
title_fullStr	Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
title_full_unstemmed	Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer
title_sort	turning the blackbox into a glassbox: an explainable machine learning approach for understanding hospitality customer
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5f940ef69e7d4012b67be0e2d867977e
work_keys_str_mv	AT ritusharma turningtheblackboxintoaglassboxanexplainablemachinelearningapproachforunderstandinghospitalitycustomer AT arpitkumar turningtheblackboxintoaglassboxanexplainablemachinelearningapproachforunderstandinghospitalitycustomer AT cindychuah turningtheblackboxintoaglassboxanexplainablemachinelearningapproachforunderstandinghospitalitycustomer
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Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer

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