Dynamics of hotel website browsing activity: the power of informatics and data analytics

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Authors: Chan, I.C.C., Ma, J., Law, R., Buhalis, D. and Hatter, R.

http://eprints.bournemouth.ac.uk/34399/

Journal: Industrial Management and Data Systems

ISSN: 0263-5577

DOI: 10.1108/IMDS-12-2019-0709

Purpose: This paper aims to investigate the temporal dynamics of users browsing activity on a hotel website in order to derive effective marketing strategies and constantly improve website effectiveness. Users' activities on the hotel's website on yearly, monthly, daily and hourly basis are examined and compared, demonstrating the power of informatics and data analytics. Design/methodology/approach: A total of 29,976 hourly Weblog files from 1 August 2014 to 31 December 2017 were collected from a luxury hotel in Hong Kong. ANOVA and post-hoc comparisons were used to analyse the data. Findings: Users' browsing behaviours, particularly stickiness, on the hotel website differ on yearly, monthly, daily and weekly bases. Users' activities increased steadily from 2014 to 2016, but dropped in 2017. Users are most active from July to September, on weekdays, and from noon to evening time. The month-, day-, and hour-based behaviours changed through years. The analysis of big data determines strategic and operational management and marketing decision-making. Research limitations/implications: Understanding the usage patterns of their websites allow organisations to make a range of strategic, marketing, pricing and distribution decisions to optimise their performance. Fluctuation of website usage and level of customer engagement have implications on customer support and services, as well as strategic partnership decisions. Originality/value: Leveraging the power of big data analytics, this paper adds to the existing literature by performing a comprehensive analysis on the temporal dynamics of users' online browsing behaviours.

This data was imported from Web of Science (Lite):

Authors: Chan, I.C.C., Ma, J., Law, R., Buhalis, D. and Hatter, R.

http://eprints.bournemouth.ac.uk/34399/

Journal: INDUSTRIAL MANAGEMENT & DATA SYSTEMS

eISSN: 1758-5783

ISSN: 0263-5577

DOI: 10.1108/IMDS-12-2019-0709

The data on this page was last updated at 05:30 on April 13, 2021.