Using multistage competing risks approaches to model web page transitions. Issue 3 (5th June 2017)

• Record Type: Journal Article
• Authors: Mair, Patrick; Treiblmaier, Horst; Lowry, Paul Benjamin
• Is Part Of: Internet research. Volume 27:Issue 3(2017)
• Page Start: 650
• Page End: 669
• Publication Date: 2017-06-05
• Subjects: E-commerce -- Survival analysis -- Online retailing -- Clickstream analysis -- Competing risks models -- Dwell-time analysis; Internet -- Periodicals; Computer networks -- Periodicals; 004.678
• DOI: 10.1108/IntR-06-2016-0167
• Languages: English
• ISSNs: 1066-2243

Abstract

Abstract : Purpose: The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users' online behavior. This information enables real-time personalization of web content. Design/methodology/approach: This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked "compete" against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams. Findings: Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown. Research limitations/implications: As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users' online behavior. Practical implications: A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior. Originality/value: The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and "big data" from hype to reality.