Analyzing online consumer behavior in mobile and PC devices: A novel web usage mining approach

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

We investigate and compare online consumer behavior on an e-retailer website in mobile versus PC devices, through the application of a web usage mining approach on clickstream data recorded in server-side log files. Online consumer behavior is characterized through both engagement measures and the discovery of common sequences of navigation patterns, using an innovative approach that combines footstep graph visualization with sequential association rule mining. We find that sessions conducted through mobile devices are more likely to consist of task-oriented behavior whereas sessions conducted through PC devices are characterized by a more exploration-oriented browsing behavior. Moreover, we find that certain sequence rules are associated with an increased likelihood of purchase in both mobile and PC sessions. The results demonstrate the value of our approach in analyzing online browsing behavior, across platforms, in the context of electronic retailing.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalElectronic Commerce Research and Applications
Volume26
DOIs
StatePublished - Nov 2017
Externally publishedYes

Keywords

  • E-commerce
  • Footstep graph
  • M-commerce
  • Navigation patterns
  • Online browsing behavior
  • Sequential association rule mining
  • Web usage mining

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