Analysing customer behaviour in mobile app usage

Qianling Chen (First Author), Xiande Zhao (Participant Author), Min Zhang (Participant Author)

Research output: Contribution to journalJournal


Purpose Big data produced by mobile apps contains valuable knowledge about customers and markets and have been viewed as productive resources. The purpose of this paper is to propose a multiple methods approach to elicit intelligence and value from big data by analysing the customer behaviour in mobile app usage. Design/methodology/approach The big data analytical approach is developed using three data mining techniques: RFM(recency, frequency, monetary) analysis, link analysis, and association rule learning. The authors then conduct a case study to apply this approach to analyse the transaction data extracted from a mobile app. Findings This approach can identify high value and mass customers, and understand their patterns and preferences in using the functions of the mobile app. Such knowledge enables the developer to capture the behaviour of large pools of customers and to improve products and services by mixing and matching the functions and offering personalised promotions and marketing information. Originality/value The approach used in this study balances complexity with usability, thus facilitating corporate use of big data in making product improvement and customisation decisions. The approach allows developers to gain insights into customer behaviour and function usage preferences by analysing big data. The identified associations between functions can also help developers improve existing, and design new, products and services to satisfy customers’ unfulfilled requirements.
Original languageEnglish
Pages (from-to)425-438
JournalIndustrial Management & Data Systems
Issue number2
Publication statusPublished - 2017


  • Big data
  • Customer behaviour
  • Mobile app

Indexed by

  • Scopus
  • SSCI


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