Browsing by Author "Els, Zandaline"

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    Development of a data analystics-driven system for instant, temporary personalised discount offers
    (South African Institute for Industrial Engineering, 2018) Els, Zandaline; Bekker, James
    The innovation of targeting customers with personalised discount offers has been incorporated into business strategies in order to ensure a competitive advantage amongst peers along with ensuring customer experience. In this article, a demonstrator model was developed which provides a holistic view of an individual customer’s behaviour in retail outlets. The demonstrator creates instant, temporary personalised discount offers based on the purchasing tendencies of that customer in retail outlets. The model illustrates the utilisation of customer behavioural data and data analytics to identify unique cross-selling and upselling opportunities to ultimately improve customer experience. This article also includes the architecture of the proposed model along with the results from the demonstrator model.
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    Development of a data analytics-driven information system for instant, temporary personalised discount offers
    (Stellenbosch : Stellenbosch University, 2019-04) Els, Zandaline; Bekker, James F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Enterprises have started including the targeting of customers with personalised discount offers in their business strategies in order to seek a competitive advantage over their peers. This innovation has been made possible by the integration of knowledge and new technology such as data analytics, mobile- and cloud computing and the internet-of-things. Along with these digital technologies, the emphasis on customer experience became the distinguishing factor amongst retail outlets. A novel approach is presented in this study to create personalised discount offers during a customer's visit to one of many participating retail outlets. It focuses on the individual customer's purchasing history, which makes it different from the loyalty programmes that are currently in use. A simulator is developed to create pseudo-customer data containing purchasing behaviour, whereafter a demonstrator is developed which provides a holistic view of the customer's behaviour in retail outlets. The demonstrator creates instant, temporary personalised discount offers based on the purchasing tendencies of that customer across various retail outlets. The model illustrates the utilisation of customer behavioural data to identify unique cross-selling and upselling opportunities to ultimately improve customer experience. The cross-selling and upselling creates opportunities for alternative revenue streams and this study provides a business case to display the business value of this system.