SciELO - Scientific Electronic Library Online

 
vol.31 issue3Improved energy budgeting process using measurement and verification principlesExploring factors that influence the mainstreaming of gendered energy interventions in poor urban environments: a structured literature review author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • On index processSimilars in Google

Share


South African Journal of Industrial Engineering

On-line version ISSN 2224-7890
Print version ISSN 1012-277X

Abstract

DROOMER, M.  and  BEKKER, J.. Using machine learning to predict the next purchase date for an individual retail customer. S. Afr. J. Ind. Eng. [online]. 2020, vol.31, n.3, pp.69-82. ISSN 2224-7890.  http://dx.doi.org/10.7166/31-3-2419.

Targeted marketing has become more popular over the last few years, and knowing when a customer will require a product can be of enormous value to a company. However, predicting this is a difficult task. This paper reports on a study that investigates predicting when a customer will buy fast-moving retail products, by using machine learning techniques. This is done by analysing the purchase history of a customer at participating retailers. These predictions will be used to personalise discount offers to customers when they are about to purchase items. Such offers will be delivered on the mobile devices of participating customers and, ultimately, physical, general paper-based marketing will be reduced.

        · abstract in Afrikaans     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License