Abstract
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.
Highlights
We live in a world that is rapidly changing when it comes to technology
Predicting a user-product pair’s purchase date using machine learning is possible. It can be seen in this paper that using a technique that takes all the data into account, including data from other userproduct pairs, performs better than the approach of predicting the next purchase date (NPD) by only using the data of one userproduct pair
This study shows how machine learning can be used to predict the purchase date for an individual retail customer
Summary
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 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. Hierdie soort voorspelling is egter baie moeilik, en hierdie artikel beskryfn studie wat met behulp van masjienleer-tegnieke ondersoek het wanneern kliënt vinnig-bewegende kleinhandel produkte sal koop. Hierdie aanbiedinge sal op deelnemende kliënte se mobiele toestelle aangebied word en uiteindelik sal veralgemeende, papiergebaseerde bemarking verminder word
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