Predicting customer behavioural patterns using a virtual credit card transactions dataset

Abstract

Nowadays, many businesses are resorting to data mining techniques on their data, to save costs and time, as well as to understand customers’ needs. Analysing such data can leader to higher profits and higher customer satisfaction. This paper presents a data mining study that is applied on millions of transactional records collected for a number of years, by a leading virtual credit card company based in Malta. In this study, 2 machine learning techniques, namely Artificial Neural Networks (ANN) and Gradient Boosting (GBM), are analysed to identify the best modelling framework that predicts the churning behaviour of this company’s customers. Apart from helping the marketing department of this firm by providing a model that predicts churning customers, we contribute to literature by identifying the minimum amount of customer activity needed to predict churn. In addition, we also analyse the “cold start” problem by performing a time-series experiment based on the few data available at the beginning of the customer purchase history.