Predicting Consumer Behaviour in Digital Market: A Machine Learning Approach

Abstract

In recent times, customer behaviour models are typically based on data mining of customer data, and each model is designed to answer one question at one point in time. Predicting customer behaviour is an uncertain and difficult task. Thus, developing customer behaviour models requires the right technique and approach. Once a prediction model has been built, it is difficult to manipulate it for the purposes of the marketer, so as to determine exactly what marketing actions to take for each customer or group of customers. Despite the complexity of this formulation, most customer models are actually relatively simple. Because of this necessity, most customer behaviour models ignore so many pertinent factors that the predictions they generate are generally not very reliable. This paper aims to develop an association rule mining model to predict customer behaviour using a typical online retail store for data collection and extract important trends from the customer behaviour data.