Prediction of Consumer Purchasing in a Grocery Store Using Machine Learning Techniques

Over the past decades, prediction of costumers' purchase behavior has been significantly considered, and completely recognized as one of the most significant research topics in consumer behavior researches. While we attempt to measure response of purchase intention to the contextual factors such as customers' age, gender and income, product price and sale promotion, most of business models are basing on a linear equation to estimate weight of these factors due to the linear equation is not only intuitive for other academics to compare and replicate but also luminous to explain the results for business practitioners. Nevertheless, comparing with other research fields (e.g. pattern recognition and text classification), the prediction methods for purchase behavior are overconcentration of the linear models, especially linear discriminant analysis and logistic regression analysis. On the other hand, as more and more information and communication technologies (ICT, e.g. POS and sensor) are introduced into retail, marketing and management to collect business data, the volumes of data are increasing in exponential growth. Analysis based on linear models are insufficient to satisfy the requirement of academics and practitioners any more, and machine learning techniques have been increasingly attracted us to conduct them as an alternative approach for knowledge discovery and data mining. With regard to these issues, this paper employs two representative machine learning methods: Bayes classifier and support vector machine (SVM) and investigates the performance of them with the data in the real world.