Location-based Hybrid Deep Learning Model for Purchase Prediction

Abstract

Consumer purchase prediction is of great significance for reducing marketing costs and improving return on investment of companies. Recently, spatial-temporal data mining has aroused increasing concern. In this paper, we propose a hybrid deep learning model (EE-CNN) for purchase prediction, which combines entity embedding and convolutional neural networks. In empirical experiments, we first explore the purchase location pattern of different consumer groups on data sets from a retail company of China. After that, our proposed EE-CNN model is utilized to predict consumer purchase behavior. It turns out that location data can help improve the performance of purchase prediction models in general. Meanwhile, our proposed EE-CNN model outperforms baselines used in the experiments. Our research provides significant guidelines for the marketing decisions of enterprise marketers.