Customer purchase behavior prediction from payment datasets

With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Here we propose a probability graphical model that exploits the payment data to discover customer purchase behavior in the spatial, temporal, payment amount and product category aspects, named STPC-PGM. As a result, the mobility behavior of an individual user could be predicted with a probabilistic graphical model that accounts for all aspects of each customer's relationship with the payment platform. To achieve real time advertising, we then develop an online framework that efficiently computes the prediction results. Our experiment results show that STPC-PGM is effective in discovering customers' profiling features, and outperforms the state-of-the-art methods in purchase behavior prediction. In addition, the prediction results are being deployed in the marketing of real-world credit card users, and have presented a significant growth in the advertising conversion rate.