Repurchase Prediction Based on Ensemble Learning

With the development of the network and the popularization of smart phones and computers, E-commerce is developing rapidly. In order to occupy more market shares, merchants sometimes run big promotions on particular dates. Unfortunately, many of the attracted buyers are one-time deal hunters, and these promotions may have little long lasting impact on sales. To alleviate this problem, it is important for merchants to identify who can be converted into repeated buyers. By targeting on these potential loyal customers, merchants can greatly reduce the promotion cost and enhance the return on investment. According to the idea of ensemble learning in machine learning, this paper proposes a two-layer model fusion algorithm (TMFBG) based on GBDT to predict repeat buyers. The algorithm is validated on the publication data from the behavior data of certain customers of the yearly ''Double 11'' on Tmall platform. The experimental results show that this fusion algorithm can improve the prediction precision and the model robustness.