Application of Data Platform

Merchant churn prediction based on transaction data of aggregate payment platform

  • XU Yiwen ,
  • LI Xiaoyang ,
  • DONG Qiwen ,
  • QIAN Weining ,
  • ZHOU Fang
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  • School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2020-08-16

  Online published: 2020-09-24

Abstract

In the field of aggregate payments, ensuring a low dropout rate of merchants on the platform is a key issue to reduce the overall platform operating cost and increase profit. This study focuses on the prediction of merchant churn for aggregate payment platforms and aims to help the platform reactivate potential churn merchants. The paper proposes a series of features that are highly relevant to merchant churn and applies a variety of traditional machine learning models for prediction. Given that the data analyzed contains sequential information, the study, moreover, applies LSTM-based techniques to address the prediction problem. Experimental results on a real dataset show that the proposed features have a certain predictive ability and the results are interpretable. And, the LSTM-based approaches are capable of capturing the timing characteristics in the data and further improve prediction results.

Cite this article

XU Yiwen , LI Xiaoyang , DONG Qiwen , QIAN Weining , ZHOU Fang . Merchant churn prediction based on transaction data of aggregate payment platform[J]. Journal of East China Normal University(Natural Science), 2020 , 2020(5) : 167 -178 . DOI: 10.3969/j.issn.1000-5641.202091016

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