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Issue 5
Sep.  2020
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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 Sciences), 2020, (5): 167-178. doi: 10.3969/j.issn.1000-5641.202091016
Citation: 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 Sciences), 2020, (5): 167-178. doi: 10.3969/j.issn.1000-5641.202091016

Merchant churn prediction based on transaction data of aggregate payment platform

doi: 10.3969/j.issn.1000-5641.202091016
  • Received Date: 2020-08-16
    Available Online: 2020-09-24
  • Publish Date: 2020-09-24
  • 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.
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  • [1]
    BHATTACHARYA C B. When customers are members: Customer retention in paid membership contexts [J]. Journal of the Academy of Marketing Science, 1998, 26(1): 31-44.
    [2]
    REICHHELD F, DETRICK C. Loyalty: A prescription for cutting costs [J]. Marketing Management, 2003, 12(5): 24-24.
    [3]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
    [4]
    BAYTAS I M, XIAO C, ZHANG X, et al. Patient subtyping via time-aware LSTM networks [C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 65-74.
    [5]
    FENG W, TANG J, LIU T X. Understanding dropouts in MOOCs [C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 517-524.
    [6]
    FEI M, YEUNG D Y. Temporal models for predicting student dropout in massive open online courses [C]// 2015 IEEE International Conference on Data Mining Workshop. IEEE, 2015: 256-263.
    [7]
    YANG C, SHI X, JIE L, et al. I know you’ll be back: Interpretable new user clustering and churn prediction on a mobile social application [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 914-922.
    [8]
    LU Y, YU L, CUI P, et al. Uncovering the co-driven mechanism of social and content links in user churn phenomena [C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 3093-3101.
    [9]
    XIE Y, LI X, NGAI E W T, et al. Customer churn prediction using improved balanced random forests [J]. Expert Systems with Applications, 2009, 36(3): 5445-5449.
    [10]
    WEI C P, CHIU I T. Turning telecommunications call details to churn prediction: A data mining approach [J]. Expert Systems with Applications, 2002, 23(2): 103-112.
    [11]
    DASGUPTA K, SINGH R, VISWANATHAN B, et al. Social ties and their relevance to churn in mobile telecom networks [C]// Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology. 2008: 668-677.
    [12]
    HUANG Y, ZHU F, YUAN M, et al. Telco churn prediction with big data [C]// Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015: 607-618.
    [13]
    CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 785-794.
    [14]
    BAI T, ZHANG S, EGLESTON B L, et al. Interpretable representation learning for healthcare via capturing disease progression through time [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 43-51.
    [15]
    GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J]. Neural Networks, 2005, 18(5/6): 602-610.
    [16]
    CHO K, VAN MERRIËNBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [EB/OL]. (2014-09-03) [2020-07-05]. https://arxiv.org/pdf/1406.1078v3.pdf.
    [17]
    SRIVASTAVA N, MANSIMOV E, SALAKHUDINOV R. Unsupervised learning of video representations using LSTMs [C]// International Conference on Machine Learning. 2015: 843-852.
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