ABSTRACT
Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited budget by providing incentives such as coupons, commissions to merchants. As a result, incentive optimization is the key to maximizing the commercial objective of the marketing campaign. With the analyses of online experiments, we found that the transaction network can subtly describe the similarity of merchants' responses to different incentives, which is of great use in the incentive optimization problem. In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing. With limited samples collected from online experiments, our end-to-end method first learns merchant representations based on an attributed transaction networks, then effectively models the correlations between the commercial objectives each merchant may achieve and the incentives under varying treatments. Thus we are able to model the sensitivity to incentive for each merchant, and spend the most budgets on those merchants that show strong sensitivities in the marketing campaign. Extensive offline and online experimental results at Alipay demonstrate the effectiveness of our proposed approach.
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. TensorFlow: a system for large-scale machine learning. (2016).Google Scholar
- Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.Google ScholarDigital Library
- Fan RK Chung. 1997. Spectral graph theory . Number 92. American Mathematical Soc.Google Scholar
- Kenneth E Clow. 2004. Integrated advertising, promotion, and marketing communications .Pearson Education India.Google Scholar
- Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi. 2015. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management , Vol. 18, 1 (2015), 69--88.Google ScholarDigital Library
- Guillermo Gallego and Ruxian Wang. 2014. Multiproduct price optimization and competition under the nested logit model with product-differentiated price sensitivities. Operations Research , Vol. 62, 2 (2014), 450--461.Google ScholarDigital Library
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017a. Inductive Representation Learning on Large Graphs. arXiv preprint arXiv:1706.02216 (2017).Google Scholar
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation Learning on Graphs: Methods and Applications. arXiv preprint arXiv:1709.05584 (2017).Google Scholar
- Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola. 2008. Kernel methods in machine learning. The annals of statistics (2008), 1171--1220.Google Scholar
- Shinji Ito and Ryohei Fujimaki. 2017. Optimization beyond prediction: Prescriptive price optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1833--1841.Google ScholarDigital Library
- Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, and Yuan Qi. 2018. Geniepath: Graph neural networks with adaptive receptive paths. arXiv preprint arXiv:1802.00910 (2018).Google Scholar
- Rainer Schlosser and Martin Boissier. 2018. Dynamic pricing under competition on online marketplaces: A data-driven approach. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 705--714.Google ScholarDigital Library
- Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks , Vol. 61 (2015), 85--117.Google Scholar
- Ryan J Tibshirani et almbox. 2014. Adaptive piecewise polynomial estimation via trend filtering. The Annals of Statistics , Vol. 42, 1 (2014), 285--323.Google ScholarCross Ref
- Petar Velivc ković , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017. Graph Attention Networks. arXiv preprint arXiv:1710.10903 (2017).Google Scholar
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019).Google Scholar
Index Terms
- Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Recommendations
Promoting Mobile Payment with Price Incentives
Using a proprietary data set containing more than 10 million transactions over 14 months from hundreds of grocery stores, we examine consumer payment choices at point of sale (POS) between cash and mobile, where the use of other methods (including cards) ...
Interest-free Installment Payment Strategy for Retailers under Third Party Payment Platform
ICIBE '18: Proceedings of the 4th International Conference on Industrial and Business EngineeringWith the development of e-commerce, more and more people have opted for online shopping. At the same time, consumers' payment forms have also undergone tremendous changes. More and more people have opted for third-party payment platforms with ...
Comments