Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2023, 6(8); doi: 10.25236/AJCIS.2023.060820.

Forecast of O2O Coupon Consumption Based on XGBoost Model


Weihan Yang, Zijie Zhang, Runze Liu, Jinjiang Liu

Corresponding Author:
Weihan Yang

Beijing 21st Century International School, Beijing, China


A precise delivery of coupon is an important way to engage existing customers or attract new ones to physical stores in O2O marketing approach. And a suitable strategy of coupon distribution can significantly heighten the user experience and facilitate coupon re-consumption. In this paper, we design a prediction model of O2O coupon usage based on XGBoost and compare the performance of XGBoost with another model based on the average AUC value. By contrast, the XGBoost performs better than the other model with 0.9584 average AUC value. So this model can help merchants to locate the target accurately.


O2O, Coupons, Machine learning, Prediction, XGBoost

Cite This Paper

Weihan Yang, Zijie Zhang, Runze Liu, Jinjiang Liu. Forecast of O2O Coupon Consumption Based on XGBoost Model. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 165-170. https://doi.org/10.25236/AJCIS.2023.060820.


[1] Partridge, K., Pathak, M. A., Uzun, E., & Wang, C. (2012, July). Picoda: Privacy-preserving smart coupon delivery architecture. In Proc. HotPETs (pp. 94-108). 

[2] Wu, J., Zhang, Y., & Wang, J. (2018). Research on Usage Prediction Methods for O2O Coupons. Neural Information Processing, 175–183. https://doi. org/10. 1007/978-3-030-04221-9_16

[3] Suo, Qiongyu. (2020). Prediction of O2O Coupon Usage Based on XGBoost Model. ICEME. https://doi. org/10. 1145/3414752. 3414775

[4] Alpar, P., & Winter, P. (2014). Comparison of redemption of print and electronic coupons. ACIS. 

[5] Zhang Chunfu, Wang Song, Wu Yadong, Wang Yong, Zhang Hongying. Diabetes risk prediction based on GA_Xgboost model [J]. Computer Engineering, 2020, 46 (03): 315-320. 

[6] Gu, Y., Gui, X., Xu, P., Gui, R., Zhao, Y., & Liu, W. (2018). A secure and targeted mobile coupon delivery scheme using blockchain. In Algorithms and Architectures for Parallel Processing: 18th International Conference, ICA3PP 2018, Guangzhou, China, November 15-17, 2018, Proceedings, Part IV 18 (pp. 538-548). Springer International Publishing. 

[7] Harmon, S. K., & Jeanne Hill, C. (2003). Gender and coupon use. Journal of Product & Brand Management, 12(3), 166-179. 

[8] Md. Abdul Hai, Rafsan Shartaj Uddin, Rahman, Y., & Raudatul Mahfuza. (2022). A Methodology for Recommending In-Vehicle Coupons Incorporating Machine Learning Algorithms for Efficient Financial Schemes. Proceedings of International Conference on Fourth Industrial Revolution and beyond 2021, 15–27. https://doi. org/10. 1007/978-981-19-2445-3_2

[9] Meherwar, Fatima, Maruf Pasha. (2017). Survey of Machine Learning Algorithms for Disease Diagnostic - Open Access Library. Www. oalib. com. https://www. oalib. com/paper/5282280

[10] Jiaxin, Gao, Zirui, Zhou, Jiangshan, Ai, Bingxin, Xia, Stephen, Coggeshall. (2019). Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms - Open Access Library. Www. oalib. com. https://www. oalib. com/paper/5420837

[11] Harvey Zheng. (2018). Analysis of Global Warming Using Machine Learning - Open Access Library. Www. oalib. com. https://www. oalib. com/paper/5298942

[12] Chen, T., He, T., & Benesty, M. (2016). Xgboost: extreme gradient boosting. 

[13] Li, W., Yin, Y., Quan, X., & Zhang, H. (2019). Gene Expression Value Prediction Based on XGBoost Algorithm. Frontiers in Genetics, 10. https://doi. org/10. 3389/fgene. 2019. 01077

[14] Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve? Emergency Medicine Journal, 34(6), 357-359. 

[15] Zhang, Z., & Jung, C. (2020). GBDT-MO: Gradient-boosted decision trees for multiple outputs. IEEE transactions on neural networks and learning systems, 32(7), 3156-3167.