Abstract
Activating old customers or attracting new ones with coupons is a frequently used and very effective marketing tool in O2O (Online to Offline) businesses. But without careful analysis, large amount of coupons can be wasted because of inappropriate delivery strategies. In this era of big data, O2O coupons can be more precisely delivered by using history usage records of customers. By implementing the mainstream data mining and machine learning models, customers’ behaviors on O2O coupons can be predicted. Then as a result, individualized delivery can be performed. Coupons with particular discounts can be delivered to those customers who are more likely to use them. So coupon usage rate can be greatly increased. In this paper, multiple classification models are used to achieve this target. Experiments on real coupons’ usage data show that compared with other methods, the Random Forest model has better classification performance, and its accuracy rate of coupon usage prediction is the highest.
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Acknowledgments
This work is supported by ZJSTF-LGF18F020011, ZJSTF-2017C31038, and NSFC 61803337.
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Wu, J., Zhang, Y., Wang, J. (2018). Research on Usage Prediction Methods for O2O Coupons. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_16
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DOI: https://doi.org/10.1007/978-3-030-04221-9_16
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