Title
DAliM: Machine Learning Based Intelligent Lucky Money Determination for Large-Scale E-Commerce Businesses.
Abstract
E-commerce businesses compete in the market by conducting marketing strategies consisting of four aspects: customers, products, marketplaces and intermediaries. One of the widely-used marketing strategies, called Lucky Money, is capable of encouraging customers to buy products from marketplaces. However, the amount of luck money for each customer is usually randomly determined or even manually determined and cannot fully achieve the business objectives. This paper proposes a machine-learning based lucky money determination approach, called DAliM, for e-commerce businesses to achieve their desired goals. We implement DAliM for the “Double 11 Global Shopping Festival 2017” initiated by Alibaba Group and evaluate it using a few hundred million real customers from all over the world. The experimental results demonstrate that our method manages to decrease the lucky money spent by 41.71% and increase the final purchase rate by 24.94% compared to the state-of-the-art baseline.
Year
Venue
Field
2018
ICSOC
Intermediary,Data mining,Computer science,Luck,Price optimization,Business objectives,E-commerce,Marketing,Price prediction
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
4
9
Name
Order
Citations
PageRank
Min Fu1214.33
Chi Man Wong200.68
Hai Zhu38722.69
Yanjun Huang47910.54
Yuanping Li5486.10
Xi Zheng615424.34
Jia Wu762065.55
Jian Yang853795.53
Chi Man Vong900.34