TY - GEN
T1 - Monetary discount strategies for real-time promotion campaign
AU - Lin, Ying Chun
AU - Huang, Chi Hsuan
AU - Hsieh, Chu Cheng
AU - Shu, Yu Chen
AU - Chuang, Kun Ta
N1 - Funding Information:
This paper was supported in part by Ministry of Science and Technology, R.O.C., under Contract 105-2221-E-006-140-MY2. We also thank Joshua Borden for providing valuable editorial feedback to the paper.
Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2)
PY - 2017
Y1 - 2017
N2 - The eectiveness of monetary promotions has been well reported in the literature to aect shopping decisions for products in real life experience [3]. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that consumers can easily use a search engine to find another merchant selling an identical product for comparing price. We study e-commerce data — shopping receipts collected from email accounts, and conclude that for non-urgent products like books or electronics, buyers are price sensitive and are willing to delay the purchase for better deals. We then present a real-time promotion framework, called the RTP system: a one-time promoted discount price is oered to allure a potential buyer making a decision promptly. To achieve more eectiveness on real-time promotion in pursuit of better profits, we propose two discount-giving strategies: an algorithm based on Kernel density estimation, and the other algorithm based on Thompson sampling strategy. We show that, given a pre-determined discount budget, our algorithms can significantly acquire better revenue in return than classical strategies with simply fixed discount on label price. We then demonstrate its feasibility to be a promising deployment in e-commerce services for real-time promotion.
AB - The eectiveness of monetary promotions has been well reported in the literature to aect shopping decisions for products in real life experience [3]. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that consumers can easily use a search engine to find another merchant selling an identical product for comparing price. We study e-commerce data — shopping receipts collected from email accounts, and conclude that for non-urgent products like books or electronics, buyers are price sensitive and are willing to delay the purchase for better deals. We then present a real-time promotion framework, called the RTP system: a one-time promoted discount price is oered to allure a potential buyer making a decision promptly. To achieve more eectiveness on real-time promotion in pursuit of better profits, we propose two discount-giving strategies: an algorithm based on Kernel density estimation, and the other algorithm based on Thompson sampling strategy. We show that, given a pre-determined discount budget, our algorithms can significantly acquire better revenue in return than classical strategies with simply fixed discount on label price. We then demonstrate its feasibility to be a promising deployment in e-commerce services for real-time promotion.
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U2 - 10.1145/3038912.3052616
DO - 10.1145/3038912.3052616
M3 - Conference contribution
AN - SCOPUS:85046884909
SN - 9781450349130
T3 - 26th International World Wide Web Conference, WWW 2017
SP - 1123
EP - 1132
BT - 26th International World Wide Web Conference, WWW 2017
PB - International World Wide Web Conferences Steering Committee
T2 - 26th International World Wide Web Conference, WWW 2017
Y2 - 3 April 2017 through 7 April 2017
ER -