From Anticipation to Action: Data Reveal Mobile Shopping Patterns During a Yearly Mega Sale Event in China

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The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world's largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals.
Publisher
IEEE COMPUTER SOC
Issue Date
2022-04
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.34, no.4, pp.1775 - 1787

ISSN
1041-4347
DOI
10.1109/TKDE.2020.3001558
URI
http://hdl.handle.net/10203/292551
Appears in Collection
CS-Journal Papers(저널논문)
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