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Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market

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  • Ying Liu
  • Hong Li
  • Geng Peng
  • Benfu Lv
  • Chong Zhang

Abstract

Online customer segmentation is a significant research topic of customer relationship management. Previous literatures mainly studied the differences between non-purchasers and purchasers, lacking further segmentation of online purchasers. There is still existing significant heterogeneity within purchaser-groups. This paper focuses on Chinese online purchaser segmentation based on large volume of real transaction data on Taobao.com, we firstly extracted and investigated Chinese online purchaser behavior indicators and classified them into six types by cluster analysis, these six categories are: economical purchasers, active-star purchasers, direct purchasers, high-loyalty purchasers, risk-averse purchasers and credibility-first purchasers; then we built an empirical model to estimate the sensitivity of each type of online purchasers to three mainstream promotion strategies (discount, advertising and word-of-mouth), and found that economical purchasers are the most sensitive to discount promotion; direct purchasers are the most sensitive to advertising promotion; active-star purchasers are the most sensitive to word-of-mouth promotion; finally, the implications of online purchaser classification for marketing strategies were discussed. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Ying Liu & Hong Li & Geng Peng & Benfu Lv & Chong Zhang, 2015. "Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market," Annals of Operations Research, Springer, vol. 233(1), pages 263-279, October.
    • Handle: RePEc:spr:annopr:v:233:y:2015:i:1:p:263-279:10.1007/s10479-013-1443-z
    DOI: 10.1007/s10479-013-1443-z
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    Cited by:

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    2. Jie Wu & Zhixin Chen & Xiang Ji, 2020. "Sustainable trade promotion decisions under demand disruption in manufacturer-retailer supply chains," Annals of Operations Research, Springer, vol. 290(1), pages 115-143, July.
    3. Lei Li & Ting Chi & Tongtong Hao & Tao Yu, 2018. "Customer demand analysis of the electronic commerce supply chain using Big Data," Annals of Operations Research, Springer, vol. 268(1), pages 113-128, September.
    4. Alaleh Razmjoo & Petros Xanthopoulos & Qipeng Phil Zheng, 2019. "Feature importance ranking for classification in mixed online environments," Annals of Operations Research, Springer, vol. 276(1), pages 315-330, May.
    5. Nan Hu & Kevin E. Dow & Alain Yee Loong Chong & Ling Liu, 2018. "Double learning or double blinding: an investigation of vendor private information acquisition and consumer learning via online reviews," Annals of Operations Research, Springer, vol. 270(1), pages 213-234, November.
    6. Xinghua Fang & Jian Zhou & Hongya Zhao & Yizeng Chen, 2022. "A biclustering-based heterogeneous customer requirement determination method from customer participation in product development," Annals of Operations Research, Springer, vol. 309(2), pages 817-835, February.
    7. Madjid Tavana & Debora Di Caprio & Francisco J. Santos-Arteaga, 2016. "Loyal customer bases as innovation disincentives for duopolistic firms using strategic signaling and Bayesian analysis," Annals of Operations Research, Springer, vol. 244(2), pages 647-676, September.
    8. Farid Huseynov & Sevgi Özkan Yıldırım, 2019. "Online Consumer Typologies and Their Shopping Behaviors in B2C E-Commerce Platforms," SAGE Open, , vol. 9(2), pages 21582440198, May.
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    10. Qing Zhu & Renxian Zuo & Shan Liu & Fan Zhang, 2020. "Online dynamic group-buying community analysis based on high frequency time series simulation," Electronic Commerce Research, Springer, vol. 20(1), pages 81-118, March.
    11. Haoyu Liu & Kim Hua Tan & Xianfeng Wu, 2023. "Who’s watching? Classifying sports viewers on social live streaming services," Annals of Operations Research, Springer, vol. 325(1), pages 743-765, June.

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