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Human dynamics in repurchase behavior based on comments mining

Author

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  • Yang, Tian
  • Feng, Xin
  • Wu, Ye
  • Wang, Shengfeng
  • Xiao, Jinghua

Abstract

Hundreds of thousands of individual deals and comments are analyzed to ask: what kinds of patterns appear in their repurchase process? Our results suggest that, in the empirical description, the intervals between two consecutive purchases obey a power-law distribution. Notwithstanding a wide range of individual preferences, shoppers’ repurchase behaviors show some similar patterns, called long-scale quiet and short-scale emergence, and the alternating appearance of them form an endless chain in repurchase. In agreement with the empirical results, these short-scale and long-scale patterns suggest an adaptive model with alterable exponents complying with a power-law distribution. And it also implies that each user behaves his own intrinsic pattern such as unique repurchase intensity and silence-emergence cycle, which contributes to customer life-time value from the new view of dynamics and repurchase cycles.

Suggested Citation

  • Yang, Tian & Feng, Xin & Wu, Ye & Wang, Shengfeng & Xiao, Jinghua, 2018. "Human dynamics in repurchase behavior based on comments mining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 563-569.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:563-569
    DOI: 10.1016/j.physa.2018.02.137
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    References listed on IDEAS

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    Cited by:

    1. Hwang, Syjung & Kim, Jina & Park, Eunil & Kwon, Sang Jib, 2020. "Who will be your next customer: A machine learning approach to customer return visits in airline services," Journal of Business Research, Elsevier, vol. 121(C), pages 121-126.

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