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Time series analysis for C2C conversion rate

Author

Listed:
  • Ke Gong

    (Chongqing Jiaotong University
    University of Electronic Science and Technology of China)

  • Yi Peng

    (University of Electronic Science and Technology of China)

  • Yong Wang

    (Chongqing Jiaotong University)

  • Maozeng Xu

    (Chongqing Jiaotong University)

Abstract

The rise of online shopping by individuals in recent years has made e-commerce a crucial topic of interest in research and practice. The critical question in this domain is the extent to which online visits convert into purchases. Researchers have proposed decision models to predict consumer conversion behavior that primarily uses click-stream data, path data, panel data, and log data. This paper proposes an empirical mode decomposition (EMD) based ensemble recognition method for conversion rate (EMDER) to explore the potential pattern, business cycles in time series for conversion rate. EMDER builds on some notions, such as the database of candidate factors time series , recognition function, the recognized factor database, cycle function, and residue-trend recognition function. We collect 50 datasets from Taobao.com and find a seasonal pattern, Index of Clothing Consumer Price pattern and the long-term time series pattern with monthly data. For the daily analysis, we discover patterns in the calendar of daily fluctuation, the hesitation window, the consumers’ cash flow determined pattern, the promotion day and holiday influence. A comparison between EMD and Wavelet-based method is conducted, which reveals EMD outperforms the Wavelet-based model in the deposition quality and do not have the model-selection problem. The data analysis results provide support for the proposed method, which indicates that our model enables managers to analyze online consumer purchasing behavior by a new easy approaching way, which is time series of conversion rate.

Suggested Citation

  • Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:4:d:10.1007_s10660-017-9283-6
    DOI: 10.1007/s10660-017-9283-6
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    References listed on IDEAS

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    Cited by:

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    4. Aitor Goti & Leire Querejeta-Lomas & Aitor Almeida & José Gaviria de la Puerta & Diego López-de-Ipiña, 2023. "Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review," Mathematics, MDPI, vol. 11(13), pages 1-32, June.

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