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Time series analysis for C2C conversion rate

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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.

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References

  1. Moe, W. W., & Fader, P. S. (2004). Dynamic conversion behavior at e-commerce site’s. Management Science, 50(3), 326–335. https://doi.org/10.1287/mnsc.1040.0153.

    Article  Google Scholar 

  2. Sismeiro, C., & Bucklin, R. E. (2004). Modeling purchase behavior at an E-commerce web site: A task-completion approach. Journal of Marketing Research, 41(3), 306–323. https://doi.org/10.1509/jmkr.41.3.306.35985.

    Article  Google Scholar 

  3. Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2), 557–575.

    Article  Google Scholar 

  4. Bharati, P., & Chaudhury, A. (2004). An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision Support Systems, 37(2), 187–197. https://doi.org/10.1016/s0167-9236(03)00006-x.

    Article  Google Scholar 

  5. Hui, S. K., Fader, P. S., & Bradlow, E. T. (2009). Path data in marketing: An integrative framework and prospectus for model building. Marketing Science, 28(2), 320–335. https://doi.org/10.1287/mksc.1080.0400.

    Article  Google Scholar 

  6. Langer, N., Forman, C., Kekre, S., & Sun, B. H. (2012). Ushering buyers into electronic channels: An empirical analysis. Information Systems Research, 23(4), 1212–1231. https://doi.org/10.1287/isre.1110.0410.

    Article  Google Scholar 

  7. De, P., Hu, Y., & Rahman, M. S. (2010). Technology usage and online sales: An empirical study. Management Science, 56(11), 1930–1945. https://doi.org/10.1287/mnsc.1100.1233.

    Article  Google Scholar 

  8. Wikipedia (2013). Taobao. (pp. http://en.wikipedia.org/wiki/Taobao).

  9. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, physical and engineering sciences, 1998 (Vol. 454, pp. 903–995, Vol. 1971). The Royal Society

    Article  Google Scholar 

  10. Zhang, X., Yu, L., Wang, S., & Lai, K. K. (2009). Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method. Energy Economics, 31(5), 768–778. https://doi.org/10.1016/j.eneco.2009.04.003.

    Article  Google Scholar 

  11. Hui, S. K., Eliashberg, J., & George, E. I. (2008). Modeling DVD preorder and sales: An optimal stopping approach. Marketing Science, 27(6), 1097–1110. https://doi.org/10.1287/mksc.1080.0370.

    Article  Google Scholar 

  12. Mintz, O., Currim, I. S., & Jeliazkov, I. (2013). Information processing pattern and propensity to buy: An investigation of online point-of-purchase behavior. Marketing Science, 32(5), 716–732. https://doi.org/10.1287/mksc.2013.0790.

    Article  Google Scholar 

  13. Wang, H., Wei, Q., & Chen, G. Q. (2013). From clicking to consideration: A business intelligence approach to estimating consumers’ consideration probabilities. Decision Support Systems, 56, 397–405. https://doi.org/10.1016/j.dss.2012.10.052.

    Article  Google Scholar 

  14. Xu, L. Z., Duan, J. A., & Whinston, A. (2014). Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science, 60(6), 1392–1412. https://doi.org/10.1287/mnsc.2014.1952.

    Article  Google Scholar 

  15. Bucklin, R. E., Lattin, J. M., Ansari, A., Gupta, S., Bell, D., Coupey, E., et al. (2002). Choice and the internet: From clickstream to research stream. Marketing Letters, 13(3), 245–258. https://doi.org/10.1023/a:1020231107662.

    Article  Google Scholar 

  16. Park, J., & Chung, H. (2009). Consumers’ travel website transferring behaviour: Analysis using clickstream data-time, frequency, and spending. Service Industries Journal, 29(10), 1451–1463. https://doi.org/10.1080/02642060903026254.

    Article  Google Scholar 

  17. Olbrich, R., & Holsing, C. (2011). Modeling consumer purchasing behavior in social shopping communities with clickstream data. International Journal Of Electronic Commerce, 16(2), 15–40. https://doi.org/10.2753/jec1086-4415160202.

    Article  Google Scholar 

  18. Rutz, O. J., & Bucklin, R. E. (2012). Does banner advertising affect browsing for brands? Clickstream choice model says yes, for some. Qme-Quantitative Marketing And Economics, 10(2), 231–257. https://doi.org/10.1007/s11129-011-9114-3.

    Article  Google Scholar 

  19. Lin, L., Hu, P. J. H., Sheng, O. R. L., & Lee, J. (2010). Is stickiness profitable for electronic retailers? Communications of the ACM, 53(3), 132–136. https://doi.org/10.1145/1666420.1666454.

    Article  Google Scholar 

  20. Shao, Z., Chao, F., Yang, S.-L., & Zhou, K.-L. (2017). A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting. Renewable and Sustainable Energy Reviews, 75(Supplement C), 123–136. https://doi.org/10.1016/j.rser.2016.10.056.

    Article  Google Scholar 

  21. Jung, J., & Tam, K.-S. (2013). A frequency domain approach to characterize and analyze wind speed patterns. Applied Energy, 103(Supplement C), 435–443. https://doi.org/10.1016/j.apenergy.2012.10.006.

    Article  Google Scholar 

  22. Xu, W., Gu, R., Liu, Y., & Dai, Y. (2015). Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China. Economic Modelling, 45(Supplement C), 127–135. https://doi.org/10.1016/j.econmod.2014.11.011.

    Article  Google Scholar 

  23. Li, H., Yang, Z., Zheng, T. Q., Zhang, B., & Sun, H. Common-mode EMI suppression based on chaotic SPWM for a single-phase transformerless photovoltaic inverter. In 2014 16th European conference on power electronics and applications, 26–28 Aug. 2014 2014 (pp. 1–7). https://doi.org/10.1109/epe.2014.6910788.

  24. Jia, X., An, H., Fang, W., Sun, X., & Huang, X. (2015). How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective. Energy Economics, 49, 588–598. https://doi.org/10.1016/j.eneco.2015.03.008.

    Article  Google Scholar 

  25. Jiang, M., An, H., Jia, X., & Sun, X. (2017). The influence of global benchmark oil prices on the regional oil spot market in multi-period evolution. Energy, 118, 742–752. https://doi.org/10.1016/j.energy.2016.10.104.

    Article  Google Scholar 

  26. Sun, E. W., & Meinl, T. (2012). A new wavelet-based denoising algorithm for high-frequency financial data mining. European Journal of Operational Research, 217(3), 589–599. https://doi.org/10.1016/j.ejor.2011.09.049.

    Article  Google Scholar 

  27. Fan, G.-F., Peng, L.-L., Hong, W.-C., & Sun, F. (2016). Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173(Part 3), 958–970. https://doi.org/10.1016/j.neucom.2015.08.051.

    Article  Google Scholar 

  28. Chen, B., Zhao, S. L., & Li, P. Y. (2014). Application of Hilbert–Huang transform in structural health monitoring: A state-of-the-art review. Mathematical Problems In Engineering. https://doi.org/10.1155/2014/317954.

    Article  Google Scholar 

  29. Duan, W. H., Wang, Q., & Quek, S. T. (2010). Applications of piezoelectric materials in structural health monitoring and repair: Selected research examples. Materials, 3(12), 5169–5194. https://doi.org/10.3390/ma3125169.

    Article  Google Scholar 

  30. Xiong, T., Bao, Y. K., & Hu, Z. Y. (2013). Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Economics, 40, 405–415. https://doi.org/10.1016/j.eneco.2013.07.028.

    Article  Google Scholar 

  31. Yu, L. A., Wang, S. Y., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623–2635. https://doi.org/10.1016/j.eneco.2008.05.003.

    Article  Google Scholar 

  32. Zhang, X., Lai, K. K., & Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics, 30(3), 905–918. https://doi.org/10.1016/j.eneco.2007.02.012.

    Article  Google Scholar 

  33. Lin, C. S., Chiu, S. H., & Lin, T. Y. (2012). Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), 2583–2590. https://doi.org/10.1016/j.econmod.2012.07.018.

    Article  Google Scholar 

  34. Kozic, I., & Sever, I. (2014). Measuring business cycles: Empirical mode decomposition of economic time series. Economics Letters, 123(3), 287–290. https://doi.org/10.1016/j.econlet.2014.03.009.

    Article  Google Scholar 

  35. Lisi, F., & Nan, F. (2014). Component estimation for electricity prices: Procedures and comparisons. Energy Economics, 44, 143–159. https://doi.org/10.1016/j.eneco.2014.03.018.

    Article  Google Scholar 

  36. Chen, M. C., & Wei, Y. (2011). Exploring time variants for short-term passenger flow. Journal of Transport Geography, 19(4), 488–498. https://doi.org/10.1016/j.jtrangeo.2010.04.003.

    Article  Google Scholar 

  37. Huang, N. E., Shen, Z., & Long, S. R. (1999). A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 31, 417–457. https://doi.org/10.1146/annurev.fluid.31.1.417.

    Article  Google Scholar 

  38. Huang, N. E., & Wu, Z. H. (2008). A review on Hilbert–Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics. https://doi.org/10.1029/2007rg000228.

    Article  Google Scholar 

  39. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11(5), 341–356.

    Article  Google Scholar 

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Acknowledgements

Our work is supported by the National Science Foundation of China (Grant Nos. 71301180, 71402011), China Postdoctoral Science Foundation (Grant Nos. 2014M560711, 2015T80974), Basic and frontier technology projects of Chongqing Municipal (Grant No. cstc2017jcyjAX0105) and Science and Technology Research Project of Chongqing Municipal Education Commission (Grant No. KJ1705119). Professor Honghui Deng and Professor Reza Torkzadeh (University of Nevada Las Vegas) have contributed to the revised and polishing of this paper.The authors are very grateful for Professor Honghui Deng, Professor Reza Torkzadeh, and anonymous reviewers’ valuable suggestions.

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Authors and Affiliations

  1. School of Management, Chongqing Jiaotong University, Chongqing, People’s Republic of China

    Ke Gong, Yong Wang & Maozeng Xu

  2. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

    Ke Gong & Yi Peng

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  1. Ke Gong
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Correspondence to Ke Gong.

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Gong, K., Peng, Y., Wang, Y. et al. Time series analysis for C2C conversion rate. Electron Commer Res 18, 763–789 (2018). https://doi.org/10.1007/s10660-017-9283-6

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