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Customer reviews for demand distribution and sales nowcasting: a big data approach

Author

Listed:
  • Eric W. K. See-To

    () (Hong Kong Polytechnic University)

  • Eric W. T. Ngai

    () (Hong Kong Polytechnic University)

Abstract

Abstract Proliferation of online social media and the phenomenal growth of online commerce have brought to us the era of big data. Before this availability of data, models of demand distribution at the product level proved elusive due to the ever shorter product life cycle. Methods of sales forecast are often conceived in terms of longer-run trends based on weekly, monthly or even quarterly data, even in markets with rapidly changing customer demand such as the fast fashion industry. Yet short-run models of demand distribution and sales forecasting (aka. nowcast) are arguably more useful for managers as the majority of their decisions are concerned with day to day discretionary spending and operations. Observations in the fast fashion market were acquired, for a collection time frame of about 1 month, from a major Chinese e-commerce platform at granular, half-daily intervals. We developed an efficient method to visualize the demand distributional characteristics; found that big data streams of customer reviews contain useful information for better sales nowcasting; and discussed the current influence pattern of sentiment on sales. We expect our results to contribute to practical visualization of the demand structure at the product level where the number of products is high and the product life cycle is short; revealing big data streams as a source for better sales nowcasting at the corporate and product level; and better understanding of the influence of online sentiment on sales. Managers may thus make better decisions concerning inventory management, capacity utilization, and lead and lag times in supply-chain operations.

Suggested Citation

  • Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
  • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2296-z
    DOI: 10.1007/s10479-016-2296-z
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    References listed on IDEAS

    as
    1. Maximo Camacho & Jaime Martinez-Martin, 2014. "Real-time forecasting US GDP from small-scale factor models," Empirical Economics, Springer, vol. 47(1), pages 347-364, August.
    2. Liao, Yi & Banerjee, Avijit & Yan, Changyuan, 2011. "A distribution-free newsvendor model with balking and lost sales penalty," International Journal of Production Economics, Elsevier, vol. 133(1), pages 224-227, September.
    3. Khouja, Moutaz, 1999. "The single-period (news-vendor) problem: literature review and suggestions for future research," Omega, Elsevier, vol. 27(5), pages 537-553, October.
    4. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    5. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    6. Marcelo Olivares & Christian Terwiesch & Lydia Cassorla, 2008. "Structural Estimation of the Newsvendor Model: An Application to Reserving Operating Room Time," Management Science, INFORMS, vol. 54(1), pages 41-55, January.
    7. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    8. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    9. Mostard, Julien & de Koster, Rene & Teunter, Ruud, 2005. "The distribution-free newsboy problem with resalable returns," International Journal of Production Economics, Elsevier, vol. 97(3), pages 329-342, September.
    10. Murphy Choy & Michelle L. F. Cheong, 2011. "Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting," Papers 1110.0062, arXiv.org.
    11. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.
    12. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    13. Hamid Ekbia & Michael Mattioli & Inna Kouper & G. Arave & Ali Ghazinejad & Timothy Bowman & Venkata Ratandeep Suri & Andrew Tsou & Scott Weingart & Cassidy R. Sugimoto, 2015. "Big data, bigger dilemmas: A critical review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(8), pages 1523-1545, August.
    14. Retsef Levi & Georgia Perakis & Joline Uichanco, 2015. "The Data-Driven Newsvendor Problem: New Bounds and Insights," Operations Research, INFORMS, vol. 63(6), pages 1294-1306, December.
    15. Tingliang Huang & Jan A. Van Mieghem, 2014. "Clickstream Data and Inventory Management: Model and Empirical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 23(3), pages 333-347, March.
    16. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    17. Thunyarat (Bam) Amornpetchkul & Izak Duenyas & Özge Şahin, 2015. "Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?," Production and Operations Management, Production and Operations Management Society, vol. 24(11), pages 1724-1749, November.
    18. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
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