Customer reviews for demand distribution and sales nowcasting: a big data approach. (English) Zbl 1412.62206
Summary: 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.
MSC:
| 62P20 | Applications of statistics to economics |
| 62M20 | Inference from stochastic processes and prediction |
| 62F03 | Parametric hypothesis testing |
References:
| [1] | AgilOne. (2014). AgilOne posts new data-driven marketing survey results. http://search.proquest.com.ezproxy.lb.polyu.edu.hk/docview/1476226999?OpenUrlRefId=info:xri/sid:primo&accountid=16210. |
| [2] | Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60, 255-265. · Zbl 0318.62075 · doi:10.1093/biomet/60.2.255 |
| [3] | Amornpetchkul, T., Duenyas, I., & Şahin, Ö. (2015). Mechanisms to induce buyer forecasting: Do suppliers always benefit from better forecasting? Production and Operations Management, 24, 1724-1749. · doi:10.1111/poms.12355 |
| [4] | Antipa, P., Barhoumi, K., Brunhes-Lesage, V., et al. (2012). Nowcasting German GDP: A comparison of bridge and factor models. Journal of Policy Modeling, 34, 864-878. · doi:10.1016/j.jpolmod.2012.01.010 |
| [5] | Babu, M. S. P., Sastry, S. H., IEEE. (2014). Big data and predictive analytics in ERP systems for automating decision making process. 2014 5th IEEE international conference on software engineering and service science (ICSESS), pp 259-262. |
| [6] | Balar, A., Malviya, N., Prasad, S., & Gangurde, A. (2013). Forecasting consumer behavior with innovative value proposition for organizations using big data analytics. In 2013 IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1-4). IEEE. |
| [7] | Banbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-casting and thereal-time data flow. European Central Bank (ECB), Working Paper No. 1564. |
| [8] | Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting with daily data. European Central Bank, Working Paper. |
| [9] | Booth, E., Mount, J., & Viers, J. H. (2006). Hydrologic variability of the Cosumnes River floodplain. San Francisco Estuary and Watershed Science 4. |
| [10] | Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information Communication and Society, 15, 662-679. · doi:10.1080/1369118X.2012.678878 |
| [11] | Bughin, J. (2015). Google searches and twitter mood: nowcasting telecom sales performance. NETNOMICS: Economic Research and Electronic Networking, 16, 87-105. · doi:10.1007/s11066-015-9096-5 |
| [12] | Buhl, H. U., Roglinger, M., Moser, F., et al. (2013). Big Data a fashionable topic with(out) sustainable relevance for research and practice?(Editorial). Business and Information Systems Engineering, 5, 65. · doi:10.1007/s12599-013-0249-5 |
| [13] | Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Berlin: Springer. · Zbl 1005.62007 |
| [14] | Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33, 261-304. · doi:10.1177/0049124104268644 |
| [15] | Camacho, M., & Martinez-Martin, J. (2014). Real-time forecasting US GDP from small-scale factor models. Empirical Economics, 47, 347-364. · doi:10.1007/s00181-013-0731-4 |
| [16] | Carriere-Swallow, Y., & Labbe, F. (2013). Nowcasting with Google trends in an emerging market. Journal of Forecasting, 32, 289-298. · doi:10.1002/for.1252 |
| [17] | Chen, Y. J., & Xiao, W. (2012). Impact of reseller’s forecasting accuracy on channel member performance. Production and Operations Management, 21, 1075-1089. · doi:10.1111/j.1937-5956.2012.01339.x |
| [18] | Chern, C.-C., Wei, C.-P., Shen, F.-Y., & Fan, Y. N. (2015). A sales forecasting model for consumer products based on the influence of online word-of-mouth. Information Systems and e-Business Management, 13(3), 445-473. · doi:10.1007/s10257-014-0265-0 |
| [19] | Choi, H., & Varian, H. (2012). Predicting the present with google trends. Economic Record, 88, 2-9. · doi:10.1111/j.1475-4932.2012.00809.x |
| [20] | Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2015). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research. doi:10.1080/00207543.2015.1066519. · doi:10.1080/00207543.2015.1066519 |
| [21] | Choy, M., Cheong, ML. (2011). Identification of demand through statistical distribution modeling for improved demand forecasting. arXiv:1110.0062 |
| [22] | Christopher, M., & Ryals, L. J. (2014). The supply chain becomes the demand chain. Journal of Business Logistics, 35, 29-35. · doi:10.1111/jbl.12037 |
| [23] | Chung, C., Niu, S.-C., & Sriskandarajah, C. (2012). A sales forecast model for short-life-cycle products: New releases at blockbuster. Production and Operations Management, 21, 851-873. · doi:10.1111/j.1937-5956.2012.01326.x |
| [24] | Cox, M., Ellsworth, D. (1997). Application-controlled demand paging for out-of-core visualization. Proceedings of the 8th conference on Visualization’97. IEEE Computer Society Press, 235-ff. |
| [25] | Cui, G., Lui, H.-K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17, 39-58. · doi:10.2753/JEC1086-4415170102 |
| [26] | Dias, F., Pinheiro, M., & Rua, A. (2015). Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence. Economic Modelling, 44, 266-272. · doi:10.1016/j.econmod.2014.10.034 |
| [27] | Ekbia, H., Mattioli, M., Kouper, I., et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66, 1523-1545. · doi:10.1002/asi.23294 |
| [28] | Fang, H., Zhang, Z. Y., Wang, C. J., et al. (2015). A survey of big data research. IEEE Network, 29, 6-9. · doi:10.1109/MNET.2015.7293298 |
| [29] | Felix S. (2015). Top online marketplaces for small businesses selling internationally. The Endica Blog. http://online-shipping-blog.endicia.com/top-online-marketplaces-for-small-businesses-selling-internationally/ |
| [30] | Guo, Z., Wong, W. K., & Li, M. (2013). A multivariate intelligent decision-making model for retail sales forecasting. Decision Support Systems, 55, 247-255. · doi:10.1016/j.dss.2013.01.026 |
| [31] | Hirashima, A., Jones, J., Bonham, CS., et al. (2015). Nowcasting tourism industry performance using high frequency covariates (No. 2015-3). University of Hawaii Economic Research Organization, University of Hawaii at Manoa. |
| [32] | Huang, T., & Van Mieghem, J. A. (2014). Clickstream data and inventory management: Model and empirical analysis. Production and Operations Management, 23, 333-347. · doi:10.1111/poms.12046 |
| [33] | Johansson, M. A., Powers, A. M., Pesik, N., Cohen, N. J., & Staples, J. E. (2014). Nowcasting the spread of chikungunya virus in the Americas. PloS one, 9(8), e104915. · doi:10.1371/journal.pone.0104915 |
| [34] | Khouja, M. (1999). The single-period (news-vendor) problem: Literature review and suggestions for future research. Omega, 27, 537-553. · doi:10.1016/S0305-0483(99)00017-1 |
| [35] | Kim, W., Won, J. H., Park, S., & Kang, J. (2015). Demand forecasting models for medicines through wireless sensor networks data and topic trend analysis. International Journal of Distributed Sensor Networks, 2015, 36. |
| [36] | Kumaran, M., & Achary, K. K. (1996). On approximating lead time demand distributions using the generalised \[\lambda\] λ-type distribution. Journal of the Operational Research Society, 47(3), 395-404. · Zbl 0852.90063 |
| [37] | Lampos, V., Miller, AC., Crossan, S., et al. (2015). Advances in nowcasting influenza-like illness rates using search query logs. Scientific Reports 5. |
| [38] | Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70. |
| [39] | Lassen, NB., Madsen, R., Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. In: Reichert, M., Rinderle-Ma, S. and Grossmann, G. (Eds.), Proceedings of the 2014 IEEE 18th international enterprise distributed object computing conference, pp 81-90. |
| [40] | Levi, R., Perakis, G., & Uichanco, J. (2015). The data-driven newsvendor problem: New bounds and insights. Operations Research, 63(6), 1294-1306. · Zbl 1333.90010 · doi:10.1287/opre.2015.1422 |
| [41] | Li, J. R., Tao, F., Cheng, Y., et al. (2015). Big data in product lifecycle management. International Journal of Advanced Manufacturing Technology, 81, 667-684. · doi:10.1007/s00170-015-7151-x |
| [42] | Liao, Y., Banerjee, A., & Yan, C. (2011). A distribution-free newsvendor model with balking and lost sales penalty. International Journal of Production Economics, 133, 224-227. · doi:10.1016/j.ijpe.2010.04.024 |
| [43] | Lu, C.-J., & Chang, C.-C. (2014). A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression. The Scientific World Journal, 55, 231-238. |
| [44] | Ma, Q., & Zhang, W. (2015). Public mood and consumption choices: Evidence from sales of sony cameras on taobao. PloS one, 10(4), e0123129. · doi:10.1371/journal.pone.0123129 |
| [45] | McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90, 60-68. |
| [46] | Mishra, B. K., Raghunathan, S., & Yue, X. (2009). Demand forecast sharing in supply chains. Production and Operations Management, 18, 152-166. · doi:10.1111/j.1937-5956.2009.01013.x |
| [47] | Moon, I., & Choi, S. (1995). The distribution free newsboy problem with balking. Journal of the Operational Research Society, 46(4), 537-542. · Zbl 0830.90039 · doi:10.1057/jors.1995.73 |
| [48] | Mostard, J., De Koster, R., & Teunter, R. (2005). The distribution-free newsboy problem with resalable returns. International Journal of Production Economics, 97, 329-342. · doi:10.1016/j.ijpe.2004.09.003 |
| [49] | Olivares, M., Terwiesch, C., & Cassorla, L. (2008). Structural estimation of the newsvendor model: an application to reserving operating room time. Management Science, 54, 41-55. · doi:10.1287/mnsc.1070.0756 |
| [50] | Osadchiy, N., Gaur, V., & Seshadri, S. (2013). Sales forecasting with financial indicators and experts’ Input. Production and Operations Management, 22, 1056-1076. |
| [51] | Puts, M., Daas, P., & de Waal, T. (2015). Finding errors in big data. Significance, 12, 26-29. · doi:10.1111/j.1740-9713.2015.00826.x |
| [52] | Sanders, N. R., & Ganeshan, R. (2015). Special issue of production and operations management on big data in supply chain management. Production and Operations Management, 24, 852-853. · doi:10.1111/poms.12381 |
| [53] | Snijders, C., Matzat, U., & Reips, U.-D. (2012). Big data: Big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7, 1-5. |
| [54] | Su, Z. F., Wang, X., & He, K. (2014). Nowcasting and short-term forecasting of Chinese quarterly GDP: Mixed frequency approach. Anthropologist, 17, 53-63. · doi:10.1080/09720073.2014.11891413 |
| [55] | Tan, K. H., Zhan, Y., Ji, G., et al. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233. · doi:10.1016/j.ijpe.2014.12.034 |
| [56] | Wagenmakers, E.-J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin and Review, 11, 192-196. · doi:10.3758/BF03206482 |
| [57] | Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist: Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34, 249-252. · doi:10.1111/jbl.12024 |
| [58] | Walsh, B. (2014). Google’s Flu Project shows the failings of big data. Time.com: 1. |
| [59] | Weinberger, D. (2014). Too big to know: Rethinking knowledge now that the facts aren’t the facts, experts are everywhere, and the smartest person in the room is the room. New York: Basic Books. |
| [60] | Wiesemann, W., Kuhn, D., & Sim, M. (2014). Distributionally robust convex optimization. Operations Research, 62, 1358-1376. · Zbl 1327.90158 · doi:10.1287/opre.2014.1314 |
| [61] | Yang, L., Xiangji, H., & Aijun, A. (2007). A sentiment-aware model for predicting sales performance using blogs. Proc SIGIR. pp. 607-615. |
| [62] | Yu, Y., Choi, T.-M., & Hui, C.-L. (2011). An intelligent fast sales forecasting model for fashion products. Expert Systems With Applications, 38, 7373-7379. · doi:10.1016/j.eswa.2010.12.089 |
| [63] | Zhou, Y., Wei, M., Cheng, Z. J., et al. (2013). The wind and temperature information of AMDAR data applying to the analysis of severe weather nowcasting of airport. International Conference on Information Science and Technology, 2013, 1005-1010. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
