Customer reviews for demand distribution and sales nowcasting : a big data approach
Annals of Operations Research
Advance online publication
Springer New York LLC
Big data, Sales nowcasting, Short-run operation, Demand distribution
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.
Copyright © Springer Science+Business Media New York 2016
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See-To, E. W. K., & Ngai, E. W. T. (2016). Customer reviews for demand distribution and sales nowcasting: A big data approach. Annals of Operations Research. Advance online publication, 1-17. doi: 10.1007/s10479-016-2296-z