Temporary deals such as flash sales nowadays are popular strategies in retail business for cleaning out excessive inventories. It is known that the success of a temporary deal is related to product quality, promotion, and discount rates. In this paper, we look at another more obscure factor, that is the timing in the market, and we argue that such timing can be learned from social media. We propose an approach to detect emerging words in social media as timing signals, and associate them with successful temporary deals. We also deploy an algorithm to select a more effective subset of signals and combine them to improve prediction accuracy. With real-world temporary deal and social media datasets, we show the effectiveness of our approach with case studies. Furthermore, in a prediction framework, we show that using social media timing signals can achieve better accuracy for predicting temporary deal success, comparing to internal deal information.
Zhang, Yihong; Hara, Takahiro; and Shirakawa, Masumi, "Discovering Social Media Timing Signals for Predicting Temporary Deal Success" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
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