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Title: 概率商品銷售策略與購物平台獲利分析
Probabilistic Goods Sales Strategy and Profit Analysis of Shopping Platform
Authors: 徐靈迪
Xu, Ling-Di
Contributors: 莊皓鈞

Chuang, Hao-Chun
Chou, Yen-Chun

Xu, Ling-Di
Keywords: 概率銷售
Agent-based modeling
Probabilistic Selling
Agent-based modeling
Online shopping platform
Computer simulation
Date: 2022
Issue Date: 2022-05-02 15:00:44 (UTC+8)
Abstract: 如何設計創新的方式以提升銷售利潤,並達成供需匹配是企業營運不變的目標,概率銷售(Probabilistic Selling)作為一種新興的銷售手法,多見於線上旅宿平台如Priceline、Hotwire等,先前研究表明其能夠減少需求與產能之間的不匹配,提高產能利用率(Fay & Xie, 2008),但過去理論文獻多使用霍特林經濟模型(Hotelling Model),探討在壟斷環境下的概率銷售。本研究則認為概率商品不應僅局限在壟斷環境下,能有更廣闊的應用,進而探討概率銷售在線上購物平台的運用。我們設計二維平面捕捉產品品質、品牌和價格的差異,並量化消費者個體偏好的異質性,採用agent-based modeling方法,將消費者視為異質的agents,模擬其個別面對概率商品的購物決策,彙整所有個體決策差異後得到平台收益。我們透過一連串的模擬實驗分析概率銷售是否能顯著提升購物平台的獲利,並挖掘消費者的偏好彈性和產品價格型態等因子與概率銷售獲利程度的關係。本論文拓展了文獻中概率銷售有限的應用情境,並且本研究的發現可幫助平台針對不同情境應用概率銷售,有效提高銷售利潤。
How to design innovative ways to improve sales profit and achieve supply-demand matching is the constant goal of enterprise operation. As an emerging sales method, Probabilistic Selling is commonly found on online travel platforms such as Priceline and Hotwire. Previous studies have shown that it can reduce the mismatch between demand and capacity and improve capacity utilization rate (Fay & Xie, 2008), but in the past theoretical literature, Hotelling Model was mostly used to discuss Probabilistic Selling in a monopoly environment. This study argues that probabilistic goods should not only be limited to monopoly environments, but can be more widely used, and then explore the application of Probabilistic Selling on online shopping platforms. We designed a two-dimensional plane to capture the differences in product quality, brand with price, and quantified the heterogeneity of consumers' individual preferences. Using agent-based modeling method, consumers were regarded as heterogeneous agents to simulate their individual purchasing decisions of probabilistic goods, and the platform benefits were obtained by aggregating all individual decision-making differences. Through a series of simulation experiments, we analyze whether Probabilistic Selling can significantly improve the profit of shopping platform. We also explore the relationship between consumer preference elasticity as well as product price type and the profit of Probabilistic Selling. This paper expands the limited application scenarios of Probabilistic Selling in literature, and the research findings can help platforms apply Probabilistic Selling in different situations on online shopping platforms and effectively improve sales profits.
Reference: Anderson, C. K., & Xie, X. (2012). A choice‐based dynamic programming approach for setting opaque prices. Production and Operations Management, 21(3), 590–605.
Chamberlin, E. H. (1949). Theory of monopolistic competition: A re-orientation of the theory of value. Oxford University Press, London.
Chernev, A. (2003). Reverse Pricing and Online Price Elicitation Strategies in Consumer Choice. Journal of Consumer Psychology, 13(1), 51–62.
Elmachtoub, A. N., Wei, Y., & Zhou, Y. (2015). Retailing with opaque products. Available at SSRN 2659211.
Fay, S. (2004). Partial-Repeat-Bidding in the Name-Your-Own-Price Channel. Marketing Science, 23(3), 407–418.
Fay, S. (2008). Selling an opaque product through an intermediary: The case of disguising one’s product. Journal of Retailing, 84(1), 59–75.
Fay, S., & Xie, J. (2008). Probabilistic Goods: A Creative Way of Selling Products and Services. Marketing Science, 27(4), 674–690.
Fay, S., & Xie, J. (2015). Timing of Product Allocation: Using Probabilistic Selling to Enhance Inventory Management. Management Science, 61(2), 474–484.
Gönsch, J. (2020). How much to tell your customer? – A survey of three perspectives on selling strategies with incompletely specified products. European Journal of Operational Research, 280(3), 793–817.
Granados, N., Gupta, A., & Kauffman, R. J. (2008). Designing online selling mechanisms: Transparency levels and prices. Decision Support Systems, 45(4), 729–745.
Huang, T., & Yu, Y. (2014). Sell Probabilistic Goods? A Behavioral Explanation for Opaque Selling. Marketing Science, 33(5), 743–759.
Jerath, K., Netessine, S., & Veeraraghavan, S. K. (2010). Revenue Management with Strategic Customers: Last-Minute Selling and Opaque Selling. Management Science, 56(3), 430–448.
Lamberson, P. J. (2016). Winner-take-all or long tail? A behavioral model of markets with increasing returns: Winner-take-all or Long Tail? System Dynamics Review, 32(3–4), 233–260.
Mang, S., Post, D., & Spann, M. (2012). Pricing of flexible products. Review of Managerial Science, 6(4), 361–374.
Ren, H., & Huang, T. (2017b). Opaque selling and last-minute selling: Revenue management in vertically differentiated markets. Available at SSRN 3060001.
Spann, M., & Tellis, G. J. (2006). Does the Internet Promote Better Consumer Decisions? The Case of Name-Your-Own-Price Auctions. Journal of Marketing, 70(1), 65–78.
Wagner, H. M. (1995). Global Sensitivity Analysis. Operations Research, 43(6), 948–969.
W.-J, H., C.-C, C., & Y.M, L. (2018). Five-star quality at three-star prices? Opaque booking and hotel service expectations. Journal of Hospitality Marketing and Management, 27(7), 833–854.
Xu, X., Lian, Z., Li, X., & Guo, P. (2016). A Hotelling queue model with probabilistic service. Operations Research Letters, 44(5), 592–597.
Zhang, Z., Joseph, K., & Subramaniam, R. (2015). Probabilistic Selling in Quality-Differentiated Markets. Management Science, 61(8), 1959–1977.
Zouaoui, F., & Venkateshwara Rao, B. (2009). Dynamic pricing of opaque airline tickets. Journal of Revenue and Pricing Management, 8(2), 148–154.
Description: 碩士
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Data Type: thesis
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