网上杂货零售商的定价策略

Diego Aparicio, Zachary Metzman, R. Rigobón
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引用次数: 15

摘要

匹配的产品数据是从美国领先的在线杂货商收集的,相同的产品在扫描仪数据中被识别出来。这篇论文记录了在线(和离线)零售商内部和之间的定价策略。首先,与线下零售商相比,在线零售商的定价明显不统一。其次,在狭窄的地理区域内,竞争连锁店之间的在线价格差异高于线下零售商。第三,线下弹性、运输距离、定价频率和当地人口统计数据的变化被用来解释价格差异。令人惊讶的是,定价技术(跨越时间)放大了价格差异(跨越地点)。这一证据促使高频研究解开算法定价的模式。数据显示,算法:在送货邮编层面个性化价格,非常频繁且幅度很小的价格更新,减少价格同步,显示更低的菜单成本,不断探索价格网格,并且经常与竞争对手的价格相匹配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Pricing Strategies of Online Grocery Retailers
Matched product data is collected from the leading online grocers in the U.S. The same exact products are identified in scanner data. The paper documents pricing strategies within and across online (and offline) retailers. First, online retailers exhibit substantially less uniform pricing than offline retailers. Second, online price differentiation across competing chains in narrow geographies is higher than offline retailers. Third, variation in offline elasticities, shipping distance, pricing frequency, and local demographics are utilized to explain price differentiation. Surprisingly, pricing technology (across time) magnifies price differentiation (across locations). This evidence motivates a high-frequency study to unpack the patterns of algorithmic pricing. The data shows that algorithms: personalize prices at the delivery zipcode level, update prices very frequently and in tiny magnitudes, reduce price synchronization, exhibit lower menu costs, constantly explore the price grid, and often match competitors' prices.
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