ALGO小时 - 利用来自在线零售市场的赞助广告信息|费施长

Algo小时
- 旧金山,加利福尼亚州

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Fei Long是UNC的营销助理教授,并在哥伦比亚商学院拥有博士学位。

Fei Long的研究兴趣在于了解营销中的重要和新兴现象,通常是数字经济驱动的。她使用理论建模和数据驱动方法。

她研究数字广告和电子商务平台,并在交叉路口问题。她也对机构理论和Salesforce补偿的主题感兴趣。

作为Hewlett-Packard / Columbia University的研究实习生,她帮助开发了一个统一的大数据框架进行定价,客户分割,新产品介绍和竞争分析。她还担任金融业的定量研究员,在那里她应用了机器学习,以预测对等贷款平台上的贷款违约率。

谈论摘要:

电子商务平台,如亚马逊和阿里巴巴,使数亿消费者能够在数百万独立卖方提供的搜索和购买产品,他们可以在平台上宣传产品。在本文中,我们研究了如何设计这种市场时,当有两种类型的不对称信息 - 卖家有一些关于他们的产品的私人信息,平台没有,而该平台有关于卖家没有的消费者的私人信息。使用博弈论模型,通过联合考虑以下要素,制定市场设计问题,以最大化市场利润(由广告收入和销售委员会组成):确保卖方加入平台,指定拍卖,使卖方能够在赞助列表中进行广告宣传,利用广告拍卖中透露的信息,以优化有机清单,并将佣金率设定给偏好,在其偏好中具有理性和异质的消费者。We show that sellers’ bids in ad auctions, through which sponsored slots are allocated, can reveal the sellers’ private information to the platform (“information effect”), which it can optimally combine with information that it has about consumers to improve the placement of organic results, a practice we call “strategic listing.” However, by introducing an externality between the sponsored and organic sides, strategic listing also leads to more competition in the ad auction (“competition effect”), thus reducing the incentive of sellers to join the platform. The platform can incentivize sellers to join by reducing the commission rate on sales; however, under certain conditions, the platform must also reduce the degree of strategic listing (i.e., commit to limiting the influence of the sponsored ad auction outcomes on the placement of organic results). If the sellers’ participation is sufficiently difficult to induce, the platform obtains a larger proportion (under some conditions, all) of its revenue from advertising than from sales commissions. Our results shed light on the variation in practices across different platforms and provide timely guidance for platforms to refine their marketplace design.

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