Essays in quantitative marketing
Abstract/Contents
- Abstract
- The first chapter explores the effect of personalized recommendations on consumer behavior. Personalized recommendations are known for their ability to navigate shoppers to the most relevant products first, saving their time. However, the hidden cost is that shoppers are less likely to find other desirable products along the search process serendipitously. Such a potential cost casts doubts on whether websites should adopt personalized recommendations. I suggest a positive spillover effect of gained efficiency from personalized recommendations: consumers explore more because increased search efficiency countervails an increasing opportunity cost of time. In addition, total shopping time is expected to decrease because the new equilibrium marginal benefit of exploration is lower. I examine these hypotheses empirically using field experiment data from one of China's biggest grocery delivery platforms. My findings are consistent with these hypotheses: consumers reduce search, spend more time exploring other categories and make more purchases while lowering their total shopping time. These findings are important because they show consumers active explorations under time pressure and they demonstrate a demand increasing mechanism of increasing search efficiency through personalized recommendations. The second chapter is joint work with Mingxi Zhu. This chapter studies information disclosure in auctions. Bidding in search advertising is commonplace today. However, determining a bid can be challenging in light of the complexity of the auction process. By designing the mechanism and aggregating the information of many bidders, the advertiser platform can assist less sophisticated advertisers. We analyze data from a platform that initiated a bid recommendation system and find that some advertisers may simply adopt the platform's suggestion instead of constructing their own bids. We discover that these less sophisticated advertisers were lower-rated and uncertain about ad effectiveness before the platform began offering information through the recommended bids. We characterize an equilibrium model of bidding in the Generalized Second Price (GSP) auction and show that following the platform's bid suggestion is theoretically sub-optimal. We then identify sophisticated and less sophisticated advertisers' private values using observed bids and the disclosed information. Counterfactual results suggest that the ad platform can increase revenue and the total surplus when it shares more information. Furthermore, the hybrid of auto-bidding with manual bidding could be a more efficient mechanism as we substitute less sophisticated bidding behavior for algorithmic bidding. These results shed light on the importance of exploring interactions between sophisticated and less sophisticated players when designing a market.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Song, Yingze |
|---|---|
| Degree supervisor | Hartmann, Wesley R. (Wesley Robert), 1973- |
| Thesis advisor | Hartmann, Wesley R. (Wesley Robert), 1973- |
| Thesis advisor | Ostrovsky, Michael |
| Thesis advisor | Somaini, Paulo |
| Degree committee member | Ostrovsky, Michael |
| Degree committee member | Somaini, Paulo |
| Associated with | Stanford University, Graduate School of Business |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Michelle Yingze Song. |
|---|---|
| Note | Submitted to the Gradaute School of Business. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/yq262vk4482 |
Access conditions
- Copyright
- © 2022 by Yingze Song
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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