Product Ranking on Online Platforms
<jats:p> On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develo...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144169 |
| _version_ | 1811095154862325760 |
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| author | Derakhshan, Mahsa Golrezaei, Negin Manshadi, Vahideh Mirrokni, Vahab |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Derakhshan, Mahsa Golrezaei, Negin Manshadi, Vahideh Mirrokni, Vahab |
| author_sort | Derakhshan, Mahsa |
| collection | MIT |
| description | <jats:p> On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator. </jats:p><jats:p> This paper was accepted by Gabriel Weintraub, revenue management and market analytics. </jats:p> |
| first_indexed | 2024-09-23T16:11:31Z |
| format | Article |
| id | mit-1721.1/144169 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:11:31Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1441692023-01-18T20:30:44Z Product Ranking on Online Platforms Derakhshan, Mahsa Golrezaei, Negin Manshadi, Vahideh Mirrokni, Vahab Sloan School of Management <jats:p> On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator. </jats:p><jats:p> This paper was accepted by Gabriel Weintraub, revenue management and market analytics. </jats:p> 2022-08-01T15:12:19Z 2022-08-01T15:12:19Z 2022 2022-08-01T14:47:11Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144169 Derakhshan, Mahsa, Golrezaei, Negin, Manshadi, Vahideh and Mirrokni, Vahab. 2022. "Product Ranking on Online Platforms." Management Science, 68 (6). en 10.1287/MNSC.2021.4044 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) SSRN |
| spellingShingle | Derakhshan, Mahsa Golrezaei, Negin Manshadi, Vahideh Mirrokni, Vahab Product Ranking on Online Platforms |
| title | Product Ranking on Online Platforms |
| title_full | Product Ranking on Online Platforms |
| title_fullStr | Product Ranking on Online Platforms |
| title_full_unstemmed | Product Ranking on Online Platforms |
| title_short | Product Ranking on Online Platforms |
| title_sort | product ranking on online platforms |
| url | https://hdl.handle.net/1721.1/144169 |
| work_keys_str_mv | AT derakhshanmahsa productrankingononlineplatforms AT golrezaeinegin productrankingononlineplatforms AT manshadivahideh productrankingononlineplatforms AT mirroknivahab productrankingononlineplatforms |