Revenue-Maximizing Rankings for Online Platforms with Quality-Sensitive Consumers

Abstract : When a customer searches for a keyword at a classified ads website, at an online retailer, or at a search engine (SE), the platform has exponentially many choices in how to sort the output to the query. The two extremes are (a) to consider a ranking based on relevance only, which attracts more customers in the long run because of perceived quality, and (b) to consider a ranking based on the expected revenue to be generated by immediate conversions, which maximizes short-term revenue. Typically, these two objectives are not perfectly positively correlated and hence the main question is what middle ground between them should be chosen. We introduce stochastic models and propose effective solution methods that can be used to optimize the ranking considering long-term revenues. A key feature of our model is that customers are quality-sensitive and are attracted to the platform or driven away depending on the average relevance of the output. The proposed methods are of crucial importance in e-business and encompass: (i) classified ad websites which can favor paid ads by ranking them higher, (ii) online retailers which can rank products they sell according to buyers' interests and/or the margins these products have, (iii) SEs which can position the content that they serve higher in the output page than third-party content to keep users in their platforms for longer and earn more. This goes in detriment of just offering rankings based on relevance only and is directly linked to the current search neutrality debate.
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Operations Research, INFORMS, 2016
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Pierre L'Ecuyer, Patrick Maillé, Nicolás Stier-Moses, Bruno Tuffin. Revenue-Maximizing Rankings for Online Platforms with Quality-Sensitive Consumers. Operations Research, INFORMS, 2016. 〈hal-00953790v3〉

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