Group for Research in Decision Analysis


Revenue-maximizing rankings for online platforms with quality-sensitive consumers

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When a keyword-based search query is received by a search engine (SE), a classified ads website, or an online retailer site, the platform has exponentially many choices in how to sort the output to the query. Two extreme rules are (a) to return a ranking based on relevance only, which attracts more requests (customers) in the long run because of perceived quality, and (b) to return a ranking based only on the expected revenue to be generated by immediate conversions, which maximizes short-term revenue. Typically, these two objectives (and the corresponding rankings) differ. A key question then is what middle ground between them should be chosen. We introduce stochastic models that yield elegant solutions for this situation, and we propose effective solution methods to compute a ranking strategy that optimizes long-term revenues. This strategy has a very simple form, which provides valuable insight. 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) SEs that can reorder their organic output and place their own content in more prominent positions than that provided by third-parties, to attract more traffic to their content and increase their expected earnings as a result; (ii) classified ad websites which can favor paid ads by ranking them higher; and (iii) online retailers which can rank products they sell according to buyers' interests and also the margins these products have. This goes in detriment of just offering rankings based on relevance only and is directly linked to the current search neutrality debate.

, 29 pages