Online stores’ ranking goals produced fewer trade-offs than expected

A study found that rankings aimed at revenue, sales or shopper welfare could improve all three measures relative to current and neutral rankings.

Editorial illustration — not from the study.

The study examines how product rankings change when an online retailer prioritizes commission or markup revenue, the number of transactions, or consumer welfare. Descriptive evidence showed that lower-priced and higher-utility products receive more demand when placed higher in rankings. That pattern suggests that promoting those products can increase transactions and consumer welfare, although it may reduce revenue in some cases.

The researchers then developed and estimated a model of how consumers search for and discover products. Using that model, they constructed hypothetical rankings for each objective. All of these rankings increased consumer welfare, transactions and platform revenue compared with a neutral benchmark and the existing ranking system. The estimated trade-offs between the ranking goals were limited.

What the rankings changed

Higher-ranked, lower-priced and higher-utility products attracted more demand in the descriptive evidence. Ranking those products more prominently was associated with higher transactions and consumer welfare, while potentially reducing revenue.

In the structural demand model, the researchers created hypothetical rankings focused on platform revenue, transactions and consumer welfare. Each of these rankings increased all three outcomes—consumer welfare, transactions and platform revenue—relative to both a neutral benchmark and the status quo. The trade-offs between the different ranking objectives were limited.

Why the trade-offs matter

Product-ranking decisions are often framed as a choice between helping shoppers, increasing sales and raising platform revenue. These results suggest that, in the setting represented by the researchers’ model, those goals did not conflict as strongly as expected. The finding is relevant to online retailers and platforms deciding how to order products for users.

Evidence and caveats

The paper combines descriptive evidence with a structural demand model that represents how consumers search for and discover products. The conclusions about the different ranking objectives come from estimated, hypothetical rankings rather than a reported real-world test of each ranking system. The abstract does not specify the underlying marketplace, data size or how broadly the results apply beyond the setting studied.

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