Investigating Nudges toward Related Sellers on E-commerce Marketplaces: A Case Study on Amazon
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
TL;DR
The study conducts a comprehensive end-to-end audit of Amazon’s choice architectures to assess potential preferential treatment toward Related Sellers. By aggregating over 75,000 Buy Box competitions across four marketplaces, conducting cross-country surveys, and analyzing seller feedback (4M reviews), the authors show that Related Sellers win the Buy Box at high rates while customer preferences often diverge from algorithmic selections. They further demonstrate that Amazon’s strike-through policy can significantly distort reported seller metrics, and that rectified metrics reduce Related Sellers’ influence on customer decisions. The findings highlight fairness concerns in multisided marketplaces and underscore the need for uniform, transparent metric evaluation and policy design to align platform incentives with consumer welfare and competitive neutrality.
Abstract
E-commerce marketplaces provide business opportunities to millions of sellers worldwide. Some of these sellers have special relationships with the marketplace by virtue of using their subsidiary services (e.g., fulfillment and/or shipping services provided by the marketplace) -- we refer to such sellers collectively as Related Sellers. When multiple sellers offer to sell the same product, the marketplace helps a customer in selecting an offer (by a seller) through (a) a default offer selection algorithm, (b) showing features about each of the offers and the corresponding sellers (price, seller performance metrics, seller's number of ratings etc.), and (c) finally evaluating the sellers along these features. In this paper, we perform an end-to-end investigation into how the above apparatus can nudge customers toward the Related Sellers on Amazon's four different marketplaces in India, USA, Germany and France. We find that given explicit choices, customers' preferred offers and algorithmically selected offers can be significantly different. We highlight that Amazon is adopting different performance metric evaluation policies for different sellers, potentially benefiting Related Sellers. For instance, such policies result in notable discrepancy between the actual performance metric and the presented performance metric of Related Sellers. We further observe that among the seller-centric features visible to customers, sellers' number of ratings influences their decisions the most, yet it may not reflect the true quality of service by the seller, rather reflecting the scale at which the seller operates, thereby implicitly steering customers toward larger Related Sellers. Moreover, when customers are shown the rectified metrics for the different sellers, their preference toward Related Sellers is almost halved.