Investigating Nudges toward Related Sellers on E-commerce Marketplaces: A Case Study on Amazon

TL;DR

This study examines how Amazon's marketplace features and algorithms influence customer choices toward related sellers, revealing biases and the impact of visible seller metrics across four countries.

Contribution

It provides an end-to-end analysis of nudging mechanisms on Amazon, highlighting differences in algorithmic presentation and their effects on customer preferences for related sellers.

Findings

Customers' preferences differ significantly when explicit choices are provided.

Amazon's evaluation policies may favor related sellers through metric discrepancies.

Visible seller ratings heavily influence customer decisions, often favoring larger sellers.

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.