  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.
