  This paper presents a bidding system for sponsored search auctions under an
unknown valuation model. This formulation assumes that the bidder's value is
unknown, evolving arbitrarily, and observed only upon winning an auction.
Unlike previous studies, we do not impose any assumptions on the nature of
feedback and consider the problem of bidding in sponsored search auctions in
its full generality. Our system is based on a bandit framework that is
resilient to the black-box auction structure and delayed and batched feedback.
To validate our proposed solution, we conducted a case study at Zalando, a
leading fashion e-commerce company. We outline the development process and
describe the promising outcomes of our bandits-based approach to increase
profitability in sponsored search auctions. We discuss in detail the technical
challenges that were overcome during the implementation, shedding light on the
mechanisms that led to increased profitability.
