  Managing discount promotional events ("markdown") is a significant part of
running an e-commerce business, and inefficiencies here can significantly
hamper a retailer's profitability. Traditional approaches for tackling this
problem rely heavily on price elasticity modelling. However, the partial
information nature of price elasticity modelling, together with the
non-negotiable responsibility for protecting profitability, mean that machine
learning practitioners must often go through great lengths to define strategies
for measuring offline model quality. In the face of this, many retailers fall
back on rule-based methods, thus forgoing significant gains in profitability
that can be captured by machine learning. In this paper, we introduce two novel
end-to-end markdown management systems for optimising markdown at different
stages of a retailer's journey. The first system, "Ithax", enacts a rational
supply-side pricing strategy without demand estimation, and can be usefully
deployed as a "cold start" solution to collect markdown data while maintaining
revenue control. The second system, "Promotheus", presents a full framework for
markdown optimization with price elasticity. We describe in detail the specific
modelling and validation procedures that, within our experience, have been
crucial to building a system that performs robustly in the real world. Both
markdown systems achieve superior profitability compared to decisions made by
our experienced operations teams in a controlled online test, with improvements
of 86% (Promotheus) and 79% (Ithax) relative to manual strategies. These
systems have been deployed to manage markdown at ASOS.com, and both systems can
be fruitfully deployed for price optimization across a wide variety of retail
e-commerce settings.
