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.
翻译:管理贴现促销活动(“ 标记” ) 是经营电子商务业务的一个重要部分, 效率低下会大大妨碍零售商的利润。 解决这一问题的传统方法在很大程度上依赖于价格弹性建模。 然而,价格弹性建模的片面信息性质,加上保护利润的不可谈判责任,意味着机器学习从业者往往必须花很长的时间来界定衡量离线模型质量的战略。 面对这种情况, 许多零售商会重新回到基于规则的方法, 从而大大提升利润, 而这可以通过机器学习获得。 在本文中, 我们引入了两种新型的端到端的减价管理系统, 以便在零售商旅程的不同阶段优化降价。 但是,价格弹性建模模型和验证程序, 第一个系统“ 指数” 推出合理的供货价格定价战略, 而没有需求估计, 并且可以作为一种“ 冷淡的开端” 解决方案来收集降价数据, 同时保持收入控制。 第二个系统“ Promotheus ” 提供了一个完整的降价框架, 通过机器学习来实现价格弹性的降价优化 。 我们详细描述的是, 将真正的市价和验证系统内部的升级的系统, 在实际的系统里, 测试系统里, 进行一个真正的升级的系统里, 实现一个精确的升级的系统, 测试, 的系统, 一个真正的升级的系统, 一个真正的升级的系统, 和验证, 的系统, 一个真正的升级的系统, 的系统, 的系统里程的升级的系统, 的系统, 实现。