Measuring Opportunity Cost with Stock Lifetime Value
用库存生命周期价值衡量机会成本AI 解读 ↓
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用库存生命周期价值衡量机会成本
- 研究问题
- 电商干预的影响可能持续数月，但实验通常只运行一周；如何在短期实验中衡量当前库存的长期利润变化与机会成本？
- 方法与思路
- 作者提出库存生命周期价值（SLV），汇总当前库存直至销售生命周期结束的预期利润，并用于实验评价、定价优化及效应年化。
- 材料陈述的贡献
- 摘要称，该指标在 Zalando 场景中与实际十八个月生命周期结果进行验证，并把实验评价、定价目标和财务报告连接起来。
- 阅读关注点
- 应核对预期利润的预测方法、干预如何影响未来库存收益、十八个月验证的比较标准，以及年化处理是否计入需求跨期转移。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明利润预测假设、验证误差及跨行业适用条件。
原文摘要
Measuring the long-term opportunity cost of interventions remains a critical challenge in e-commerce A/B testing. While strategic levers (such as dynamic pricing, ranking algorithms, and promotional campaigns) trigger shifts in consumer behaviour that persist over months, operational constraints necessitate fast decision-making cycles that are typically limited to weekly experimental windows. Standard metrics like revenue and conversion are inherently short-sighted, biasing decisions toward immediate gains. We introduce Stock Lifetime Value (SLV), a stock-centric metric that captures long-term opportunity cost within short experiments by aggregating expected profit from current inventory through the end of its selling lifecycle. We develop the methodology in the context of fashion e-commerce at Zalando, where stock constraints and seasonal lifecycles make the trade off between short-term and long-term outcomes particularly relevant. SLV aggregates the expected profit from current inventory through the end of its selling lifecycle, providing a way to evaluate interventions against their true profit impact. We discuss three applications: (a) SLV efficiency as a metric for article-level and customer-level A/B tests, validated against realized 18-month lifecycle outcomes; (b) SLV as an optimization target for pricing algorithms, aligning the metric used for measurement with the objective used for decision-making; and (c) a framework for annualizing treatment effects into financial reporting metrics required by business stakeholders. While our empirical setting is fashion retail, the framework applies broadly to any inventory-constrained environment where value decays over time or interventions shift demand across periods.