🤖 AI Summary
This study addresses the limitation of conventional e-commerce A/B tests, which often overlook the long-term impact of interventions on profitability across an inventory item’s full lifecycle due to short experimental windows. To overcome this, the authors propose Stock Lifetime Value (SLV), a novel metric that aggregates the expected profit of current inventory over its entire sales horizon within short-term experiments, thereby enabling more accurate assessment of long-term profitability. SLV uniquely integrates inventory constraints and seasonal lifecycle dynamics into the A/B testing framework, combining causal inference with financial mapping to support both item-level and user-level experimentation while aligning with annual financial reporting. Empirical validation at Zalando demonstrates that SLV effectively predicts actual profits over an 18-month horizon, enhances pricing algorithm performance, and delivers interpretable estimates of annual financial impact.
📝 Abstract
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.