Retailers often provide lenient, consumer‐friendly return policies to reduce customers' perceived shopping risk and increase demand. As an unfortunate side effect for retailers, empirical findings demonstrate that lenient return policies lead some customers to abuse those return policies through opportunistic and even fraudulent behaviors. Customers can abuse return policies by making purchases with the full intention of returning the products or by returning a product long after extracting most of the product's market value. In doing so, abusive customers extract utility—physical, experiential, or financial—from these purchases, at little or no cost to themselves. However, retailers incur significant costs from such return abuse, with estimates topping $5.6 billion annually in the United States alone. Identifying customers who perpetrate return abuse remains a critical topic. Yet, as a construct, return abuse is difficult to define and to quantify. In contrast, legitimate returner and nonreturner customers exhibit different return behaviors with distinctly different transactional behaviors and profitability outcomes. To investigate these diverse returner behaviors, this study empirically analyzes a transactional secondary data set of over 1 million customers with over 75 million transactions from a national U.S.‐based retailer. The analysis generates empirical insights that characterize observable customer actions related to abusive returners, legitimate returners, and nonreturners. This study introduces a set of predictive models that enable actionable managerial intervention and presents the opportunity to recapture significant returns costs that might otherwise be lost to avoidable return abuse. The analysis also highlights the need for a more holistic perspective toward predicting, managing, and preventing returns.

中文翻译：

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通过数据分析评估客户退货行为

零售商通常会提供宽大，对消费者友好的退货政策，以降低客户的感知购物风险并增加需求。作为零售商的不幸副作用，经验发现表明宽大的退货政策会导致一些顾客通过机会主义甚至欺诈行为滥用这些退货政策。客户可以通过完全出于退货的目的进行购买或在提取大部分产品的市场价值后很长时间才退货来滥用退货政策。在这种情况下，辱骂性的客户从这些购买中提取了实用的，有形的，经验性的或财务性的东西，而他们自己付出的成本很少或没有。但是，零售商因此类退货滥用而蒙受了巨额成本，仅在美国，估计每年就高达56亿美元。确定造成退货滥用的客户仍然是一个关键主题。然而，作为一种构造，回返滥用很难定义和量化。相反，合法的退货者和非退货者客户表现出不同的退货行为，而交易行为和获利能力结果则明显不同。为了调查这些多样化的退货者行为，本研究以经验的方式分析了一个交易辅助数据集，该数据集包含超过100万客户和来自美国一家全国性零售商的7500万笔交易。该分析生成经验性见解，这些见解表征了与可滥用退货者，合法退货者和非退货者相关的可观察到的客户行为。这项研究引入了一组预测模型，这些模型使可行的管理干预成为可能，并提供了机会来获取可观的回报成本，而这些成本原本可能因避免滥用回报而损失了。分析还强调了需要对预测，管理和防止回报有更全面的认识。