Improving customer repurchase intention constitutes a key activity for maintaining sustainable business performance. Returning customers provide many economic and other benefits to businesses. In contrast, attracting new customers is a process that is associated with high costs. This work proposes a novel counterfactual explanations methodology that utilizes textual data from electronic word of mouth to recommend business changes that can improve customers' repurchase behavior. Counterfactual explanation methods gained considerable attention because their logic aligns with human reasoning and the fact that they can recommend low-cost actions on how to turn an unfavorable outcome into a favorable. Most counterfactual explanation methods however recommend actions that can change the outcome of individual instances (i.e. one customer) rather than a group of instances. Therefore, this work proposes a multi-instance counterfactual explanation method that recommends optimum changes to an organization's practices/policies that increase repurchase intention for many customers or customer segments.

中文翻译：

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支持有关如何使用多实例反事实解释提高客户重复购买的组织决策


提高客户复购意愿是维持可持续业务绩效的关键活动。回头客为企业带来许多经济和其他利益。相比之下，吸引新客户是一个成本高昂的过程。这项工作提出了一种新颖的反事实解释方法，该方法利用来自电子口碑的文本数据来推荐可以改善客户重复购买行为的业务变更。反事实解释方法获得了相当多的关注，因为它们的逻辑与人类推理一致，而且它们可以推荐低成本的行动来将不利的结果变成有利的结果。然而，大多数反事实解释方法推荐的行动可以改变单个实例（即一个客户）而不是一组实例的结果。因此，这项工作提出了一种多实例反事实解释方法，该方法建议对组织的实践/政策进行最佳改变，以增加许多客户或客户群的重复购买意愿。