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A Multiactivity Latent Attrition Model for Customer Base Analysis

机译:用于客户群分析的多活动潜在损耗模型

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Customer base analysis is a key element in customer valuation and can provide guidance for decisions such as resource allocation. Yet extant models often focus on a single activity, such as purchases from a retailer or donations to a nonprofit organization. These models do not consider other ways that an individual may engage with an organization, such as purchasing in multiple brands or contributing user-generated content. In this research, we propose a framework to generalize extant models for customer base analysis to multiple activities. Using the data from a website that allows users to purchase digital content and/or post digital content at no charge, we develop a flexible "buy 'til you die" model to empirically examine how the two activities are related. Compared with benchmarks, our model more accurately forecasts the future behavior for both types of activities. In addition to finding evidence of coincidence between the activities while customers are "alive," we find that the latent attrition processes are related. This suggests that conducting one type of activity is informative of whether customers are still alive to conduct another type of activity and, consequently, affects inferences of customer value.
机译:客户群分析是客户评估中的关键要素,可以为诸如资源分配等决策提供指导。但是,现有模型通常只关注单个活动,例如从零售商那里购买商品或向非营利组织捐款。这些模型没有考虑个人与组织互动的其他方式,例如购买多个品牌或提供用户生成的内容。在这项研究中,我们提出了一个框架,以将用于客户群分析的现有模型推广到多个活动。利用允许用户免费购买数字内容和/或发布数字内容的网站数据,我们开发了一种灵活的“直到死亡”的购买模型,以实证检验这两项活动之间的关系。与基准相比,我们的模型可以更准确地预测两种活动的未来行为。除了在客户“活跃”时找到活动之间的巧合的证据外,我们还发现潜在的减员过程是相关的。这表明进行一种类型的活动可以告知客户是否仍然活着来进行另一种类型的活动,因此会影响对客户价值的推断。

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