Understanding Service Retention within and across Cohorts using Limited Information
Understanding Service Retention within and across Cohorts using Limited Information
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DOI：
10.1509/jmkg.72.1.082
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发表时间：
2008-01
影响因子：
12.9
通讯作者：
David A. Schweidel;P. Fader;Eric T. Bradlow
中科院分区：
文献类型：
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作者：
David A. Schweidel;P. Fader;Eric T. Bradlow
服务流失率和保留率仍然是营销活动中的核心概念，例如服务用户的估值和资源分配。虽然现存的方法已经提出了与外部因素，如满意度报告，营销组合活动，等等，服务流失，管理人员经常面临的情况下，唯一可用的信息是用户有服务的持续时间。在这种情况下，他们能否预测服务流失并了解促成因素，以便随后进行干预？作者提出了一个框架，以检查可能的基础服务保留在合同设置的因素。具体来说，他们使用了一个保留模型，该模型考虑了（1）持续时间依赖性，（2）促销效应，（3）订户异质性，（4）跨队列效应，以及（5）时间-时间效应（例如，季节性）。然后，他们将该框架应用于电信提供商提供的七种服务的订阅数据库，反映了预测未来服务流失（和做出管理决策）的常用格式。在所有七个服务，包括促销效果总是提高预测的准确性保留行为，而包括跨队列的影响并没有显着提高it. In五个服务，客户异质性，时间效应，持续时间的依赖性也有助于提高预测。作者使用这些结果来了解订阅的预期价值在不同模型规范中有何不同。他们发现模型规格之间存在相当大的差异，这表明模型规格错误会影响资源分配决策和其他对公司很重要的营销工作。
Service churn and retention rates remain central as constructs in marketing activities, such as valuation of service subscribers and resource allocation. Although extant approaches have been proposed to relate service churn to external factors, such as reported satisfaction, marketing-mix activities, and so on, managers often face situations in which the only information available is the duration for which subscribers have had service. In such cases, can they forecast service churn and understand the contributing factors, which may allow for subsequent intervention? The authors propose a framework to examine factors that may underlie service retention in a contractual setting. Specifically, they use a model of retention that accounts for (1) duration dependence, (2) promotional effects, (3) subscriber heterogeneity, (4) cross-cohort effects, and (5) calendar-time effects (e.g., seasonality). Then, they apply the framework to subscription databases of seven services offered by a telecommunications provider, mirroring the format commonly used to forecast future service churn (and to make managerial decisions). Across all seven services, the inclusion of promotional effects always improves the forecast accuracy of retention behavior, whereas including cross-cohort effects does not significantly improve it. In five of the services, customer heterogeneity, calendar-time effects, and duration dependence also contribute to improved forecasts. The authors use these results to understand how the expected value of a subscription differs across model specifications. They find considerable variation across model specifications, indicating that model misspecification can affect resource allocation decisions and other marketing efforts that are important to a firm.