Collaborative Research: Optimizing Direct-Marketing Strategies in Non-Profit Fundraising: An Integrated Framework for Segmentation, Estimation and Control

合作研究：优化非营利筹款中的直接营销策略：细分、估算和控制的综合框架

• 批准号: 1335104
• 负责人: Pablo Durango-Cohen
• 金额: $ 19.5万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1335104&HistoricalAwards=false
• 关键词: Collaborative; Research; Optimizing; Direct; Marketing

The objective of this collaborative award is to provide insights into links between direct-marketing strategies and their impact on the performance (revenue generation, member retention) of nonprofit organizations, through the linkage of a general class of sophisticated optimization tools (stochastic optimal control) with state-of-the-art estimation methods (finite mixture models). This integration allows the proposed framework to serve as a practical tool to segment alumni/donor populations into homogeneous groups, based on their contribution sequences, which can be targeted with specific marketing actions. The framework's capability to process contribution sequences, i.e., longitudinal data, provides fundamental new insights into how direct-marketing strategies affect donor contribution behavior, and informs data acquisition strategies (via surveys and field experiments) aimed at model calibration and validation.If successful, the results of this research will provide a rigorous mechanism to infer and segment the population based on unobserved heterogeneities, e.g., personality traits that influence the propensity of an individual to donate in response to solicitations. While there is ample empirical evidence that such differences can be statistically significant, existing models to support direct marketing activities are only capable of explaining these differences as random variations within the population. In contrast, the methodology provides a basis to tailor direct-marketing policies to optimize specific performance criteria. Moreover, the research will develop methodological tools that can guide decision-making for a broad set of segmentation, estimation and control problems in other areas. For example, in the management of transportation systems, the problem is to segment facilities that share similar characteristics, and to find optimal resource allocation policies for each of the groups.

该合作奖的目的是通过将一般类型的复杂优化工具（随机最优控制）与最先进的估计方法（有限混合模型）联系起来，深入了解直接营销策略及其对非营利组织绩效（创收，会员保留）的影响之间的联系。这一整合使拟议的框架成为一个实用工具，可根据校友/捐助者的捐款顺序将其划分为同类群体，并可针对这些群体采取具体的营销行动。框架处理贡献序列的能力，即，纵向数据，为直接营销策略如何影响捐赠行为提供了基本的新见解，并为旨在模型校准和验证的数据获取策略（通过调查和实地实验）提供了信息。如果成功，这项研究的结果将提供一个严格的机制，根据未观察到的异质性推断和划分人口，例如，影响个人响应募捐的倾向的人格特征。虽然有充分的经验证据表明，这种差异在统计上可能是显著的，但支持直接营销活动的现有模型只能将这些差异解释为人口中的随机变化。与此相反，该方法提供了一个基础，定制直销政策，以优化具体的业绩标准。此外，该研究将开发方法工具，可以指导决策的一系列广泛的细分，估计和控制问题在其他领域。例如，在运输系统的管理中，问题是将具有相似特征的设施分段，并为每个组找到最佳资源分配策略。