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.
