SBIR Phase II: An Innovative and More Effective Means to Manage the Communication Process Between Colleges and Prospective Students

SBIR 第二阶段：管理大学和未来学生之间沟通过程的创新且更有效的方法

• 批准号: 0956891
• 负责人: Douglas Wofford
• 金额: $ 50万
• 依托单位: 422 Group
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0956891&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Innovative; More

This Small Business Innovation Research (SBIR) Phase II project proposes to commercialize a predictive modeling technology that automatically adapts to changing interaction patterns between providers of higher education (colleges and universities) and consumers (prospective students). Current methods produce only retrospective static models which, due to peculiarities of the higher education recruitment cycle, require at least a one-year lag between data acquisition and application to new prospects. As a result, data mining techniques have gained only limited popularity in college recruiting. The approach proposed here employs a proprietary adaptive modeling engine (AME) to leverage real-time transactional data from a CRM system and dynamically update scoring algorithms to predict outcomes. AME relies on a logical interface and unified dimensional data model to extract analyzable record-sets accurately representing the state of underlying transactional data at any time-slice within the system's effective-dated range. The integration of these key technologies allow relationship patterns to be identified in the recruitment process as they occur and scoring algorithms to dynamically adapt to changing patterns within a single recruitment cycle. It is believed that the changing demographics of college-going students will present a number of significant obstacles to the traditional college business model and could jeopardize the future financial health of many higher education providers in this county. The decade-long trend of yearly increases in demand, as represented by the number of new students entering college, comes to an end in 2009. In stark contrast to the 24% growth the market has experienced over the past decade, future enrollment numbers will remain stagnant overall, and in many localities college enrollment will actually decline. Furthermore, dramatic shifts are coming in the geodemographic, ethnic, and cultural mix of high school graduates that feed the higher education market. As competition for students increases dramatically in the face of rising attendance costs, dwindling endowments, changing demographics, and a decline in college-bound students, each college's ability to survive, much less prosper, will depend directly on its ability to identify, understand, and communicate with students in a more efficient and cost-effective manner. Those that are able to adapt this new landscape through the use of innovative tools like AME will flourish, and those who are unable to adapt will face an uncertain future of declining enrollments and financial instability.

这个小企业创新研究（SBIR）第二阶段项目建议将预测建模技术商业化，该技术可以自动适应高等教育提供者（学院和大学）和消费者（未来的学生）之间不断变化的交互模式。目前的方法只能产生回顾性的静态模型，由于高等教育招聘周期的特殊性，在数据获取和应用于新的前景之间至少需要一年的滞后。因此，数据挖掘技术在大学招聘中只获得了有限的普及。本文提出的方法采用专有的自适应建模引擎（AME）来利用CRM系统中的实时事务数据，并动态更新评分算法来预测结果。AME依赖于逻辑接口和统一维度数据模型来提取可分析的记录集，这些记录集准确地表示系统有效日期范围内任何时间片上底层事务数据的状态。这些关键技术的集成允许在招聘过程中识别关系模式，并允许评分算法在单个招聘周期内动态适应不断变化的模式。据信，大学生人口结构的变化将给传统的大学商业模式带来许多重大障碍，并可能危及这个国家许多高等教育机构未来的财务健康。以大学新生数量为代表的长达十年的需求逐年增长的趋势将在2009年结束。与过去十年24%的市场增长率形成鲜明对比的是，未来的招生人数总体上将停滞不前，许多地区的大学招生人数实际上会下降。此外，支撑高等教育市场的高中毕业生的地理、种族和文化组合正在发生巨大变化。随着出勤成本的上升、捐赠基金的减少、人口结构的变化以及大学生人数的减少，对学生的竞争急剧加剧，每所大学的生存能力（更不用说繁荣了）将直接取决于其以更有效、更经济的方式识别、理解和与学生沟通的能力。那些能够通过使用像AME这样的创新工具来适应这种新形势的学校将蓬勃发展，而那些无法适应的学校将面临入学率下降和金融不稳定的不确定未来。