SBIR-STTR Award

This Small Business Innovation Research Phase I project seeks to develop a more effective means to manage the communication process between colleges and prospective students by automating the response logic needed to successfully transition critical decision making steps. Data mining techniques and geo-demographic analysis have recently gained limited popularity in college recruiting as a means to segment prospect populations based on historical data and then to recalibrate manual communication strategies. However, these static methods are retrospective in nature and require several years of consistent historical data for implementation, limiting their appeal. The approach proposed in this research employs an automated system that analyzes the ongoing interaction between colleges and prospects. Through the application of database-embedded and integrated modeling and pattern analysis techniques, key decision points are identified in the communication process as they occur. The recruitment process in higher education is becoming increasingly complex and compressed. Students are waiting longer to reveal their interest to colleges and submitting applications to more colleges. There exists only a brief window, between the point a prospect becomes 'known' to a college and the actual matriculation decision, when the opportunity exists for targeted communications to simultaneously inform and influence each students' decision-making process. As competition for students increases dramatically over the next decade in the face of rising attendance costs, changing demographics, and a decline in the number of college-bound students, each institutions' ability to survive, much less prosper, will depend directly on its ability to identify, qualify, and communicate with prospective students in an more efficient and cost-effective manner. If successful, the effort proposed will provide a means for measurable value for those institutions that embrace this approach

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