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.
The changing demographics of college-bound students present a number of significant obstacles to the tuition-driven college business model and could jeopardize the future financial health of many higher education providers in this country. Colleges that are able to adapt their recruitment methods to this changing landscape will prosper, and those that are unable to adapt will face an uncertain future characterized by declining enrollments and financial instability. Services, tools and techniques that provide a competitive edge in this new environment will be highly sought-after and command a premium in price. Through this SBIR project, 422 Group has developed a scalable, automated Adaptive Modeling Engine (AME) designed to generate an easy-to-use Adaptive Integrated Modeling (AIM) Score that will improve the matching process between colleges and prospective students, reducing the recruitment cost to colleges, broadening the reach of the process to underserved populations, creating greater satisfaction in the match for both parties, and improving the overall efficacy of the college recruiting and enrollment process. Because the AIM Score will be embedded within an affordably-priced, highly-customized, hosted CRM application, it will be accessible to a large number of colleges, allowing them to identify key "decision points" in the student recruitment process as they occur and individually target desirable "at-risk" prospects for CRM communication interventions designed to enhance affinity, encourage continued participation in the recruitment process, and greatly enhance the likelihood of matriculation. The significant value and affordable packaging of the proposed approach will expand the potential pool of higher education buyers to include the "middle-tier" in higher education—comprised of more than 70% of all institutions (U.S Department of Education, 2011). Once it has been demonstrated in the higher education market, the work will have potential applications in other markets with similar choice and matching complexities.
