Statement of Problem
In the dynamic realm of higher education, many colleges grapple with a common hurdle—the soaring dropout rates typically observed within the initial two semesters of student enrollment. This not only translates to a direct loss of revenue but also triggers additional costs related to marketing endeavors and sales efforts, often yielding meager returns.
One such college, choosing to remain anonymous, boasted an excellent sales team with an impressive throughput of almost 20%. However, the institution was grappling with significant enrollment losses for diverse reasons. Determined to pinpoint the root causes and intervene strategically, the college aimed to minimize its enrollment dropouts.
This anonymous college recognized that while its sales team demonstrated commendable performance, a substantial portion of enrollments was slipping away. The institution sought to delve into the heart of the matter, identifying key issues and implementing targeted interventions to curtail enrollment dropouts effectively. The focus was on leveraging available data during enrollment, encompassing essential demographic details, academic performance metrics, and financial aid information. The overarching goal was to deploy predictive analytics, facilitating timely and personalized support to mitigate academic and financial challenges, and optimizing resource allocation for sustainable student retention.
Exploration and Decision Trees
The initial phase involved an exploration of predictive models, and decision trees emerged as a valuable tool. These trees effectively identified key metrics and features contributing to the risk of student dropout. This preliminary analysis was pivotal and laid the groundwork for the subsequent stages of the project. Planning, Budgeting, and Resource Allocation
With key decision-makers, including the CEO, on board, the project moved into a comprehensive planning phase. Decision trees’ success in identifying critical risk factors facilitated a smooth transition. A detailed budget was crafted, aiming to utilize 80% of the initial budget while accommodating potential cost overruns. Resource allocation became a strategic task, with the data scientist diligently identifying the necessary resources to ensure project completion within the allocated budget.
Project Initiation and Model Development
The final model was trained on enrollment data, encompassing demographic information, credit history, and a host of relevant features. Manual data cleaning and exploratory data analysis were initially employed and later automated through an ETL pipeline. Random Forest was chosen to enhance accuracy over decision trees, providing a more robust model with low variance. The pipeline was trained on open-source algorithms, effectively managing costs. Notably, the landscape has evolved, and current processes, such as ETL and exploratory data analysis, can be automated, significantly reducing costs. The full pipeline is shown below.
Model Efficacy and Cost Analysis
The developed predictive model showcased an impressive 82% accuracy, successfully reducing the dropout rate to 8% within the identified segments during the trial run. With an average total enrollment of 823 students for a full year, the initial 18% dropout rate saw a substantial reduction to 11%, indicating a remarkable 39% improvement in student retention.
Considering an acquisition cost of $5,000 per student and a calculated lifetime value of $40,000 for each student ( derived from the total tuition of $80,000 for the 2-year accelerated program), the total savings amounted to $263,360. This surpasses the initial deployment cost of around $300,000, showcasing a remarkable 8.81x savings rate.
This case study underscores the transformative impact of predictive analytics in enhancing student retention, achieving substantial cost savings, and demonstrating the continuous evolution of data science methodologies.
For more details on our approach and methodologies, please contact our team.
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