Case Study: Analyzing MBA Decisions—Data-Driven Insights for Marketing Business Education MBA Capstone Research Project | Wright State University | April 2025

Context

In today’s competitive academic landscape, business schools face challenges in attracting qualified MBA candidates. Understanding the motivations and barriers behind the decision to pursue an MBA is essential for designing more effective marketing and recruitment strategies.

Objective

To identify the key demographic, academic, and financial factors influencing prospective students’ decisions to pursue an MBA, and to translate these findings into actionable marketing recommendations for business schools.

Approach

• Data Source: Kaggle dataset of 10,000 individuals with bachelor’s degrees considering an MBA, capturing demographics, academics, career goals, financial variables, and motivations.

• Data Preparation: Cleaned and preprocessed data, managed missing values, encoded categorical features, and standardized numerical variables.

• Analysis Methods:

  • Descriptive statistics to summarize participant profiles

  • Correlation analysis to identify relationships between key factors and MBA decision

  • Predictive modeling (Logistic Regression, Random Forest, XGBoost) to forecast decision drivers (XGBoost accuracy up to 85%)

  • Cluster analysis (K-means) to segment applicant types (career-focused, cost-conscious, experience-driven)

• Visualization & Tools: Used Pandas, Scikit-learn, XGBoost, Matplotlib, and Seaborn for analytics and visualization.

Key Findings

• Motivators: Career advancement (62%), higher salary (48%), and entrepreneurship (22%) are the top reasons to pursue an MBA.

• Barriers: Cost (58%), time commitment (35%), and uncertainty of ROI (27%) are the main obstacles.

• Strong Predictors: Years of work experience, expected post-MBA salary, and funding source (loan/self-funded) most strongly predict intent.

• Market Segments:

  • Career-Oriented: Motivated by career advancement and salary

  • Financially Driven: Seek scholarships/financial support

  • Experience Seekers: Value networking and personal growth

• Program Preferences: 45% prefer online/hybrid MBA formats; flexibility is essential for working professionals.

Marketing Recommendations

• Highlight alumni success stories and salary ROI in campaigns.

• Communicate funding options and scholarships transparently.

• Promote flexibility with testimonials from online/hybrid program graduates.

• Use predictive modeling to personalize and segment outreach.

Impact

This case study demonstrates how analytics and machine learning can guide business schools in tailoring recruitment strategies, improving marketing ROI, and driving targeted enrollment growth.

Tools Used

Pandas, Scikit-learn, XGBoost, Matplotlib, Seaborn, Excel

Summary Statement:This case study reflects my ability to apply advanced analytics, machine learning, and marketing strategy to solve complex, real-world business challenges. The process and insights are directly applicable to data-driven decision-making in marketing and beyond.