This project analyzes employee training utilization and learning effectiveness across roles, regions, and delivery formats. It was completed as part of a graduate analytics consulting engagement.
Organizations invest heavily in training, but participation and learning outcomes often vary widely across employee groups. Understanding who learns best, how they learn, and where learning breaks down is critical for improving ROI on training programs.
Terra Nova Learning Academy (TNLA) sought to evaluate how employees engage with learning programs globally and identify opportunities to improve completion and learning gains.
- Measure training utilization across delivery formats, job roles, and regions
- Evaluate learning effectiveness using intake vs outcome proficiency
- Identify learner segments based on motivation and learning behavior
- Provide actionable recommendations to improve training outcomes
- Built KPI frameworks for enrollment, completion rate, and learning gain
- Designed executive-ready dashboards to compare performance across modalities
- Conducted learner segmentation using PCA and K-Means clustering
- Translated analytics into clear business recommendations
- In-person learning shows the highest completion rate (93%) and strongest learning gains (+8.6)
- Asynchronous learning underperforms, particularly for early-career and frontline roles
- Training engagement is concentrated in mid-level operational roles
- Motivation-based learner segmentation reveals where targeted interventions can create the highest impact
- Tableau / Power BI
- Python (PCA, K-Means Clustering)
- SQL
- Excel
- Interactive dashboards summarizing utilization and learning effectiveness
- Executive summary for leadership stakeholders
- Final presentation deck with insights and recommendations
All data used in this project is anonymized and simulated for academic purposes.
This repository contains no proprietary, confidential, or personally identifiable information.
Thirumurugan Selvakumar
MS in Business Analytics
University of Rochester