The objective was to centralize and standardize feedback data from 290 distributed Google Forms and assess course performance across three key metrics: structure, relevance, and visual design. The project aimed to identify key satisfaction drivers, detect performance gaps, and evaluate course effectiveness at both individual and portfolio levels using structured datasets.

The analysis showed that structure and relevance have the highest correlation with overall satisfaction, indicating they are primary performance drivers, while visual design has lower marginal impact. Portfolio level insights revealed a mismatch between high quality niche courses and lower performing high volume courses. These findings led to strategic recommendations, including scaling high performing content, optimizing underperforming large scale courses, and reallocating resources toward instructional design improvements to maximize overall impact.

Google Apps Script was used to build an automated ETL pipeline that extracts, transforms, and loads data into a centralized database. Data preprocessing and validation were applied to ensure consistency. Analytical methods included correlation analysis to measure driver impact, weighted scoring models to compute overall performance, and segmentation techniques such as impact matrix mapping and tier-based clustering. Data visualization dashboards were developed to support comparative analysis, trend identification, and performance diagnostics across courses.