Optimizing Client Satisfaction and IT Project Delivery Efficiency

IT service organizations frequently experience schedule overruns and client dissatisfaction, undermining operational effectiveness and market competitiveness. To address these challenges, we propose a predictive analytics pipeline that combines synthetic data generation, machine learning, and interactive visualization. Employing Python’s Faker library, we synthesized 40 000 project records and 1 000 client profiles, capturing metrics such as defect counts, resolution times, team composition, communication ratings, and usability feedback. Two Random Forest regression models were developed: one to forecast delivery delays in days and another to predict client satisfaction scores on a 1–10 scale. Hyperparameter tuning via grid search and fivefold cross-validation yielded robust performance (delivery delay: MSE = 70.12, R² = 0.92; satisfaction: MSE = 0.74, R² = 0.86). Outputs are visualized in an auto-refreshing Power BI dashboard, presenting key indicators—average delay, defect resolution efficiency, satisfaction trends, and churn risk segmentation. Feature importance analysis identifies defect resolution ratio and communication quality as primary drivers of outcomes, superseding budgetary factors. This modular framework is readily adaptable to real-world data and supports proactive decision making in IT project management.