Optimizing Client Satisfaction and IT Project Delivery Efficiency

Abstract

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 five-fold 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.

Introduction

Efficient IT service project delivery and client satisfaction remain challenging despite Agile methods, largely due to hidden operational issues like defect backlogs and poor communication. Traditional monitoring often misses these early warning signs. This work addresses these challenges by creating a large synthetic dataset to develop machine learning models that predict project delivery delays and client satisfaction. Using Random Forest regression on 40,000 synthetic project records and 1,000 client entries, the models achieved high accuracy (R² of 0.92 for delays and 0.86 for satisfaction).

Key findings highlight that defect resolution efficiency and communication quality are more critical to project success than budget size. The models are integrated into a Power BI dashboard providing real-time visualizations and actionable insights to project managers. This data-driven framework enables proactive risk detection and informed decision-making. Future improvements include using live data, advanced algorithms, and automated alerts to enhance project management effectiveness.

Conclusion

This study demonstrates a scalable analytics framework for forecasting IT project delivery delays and client satisfaction using synthetic data and Random Forest models. Defect resolution efficiency and communication quality emerge as key levers for improvement. The modular pipeline—spanning data synthesis, preprocessing, modeling, and dashboard visualization—enables rapid deployment and adaptation to live data environments. Future research will integrate real world project data, explore advanced algorithms (e.g., gradient boosting), and implement automated alerting mechanisms to further enhance proactive risk management.

References

[1] Adekugbe, P., & Ibeh, V. (2024). Utilizing Comprehensive Data Dashboards to Improve Service Delivery: Insights from U.S. Case Studies. International Journal of Frontiers in Engineering and Technology Research, 6(2), 8–18. [2] Aguiar Costa, L. M. et al. (2022). Customer Satisfaction in Service Delivery with Artificial Intelligence: A Meta Analytic Study. RAM. Revista De Administração Mackenzie, 23(6). [3] Diegmann, P., Basten, D., & Pankratz, O. (2015). Influence of Communication on Client Satisfaction in Information System Projects. International Research Workshop on IT Project Management. [4] Haq, N. U. et al. (2018). Determinants of Client Satisfaction in Web Development Projects from Freelance Marketplaces. International Journal of Managing Projects in Business, 11(3), 583–607. [5] Kennedy, S. I. et al. (2024). Agile Practices and IT Development Team Well Being: Unveiling the Path to Successful Project Delivery. Engineering Management Journal. [6] Ngo, V. M. (2015). Measuring Customer Satisfaction: A Literature Review. ResearchGate.

Copyright

Copyright © 2025 Ms. K. Nithiya Banu, Dr. T. Amutha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.