SavedAdd your journal

Journals / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2026 / Cilt: 22 Sayı: 2

Improving the Quality of Enterprise Data Management with Tree-Based Models

Erdal Büyükbıçakcı(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, BİLİŞİM TEKNOLOJİLERİ MYO, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ)0000-0001-7276-741X
Pages
236–249
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With tree-based models, substantial performance gains were observed, with precision up to 90.8%, recall up to 74%, and overall accuracy up to 91.6%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Data credibility is essential for reliable decision-making and decision-support systems across Salesforce environments during the processing of transactional data as observed in the Online Retail Dataset.
📊
Key Findings & Data:With tree-based models, substantial performance gains were observed, with precision up to 90.8%, recall up to 74%, and overall accuracy up to 91.6%.
💡
Conclusion & Impact:The system’s retraining mechanism and feedback loop ensure protection against long-term data degradation in enterprise CRM environments.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · Customer relationship management · Data quality improvement

Abstract

Data credibility is essential for reliable decision-making and decision-support systems across Salesforce environments during the processing of transactional data as observed in the Online Retail Dataset. This work analyses the application of tree-based machine-learning models in improving data quality through transformation and cleaning processes for the removal of missing values, duplication, and outliers. The approach includes data preparation, relevant feature selection, model construction, and deployment within Salesforce processes to monitor data quality in real time and batch workflows. With tree-based models, substantial performance gains were observed, with precision up to 90.8%, recall up to 74%, and overall accuracy up to 91.6%. After Salesforce integration, completeness increased by 12%, accuracy by 10%, and consistency by 15%. The system’s retraining mechanism and feedback loop ensure protection against long-term data degradation in enterprise CRM environments.

🔖

Cite This Article

Büyükbıçakcı, E. (2026). Improving the Quality of Enterprise Data Management with Tree-Based Models. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 22 Sayı: 2, 236–249. https://doi.org/10.18466/cbayarfbe.1677875

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (37)

  1. . Yocupicio-Zazueta, A., Brau-Avila, A., Cirett-Galán, F., & Valenzuela-Galván, M. (2024). Design and Deployment of ML in CRM to Identify Leads. Applied Artificial Intelligence, 38(1): 2376978. (https://doi.org/10.1080/08839514.2024.2376978)
  2. . Pookandy, J. (2022). AI-based data cleaning and management in Salesforce CRM for improving data integrity and accuracy to enhance customer insights. International Journal of Advanced Research in Engineering and Technology (IJARET), 13(5), 108-116.
  3. . Elouataoui, W., El Mendili, S., & Gahi, Y. (2023). An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis. Data, 8(12): 182. (https://doi.org/10.3390/data8120182)
  4. . Xie, J., Sun, L., & Zhao, Y. F. (2025). On the data quality and imbalance in machine learning-based design and manufacturing—A systematic review. Engineering, 45, 105-131. (https://doi.org/10.1016/j.eng.2024.04.024)
  5. . Azimi, S., Pahl, C. 2024. Anomaly analytics in data-driven machine learning applications. Azimi, S., & Pahl, C. (2024). International Journal of Data Science and Analytics, 19, 155–180. (https://doi.org/10.1007/s41060-024-00593-y)
  6. . Panarese, A., Settanni, G., Vitti, V., & Galiano, A. (2022). Developing and preliminary testing of a machine learning-based platform for sales forecasting using a gradient boosting approach. Applied Sciences, 12(21): 11054. (https://doi.org/10.3390/app122111054)
  7. . Massaro, A., Panarese, A., Giannone, D., & Galiano, A. (2021). Augmented data and XGBoost improvement for sales forecasting in the large-scale retail sector. Applied Sciences; 11(17): 7793. (https://doi.org/10.3390/app11177793)
  8. . Chinta, U., Aggarwal, A., & Goel, P. (2024). Quality Assurance in Salesforce Implementations: Developing and Enforcing Frameworks for Success. International Journal of Computer Science and Engineering, 13(1): 27-44.
  9. . Zhao, Y., Nasrullah, Z., & Li, Z. (2019). PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of Machine Learning Research, 20(96): 1–7.
  10. . Online Retail Data Set (UCI Machine Learning Repository). https://archive.ics.uci.edu/dataset/352/online%2Bretail (accessed at 02.01.2025). (https://doi.org/10.24432/C5BW33)
  11. . Rangineni, S., Bhanushali, A., Suryadevara, M., Venkata, S., & Peddireddy, K. (2023). A Review on enhancing data quality for optimal data analytics performance. International Journal of Computer Sciences and Engineering, 11(10), 51-58. (https://doi.org/10.26438/ijcse/v11i10.5158)
  12. . Glackin, C. E., & Adivar, M. (2023). Using the power of machine learning in sales research: process and potential. Journal of Personal Selling & Sales Management, 43(3), 178-194. (https://doi.org/10.1080/08853134.2022.2128812)
  13. . Chakraborty, I., Chiong, K., Dover, H., & Sudhir, K. (2025). Can AI and AI-hybrids detect persuasion skills? Salesforce hiring with conversational video interviews. Marketing Science, 44(1), 30-53. (https://doi.org/10.1287/mksc.2023.0149)
  14. . Suh, Y. (2023). Exploring the impact of data quality on business performance in CRM systems for home appliance business. IEEE Access, 11, 116076-116089. (https://doi.org/10.1109/ACCESS.2023.3325892)
  15. . Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., Luqman, A., & Zahid, R. (2021). Impact of big data analytics on sales performance in pharmaceutical organizations: The role of customer relationship management capabilities. PLOS ONE, 16(4), e0250229. (https://doi.org/10.1371/journal.pone.0250229)
  16. . Kedi, W. E., Ejimuda, C., Idemudia, C., & Ijomah, T. I. (2024). Machine learning software for optimizing SME social media marketing campaigns. Computer Science & IT Research Journal, 5(7), 1634-1647. (https://doi.org/10.51594/csitrj.v5i7.1349)
  17. . Mollá, N., Heavin, C., & Rabasa, A. (2022). Data-driven decision making: New opportunities for DSS in data stream contexts. Journal of Decision Systems, 31(sup1), 255-269. (https://doi.org/10.1080/12460125.2022.2071404)
  18. . Lim, S., Henriksson, A., & Zdravkovic, J. (2021). Data-driven requirements elicitation: A systematic literature review. SN Computer Science, 2(1), 16. (https://doi.org/10.1007/s42979-020-00416-4)
  19. . Liso, A., Cardellicchio, A., Patruno, C., Nitti, M., Ardino, P., Stella, E., & Renò, V. (2024). A review of deep learning-based anomaly detection strategies in industry 4.0 focused on application fields, sensing equipment, and algorithms. IEEE Access, 12, 93911-93923. (https://doi.org/10.1109/ACCESS.2024.3424488)
  20. . Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937-1967. (https://doi.org/10.1007/s10462-020-09896-5)
  21. . Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. IEEE Access, 9, 78658-78700. (https://doi.org/10.1109/ACCESS.2021.3083060)
  22. . Siddique, K., Akhtar, Z., Lee, H. G., Kim, W., & Kim, Y. (2017). Toward bulk synchronous parallel-based machine learning techniques for anomaly detection in high-speed big data networks. Symmetry, 9(9), 197. (https://doi.org/10.3390/sym9090197)
  23. . Zhao, X., Liu, Y., & Zhao, Q. (2024). Improved LightGBM for extremely imbalanced data and application to credit card fraud detection. IEEE Access, 12, 159316-159335. (https://doi.org/10.1109/ACCESS.2024.3487212)
  24. . Verdonck, T., Baesens, B., Óskarsdóttir, M., & vanden Broucke, S. (2024). Special issue on feature engineering editorial. Machine Learning, 113(7), 3917-3928. (https://doi.org/10.1007/s10994-021-06042-2)
  25. . Wang, J. F. (2023). The impact of artificial intelligence (AI) on customer relationship management: A qualitative study. International Journal of Management and Accounting, 5(5), 74-88. (https://doi.org/10.34104/ijma.023.0074090)
  26. . Ledro, C., Nosella, A., & Vinelli, A. (2022). Artificial intelligence in customer relationship management: literature review and future research directions. Journal of Business & Industrial Marketing, 37(13), 48-63. (https://doi.org/10.1108/JBIM-07-2021-0332)
  27. . Larriva-Novo, X., Vega-Barbas, M., Villagra, V. A., Rivera, D., Alvarez-Campana, M., & Berrocal, J. (2020). Efficient distributed preprocessing model for machine learning-based anomaly detection over large-scale cybersecurity datasets. Applied Sciences, 10(10), 3430. (https://doi.org/10.3390/app10103430)
  28. . Chen, H., Chen, J., & Ding, J. (2021). Data evaluation and enhancement for quality improvement of machine learning. IEEE Transactions on Reliability, 70(2), 831-847. (https://doi.org/10.1109/TR.2021.3070863)
  29. . Ledro, C., Nosella, A., & Dalla Pozza, I. (2023). Integration of AI in CRM: Challenges and guidelines. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100151. (https://doi.org/10.1016/j.joitmc.2023.100151)
  30. . Kirisci, M. (2022). Correlation coefficients of Fermatean fuzzy sets with a medical application. Journal of Mathematical Sciences and Modelling, 5(1), 16-23. (https://doi.org/10.33187/jmsm.1039613)
  31. . Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications, 221, 119738. (https://doi.org/10.1016/j.eswa.2023.119738)
  32. . Hossain, Q., Hossain, A., Nizum, M. Z., & Naser, S. B. (2024). Influence of artificial intelligence on customer relationship management (CRM). International Journal of Communication Networks and Information Security, 16(3), 653-663.
  33. . Alnofeli, K., Akter, S., & Yanamandram, V. (2023). Understanding the Future trends and innovations of AI-based CRM systems. In Handbook of big data research methods (pp. 279-294). Edward Elgar Publishing. (https://doi.org/10.4337/9781800888555.00021)
  34. . Carneiro, D., Guimaraes, M., Carvalho, M., & Novais, P. (2023). Using meta‐learning to predict performance metrics in machine learning problems. Expert Systems, 40(1), e12900. (https://doi.org/10.1111/exsy.12900)
  35. . Sabbeh, S. F. (2018). Machine-learning techniques for customer retention: A comparative study. International Journal of advanced computer Science and applications, 9(2). (https://doi.org/10.14569/IJACSA.2018.090238)
  36. . Vasudevan, M., Narayanan, R. S., Nakeeb, S. F., & Abhishek, A. (2022). Customer churn analysis using XGBoosted decision trees. Indonesian Journal of Electrical Engineering and Computer Science, 25(1), 488-495. (https://doi.org/10.11591/ijeecs.v25.i1.pp488-495)
  37. . AL-Shatnwai, A. M., & Faris, M. (2020). Predicting customer retention using XGBoost and balancing methods. International Journal of Advanced Computer Science and Applications, 11(7). (https://doi.org/10.14569/IJACSA.2020.0110785)