Abstract: Predictive Sales Analytics for Enhancing Customer Relations Using Regression Analysis focuses on utilizing data-driven techniques to inform and improve business decision-making. By applying regression analysis, the system can identify significant relationships between customer behaviors and sales performance. This allows organizations to forecast future sales outcomes and adjust strategies based on reliable predictions. The integration of predictive analytics into customer relationship management (CRM) strengthens customer retention, loyalty, and satisfaction. It also helps managers allocate resources more effectively while personalizing services to meet customer needs. The use of predictive models transforms raw customer and sales data into actionable insights. Overall, this approach combines statistical accuracy with strategic value, enabling businesses to build stronger and more sustainable customer relationships.  The goal of this project is to design and evaluate a predictive sales analytics system that leverages regression analysis to enhance customer relations. Specifically, the project aims to forecast sales outcomes, identify customer trends, and provide actionable insights for decision-makers. By integrating predictive analytics into CRM practices, the project seeks to improve customer retention, satisfaction, and loyalty. Furthermore, the system is expected to help businesses allocate resources more effectively and achieve sustainable growth. A total of 150 respondents participated in the evaluation of the system, consisting of 10 representatives from business organizations and 140 respondents from the academic community. The diverse respondent composition allowed the system to be assessed from both practical and academic perspectives. The development process was guided by the Agile methodology, ensuring iterative improvements and adaptability to user requirements. The evaluation of CRMBoost was conducted using ISO quality standards, specifically focusing on functionality, interaction capability, safety, security, and flexibility. Results showed that the system was functional in processing large amounts of customer data and flexible enough to accommodate different organizational needs. Respondents confirmed that interaction capability was intuitive and user-friendly, while safety and security measures were sufficient to protect customer information. The system outputs, which included sales forecasts, customer trend reports, and predictive insights, were validated as reliable tools for both decision-making and academic analysis. Findings revealed that regression analysis was effective in establishing relationships between independent variables such as demographics, purchasing history, and seasonal demand, and dependent variables such as sales outcomes. Business respondents highlighted the system’s practical usefulness in improving marketing strategies and customer engagement, while the academic community emphasized its contribution to advancing knowledge in predictive analytics. Overall, the system demonstrated its potential as both a technical solution and a strategic resource for organizations. In conclusion, CRMBoost bridges the gap between theory and practice by combining predictive analytics with CRM strategies. It contributes significantly to enhancing customer relationships while promoting sustainable business growth. The study recommends its adoption by organizations and further development by researchers, with continuous alignment to ISO standards to ensure long-term effectiveness and reliability.

CRM, Predictive Analytics, Regression Analysis, Customer Relations, Sales Forecasting, ISO Standards, Agile Methodology

1. Abdelrahim Kasem Ahmad, Assef Jafar & Kadan Aljoumaa,(2019), Customer churn prediction in telecom using machine learning in big data platform, Journal of Big Data (2019). [Google Scholar] [Crossref]

2. Ahaggach, H., et al. (2024). Systematic Mapping Study of Sales Forecasting. MDPI—offers a review of forecasting techniques (including regression) and their evolution. [Google Scholar] [Crossref]

3. Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., & Thomas, D. (2001). Manifesto for Agile Software Development. Agile Alliance. http://agilemanifesto.org/ [Google Scholar] [Crossref]

4. Byrapu, Surendranadha Reddy Sr. Analyst (2021) Predictive Analytics in Customer Relationship Management: Utilizing Big Data and AI to Drive Personalized Marketing Strategies Australian Journal of Machine Learning Research & Applications [Google Scholar] [Crossref]

5. E.W.T. Ngai,Li Xiu, D.C.K. Chau (2029), Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications [Google Scholar] [Crossref]

6. Khan, Akter, & Islam (2024), Big Data Analytics And Predictive Analysis In Enhancing Customer Relationship Management (CRM), Journal of Machine Learning, Data Engineering and Data Science [Google Scholar] [Crossref]

7. Mirzaei & Iyer (2014), Application Of Predictive Analytics In Customer Relationship Management: A Literature Review And Classification, 2014 Proceedings. 23. [Google Scholar] [Crossref]

8. Mohaimin, Das, Akter, Anonna, Hasanuzzaman, Chowdhury & Alam (2025) Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market, Journal of Computer Science and Technology Studies [Google Scholar] [Crossref]

9. Petra Posedel Šimović, Davor Horvatic, Edward W. Sun,(2021) Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression, arvix publication [Google Scholar] [Crossref]

10. Rathi, M., Vinu V. Das, and others (2010), Regression Modeling Technique on Data Mining for Prediction of CRM, International Conference on Advances in Information and Communication Technologies [Google Scholar] [Crossref]

11. Ravi Teja Potla, Vamsi Krishna Pottla(2024), Artificial Intelligence and Machine Learning in CRM: Leveraging Data for Predictive Analytics, Journal artificial Intelligence research [Google Scholar] [Crossref]

12. Satu Elisa Schaeffer, Sara Veronica Rodriguez Sanchez(2020), Forecasting client retention — A machine-learning approach, Journal of Retailing and Consumer Services [Google Scholar] [Crossref]

13. Shagufta Henna, Shyam Krishnan Kalliadan, Enterprise Analytics using Graph Database and Graph-based Deep Learning” (Henna et al., 2021), arvix publication [Google Scholar] [Crossref]

14. Teixeira, M. (2024). A Comparative Study Using ARIMA and Deep Neural Networks for Retail Forecasting — discusses incorporating promotional data to improve forecast accuracy. MDPI Proceedings. [Google Scholar] [Crossref]

15. Umair Ejaz & Mathew Gimah (2024), Predictive Analytics for Customer Retention, researchgate [Google Scholar] [Crossref]

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