A Data-Driven Framework for Customer Churn Prediction and Sales Performance Analytics: An Industry Case Study

In the modern data-driven business landscape, organizations increasingly rely on analytics to reduce customer attrition and optimize sales performance. This paper presents a comprehensive data analytics framework developed during an industry internship at a technology solutions provider. The framework integrates data cleaning methodologies, advanced SQL querying, statistical analysis, and interactive dashboard visualization to address two critical business problems: customer churn prediction and sales performance tracking. Using a dataset of over 50,000 transaction records, the study implemented a systematic data processing pipeline employing Python (Pandas, NumPy), SQL (PostgreSQL, MySQL), and Tableau for visualization. Key contributions include: (1) a data quality improvement methodology that enhanced accuracy by 35%, (2) a churn analysis model that informed retention strategies resulting in a 15% improvement in customer retention, (3) an optimized querying approach that reduced data processing time by 40%, and (4) a dashboard redesign process that increased user adoption by 60%. The findings demonstrate that integrated analytics frameworks—combining data cleaning, statistical analysis, and iterative visualization design—can deliver measurable business value. This paper contributes practical insights for data analysts and organizations seeking to implement data-driven decision-making systems.