NATIONAL SALES PERFORMANCE SCORECARD WITH TERRITORY-LEVEL DRILL-THROUGH AND PRODUCT REVENUE ATTRIBUTION

Abstract

This project presents the design and implementation of an intelligent data analytics system focused on building a comprehensive National Sales Performance Scorecard with advanced territory-level drill-through capabilities and product revenue attribution. The system enables organizations to monitor, analyze, and optimize sales performance across multiple geographic regions and product categories. Understanding sales performance at both national and granular territory levels helps businesses make strategic decisions, identify high-performing regions, and address underperforming areas effectively. Traditional reporting methods often rely on static dashboards and manual analysis, which are time-consuming and lack the flexibility needed for dynamic business environments. The proposed system utilizes historical sales data, including transaction records, product-wise revenue, territory-wise sales distribution, customer demographics, and purchase frequency. Data preprocessing techniques such as handling missing values, normalization, encoding categorical variables, and feature selection are applied to ensure data quality and consistency. Advanced data analytics and machine learning techniques are implemented to enhance insights. These include sales trend analysis, territory segmentation, revenue attribution modeling, and predictive analytics using algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and KNearest Neighbors (KNN). The models are evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix to determine optimal performance.