Problem: Businesses struggle to analyze and optimize their sales performance due to fragmented data sources.
Outcome: Develop a data-driven analytics tool that provides real-time insights into sales performance and forecasting.
Week 1-2: Data Collection & Preprocessing
Collect sales transaction data from CRM, ERP, and POS systems.
Clean and preprocess data for consistency.
Week 3-4: Exploratory Data Analysis (EDA) & KPI Identification
Identify key sales performance indicators (KPIs).
Perform correlation analysis on sales trends.
Train ML models (Linear Regression, XGBoost) for sales forecasting.
Validate models with past sales data.
Create interactive dashboards using Tableau/Power BI.
Implement filters for region, product category, and sales team performance.
Week 9-10: Business Strategy Insights & Optimization
Provide actionable insights for improving sales strategies.
Implement automated reporting & alert mechanisms.
Week 11-12: Final Report & Deployment
Document findings and tool functionality.
Deploy the tool in a cloud-based environment for access.
