Revenue Intelligence Dashboard

Project Overview: Developed a dynamic Power BI dashboard that consolidated data from multiple sources to provide actionable insights into revenue streams, customer behavior, and sales performance. The dashboard featured interactive visualizations, drill-down capabilities, and automated data refreshes to ensure up-to-date information for decision-makers. Challenge: Leadership lacked real-time visibility into revenue performance across regions, products, and customer segments. Reporting was manual, inconsistent, and required multiple spreadsheets, making it difficult to identify trends or take timely action. Role: Lead Analytics Consultant responsible for data modeling, DAX measure development, dashboard design, and stakeholder alignment. Data Sources: • SQL Server (fact tables, dimension tables) • CRM exports (customer attributes) • Finance spreadsheets (targets, budgets) Tools & Technologies: Power BI, SQL, DAX, Power Query, Data Modeling (Star Schema) Process: 1. Data Collection - Connected to SQL Server and imported fact_sales, dim_customer, dim_product, and dim_region. - Standardized CRM and finance spreadsheets using Power Query. 2. Data Cleaning - Resolved inconsistent customer IDs across systems. - Removed duplicate transactions and corrected date hierarchies. - Normalized product categories for consistent reporting. 3. Data Modeling - Designed a clean star schema with relationships based on surrogate keys. - Built reusable DAX measures for revenue, YoY growth, retention, and customer lifetime value. 4. Analysis - Identified top-performing regions and underperforming product lines. - Segmented customers by revenue contribution and churn risk. - Analyzed seasonality and promotional impact. 5. Visualization - Designed an interactive Power BI dashboard with drill-throughs, bookmarks, and KPI cards. - Created a clean, executive-friendly layout with consistent color coding and typography. Key Insights: • 62% of total revenue came from only 18% of customers (Pareto pattern). • Region C underperformed due to a 14% decline in repeat purchases. • Seasonal dips in Q3 were linked to supply chain delays. • High-value customers showed strong retention but low engagement in new product lines. Business Impact: • Reduced reporting time from 6 hours per week to under 10 minutes. • Enabled leadership to make data-driven decisions during monthly reviews. • Identified £1.2M in potential revenue recovery opportunities. • Improved visibility led to a 9% increase in quarterly revenue forecasting accuracy.