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Retail Sales Performance Dashboard — Retool + PostgreSQL
A full sales analytics dashboard built in Retool, backed by a real ~10,000-row retail dataset — revenue trends, category performance, regional breakdowns, and top products, all queried live with SQL.
What's in it:
---> KPI summary cards showing total revenue and total profit across the full dataset
---> A monthly revenue trend chart showing seasonality and growth over time
---> A category breakdown showing how sales are distributed across product categories
---> A top-10 products table ranked by revenue, with units sold
---> A regional performance breakdown comparing sales and profit across regions
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Title:
🛒 Retail Sales Dashboard
Description:
Built an interactive Retail Sales Dashboard to monitor total sales, customer orders, product performance, regional sales, payment methods, and revenue trends. Designed dynamic KPI cards, sales insights, and interactive visualizations using Microsoft Excel, SQL, and Power BI to help businesses improve retail performance and make data-driven decisions.
ABC Classification (Pareto Analysis) — SQL-Based Inventory Prioritization
Analyzed 293,569 orders and 2,350 SKUs to identify which products actually drive revenue — and which don't.
Approach:
Built a CTE-based ABC classification in SQL Server, ranking products by cumulative revenue contribution using window functions (SUM() OVER, CUME_DIST(), PERCENT_RANK()), then segmented into three tiers based on Pareto thresholds.
Result:
Category A (20% of products) → 70%+ of total revenue
Category B (30% of products) → 20-25% of revenue
Category C (50% of products) → 5-10% of revenue
Business impact:
This reprioritizes where operational effort should go — tighter stock monitoring and faster reordering for Category A, reduced tracking overhead for Category C. The same logic fed into a related late-delivery root cause analysis on the same dataset.
Stack: SQL Server · Tableau
