A Power BI dashboard that does more than just display metrics — it pinpoints why things happen. Built to drive decisions, not just visualizations.
This project dives deep into retail sales data to uncover not just what’s happening, but why it’s happening. By integrating performance metrics, anomaly detection, and root cause layers, this dashboard delivers a complete analytical narrative that’s both executive-friendly and actionable.
- Track core retail KPIs: Revenue, Profit, Discount, and Delivery Timeliness.
- Identify regional and category-wise profit patterns.
- Surface root causes behind profit dips.
- Enable drill-down with filters to assist category managers and business teams.
| Metric | Description |
|---|---|
| 🧾 Total Revenue & Profit | Track performance across all sales over a year |
| 💸 Avg. Discount | Understand how discounting impacts margins |
| 🚚 Late Deliveries | Highlight logistics issues affecting profitability |
| 📉 Monthly Profit Trend | Time-series analysis segmented by region |
| 🧩 Root Cause Widgets | Combine Category, Discount, and Delivery to isolate drivers of poor performance |
- 🔻 South Region: Profit fell significantly post-July 2024.
- 🔥 High Discounts (21%+): Strong link to margin erosion, especially in Technology category.
- 🛻 Late Deliveries: Consistently correlated with lower average profit per order.
| Tool | Purpose |
|---|---|
| Power BI | Dashboard creation, DAX measures, interactivity |
| Excel | Data cleaning and preprocessing |
| DAX | Custom calculations like discount bins, average delivery profit, and conditional KPIs |
Profit Margin % = DIVIDE([Total Profit], [Total Revenue], 0)
Late Delivery % =
DIVIDE(CALCULATE(COUNTROWS(Sales), Sales[Delivery Status] = "Late"), COUNTROWS(Sales))
Discount Bin =
SWITCH(TRUE(),
Sales[Discount] <= 0.10, "0%-10%",
Sales[Discount] <= 0.20, "11%-20%",
"21%-30%")
🔍 Filters for Deep Dive
Region (Central, East, South, West)
Category (Technology, Furniture, Office Supplies)
Discount Bin (0–10%, 11–20%, 21–30%)
Date Range
📌 Use Case This dashboard is ideal for:
Retail managers identifying sales decline patterns.
Category leads adjusting discounting strategies.
Operations teams fixing delivery bottlenecks.
🚀 What’s Next Add predictive capabilities (forecasting next quarter’s profit).
Integrate shipping data to analyze vendor-level delays.
Include dynamic benchmarking against market targets.
🤝 Let’s Connect If you’re a hiring manager, data nerd, or just love a good dashboard — check out my Portfolio Website or connect on LinkedIn.