Identifying growth opportunities, margin inefficiencies, and data quality issues in a retail company's operations through business-focused data analysis.
| Detail | Description |
|---|---|
| Tool | Tableau Desktop |
| Dataset | transactions_clean.xlsx — 25,000 retail transactions |
| Period | January 2024 – February 2026 |
| Scope | Revenue, Profit, Margin, Return Rate by Product, Category, Region & Channel |
📦 retail-commercial-monitor/
├── 📊 dashboard/
│ ├── Commercial_Monitor.twbx ← Tableau packaged workbook
│ └── screenshots/
│ ├── dashboard_1_performance_monitor.png
│ └── dashboard_2_product_analysis.png
├── 📂 data/
│ └── transactions_clean.xlsx ← Clean dataset (25K transactions)
└── README.md
KPIs, Revenue vs. Profit trend over time, and Revenue by Product
Key KPIs at a glance:
- 💰 Total Revenue: $3,689,482
- 📈 Total Profit: $1,213,857
- 📊 Avg. Margin: 31.05%
- 🔄 Return Rate: 8.61%
Revenue vs. Margin scatter plot with outlier detection + interactive order table
Finding: The company is heavily reliant on the Electronics category to drive the bulk of its revenue, leaving significant untapped potential in its second-strongest category: Home.
Evidence: The top 5 Electronics products each generate over $360K individually (Webcam leads at $397K), while the top Home product (Lamp) reaches only $194K — a gap of more than 50%. In the scatter plot, the Electronics cluster dominates the right side of the revenue axis with a standard ~30% margin.
Recommendation: Implement a cross-selling strategy by bundling Electronics best-sellers (e.g., Monitor, Webcam) with high-potential Home products (e.g., Lamp, Desk Chair). This would accelerate Home category growth without requiring new inventory investment.
Finding: Several products in the portfolio offer exceptionally high margins (≥80%) but are currently generating near-zero sales volume — representing the most immediate opportunity to boost net profit.
Evidence: In the scatter plot (Dashboard 2), points labeled "T" (Revenue Outliers) cluster in the upper-left quadrant: margins above 80% with revenue between $0–$20K. These products are virtually invisible to the business today.
Recommendation: This is the highest-priority action. Two levers to activate:
- Targeted marketing campaigns focused specifically on these high-margin SKUs
- Commission incentive restructuring — reward sales teams more aggressively for selling these products
Since these items already exist in inventory, any increase in sales volume translates directly into net profit with no additional cost burden. This is the fastest path to injecting liquidity and maximizing short-term profitability.
Finding: A segment of transactions is categorized under an "Unknown" product label, creating a blind spot in performance analysis and preventing accurate SKU-level tracking.
Evidence: Both the order detail table and the scatter plot show transactions with 20–30% margins registered under "Unknown" — indicating these products were not correctly mapped in the system at the point of entry.
Recommendation: Conduct a cross-functional data audit between the Sales and Operations teams to reconcile these SKUs. Correcting the mapping will unlock cleaner reporting and may reveal additional revenue from currently unattributed products.
| Priority | Insight | Timeline | Expected Impact |
|---|---|---|---|
| 🥇 #1 | Push high-margin products (≥80%) via campaigns + incentives | Short-term | Immediate profit boost |
| 🥈 #2 | Cross-sell Electronics + Home bundles | Medium-term | Revenue diversification |
| 🥉 #3 | Audit and fix "Unknown" SKU registrations | Short-term | Data integrity & visibility |
- Tableau Desktop — Dashboard design, scatter plots, time series, KPI cards, interactive filters
- Excel / Power Query — Data cleaning and preparation (25K rows)
- Business Analysis — Margin analysis, outlier detection, cross-selling strategy, revenue decomposition
- Data Storytelling — Translating visual patterns into prioritized, actionable business recommendations
Isabella Rondón — Business Intelligence & Data Analyst | Economist
Passionate about turning complex datasets into strategic decisions that make businesses more competitive.