Projects / Tableau / Dashboard

Retail Sales Performance Engine

View Live Dashboard

Developed a full-scale retail intelligence system to audit 10,000 transactions across the European market. By consolidating disparate data points into an interactive executive-level view, this project identifies high-growth geographic hubs and isolates "profit-bleed" sub-categories, shifting retail management from reactive reporting to proactive, margin-focused strategy.

10K Transactions Audited
20 High Value Customers Identified
Top 3 Market Leaders(DE, FR, UK)
14.5M Total Revenue
Project Overview

Retail Sales Performance Dashboard

This project focuses on developing a full-scale Retail Sales Performance Dashboard for a European retail dataset. The objective was to analyze key business metrics such as revenue, profit, customer distribution and product performance, and to design an interactive Tableau dashboard for business decision-making.

Using a synthetic but realistic dataset of 10,000 European retail transactions, the project replicates the challenges faced by commercial analytics teams in sales-driven organizations.

Business Impact

The engine transforms raw transactional data into actionable intelligence. By isolating the Top 10 products and customers alongside geographic "Heat Maps," the dashboard allows the supply chain team to optimize stock for high-demand areas (like Germany and the UK) while suggesting price adjustments or sunsets for negative-margin items in the Furniture category.

📊
10.000
Transactions Analyzed
⚡
5
Core KPIs (Sales, Profit, Margin, Volume, Customers)
🎯
21
Unique Fields (Demographic, Geographic, & Financial)
Problem Statement

The Challenge

Retail managers frequently operate with "data silos," where sales volume is visible but actual profitability is obscured. In this European dataset, the company faced five critical visibility gaps:

  • Margin Blindness:Inability to see which products were driving revenue but losing money.
  • Segment Variance:Unclear understanding of how Corporate vs. Home Office segments contribute to the bottom line.
  • Geographic Underperformance: Difficulty identifying which European regions required intervention.
  • Static Reporting: Lack of real-time, interactive tools for executive-level "drill-downs."

Methodology

Solution Approach

Phase 1
Discovery & Data Integrity
Generated and cleaned a 10,000-record dataset. Used Excel for rigorous ETL (Extract, Transform, Load) processes, handling null values in profit metrics and ensuring 100% accuracy in date-time formatting for monthly trend analysis.
Phase 2
KPI Logic & Modeling
Engineered core KPI headers. Beyond standard sales, I created a "Negative Profit" counter—a critical risk metric that flags the total number of orders resulting in a financial loss.
Phase 3
Spatial & Temporal Analysis
Mapped sales distribution across Europe. Built a temporal model to track monthly fluctuations, allowing managers to distinguish between seasonal peaks and genuine structural declines in sales
Phase 4
Multi-Level Segmentation
Implemented a 80/20 stratified split. Evaluated performance using Confusion Matrices and Classification Reports, focusing on the F1-score to balance precision and recall for business utility.
Phase 5
Loss Mitigation Framework
Isolated the "Bottom 10" products. By visualizing the specific sub-categories with negative margins, the dashboard provides a roadmap for SKU rationalization or pricing adjustments.
Technical Implementation

Technical Dive

  • 📊 Tableau Public:Final Interactive Visualization & Hosting
  • 📁 Microsoft Excel:Data Cleaning, Deduplication, & Modeling
  • 🤖 ChatGPT:Synthetic Data Generation & Logic Validation
  • 🐙 GitHub:Documentation & Portfolio Hosting
Impact & Outcomes

Results & Strategic Findings

The engine provides a clear roadmap for European expansion and margin protection.

  • 🎯 Segment Optimization: Technology identified as the primary engine for both growth and profit.
  • 📍 Geographic Focus: Confirmed Germany, France, and the UK as the "Big 3" revenue pillars, suggesting concentrated logistics investment in these hubs.
  • 📉 Loss Identification: Isolated specific Furniture sub-categories as "Loss Leaders," providing data-backed evidence for price hikes.
  • 👑 Customer Retention: Identified the Top 20 high-value customers, enabling the sales team to launch targeted "White Glove" loyalty initiatives.
Insights & Reflections

Key Learnings

1. The "Top Line" Trap

High sales volume does not guarantee business health. The dashboard revealed that some of the highest-selling products in the Furniture category were actually destroying value due to negative margins.

2. Pareto Principle in Retail

A "Small Set" of customers contributes disproportionately to total profit. Protecting these relationships is 5x more valuable than acquiring new, low-margin consumers.

3. Regional Nuance

Performance varies drastically by country. "One-size-fits-all" pricing across Europe is ineffective; localized pricing strategies are required to maintain margins in underperforming regions.

4. Visibility Drives Action

Transitioning from a spreadsheet to a visual heatmap changed the conversation from "What happened?" to "Why is this region losing money?"—the core of business intelligence.