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PROJECT OVERVIEW

Steps
  1. 01PROJECT OVERVIEW
  2. 02DATASET REQUIREMENTS
  3. 03PROJECT ROADMAP
  4. 04STEP-BY-STEP IMPLEMENTATION
  5. 05EXPECTED OUTPUT AFTER EVERY MAJOR STEP — SUMMARY TABLE
  6. 06VISUALIZATION AND DASHBOARD
  7. 07INSIGHTS AND RECOMMENDATIONS
  8. 08FINAL PROJECT DELIVERABLES
  9. 09FURTHER READING & REFERENCES
  10. 10FINAL PROJECT CHECKLIST

PROJECT OVERVIEW

Project Title: E-commerce Business Performance Dashboard (Power BI)

Business Scenario: You are working as a Junior Data Analyst at BrightCart, a mid-sized e-commerce retailer that sells furniture, office supplies, and technology products online across the United States. Leadership has four years of order data (2014–2017) sitting in a spreadsheet export from the order system, but nobody has turned it into a decision-ready view. The COO wants to know where the business is actually making money — and where it’s quietly losing it.

Company / Industry Context: BrightCart runs on thin retail margins. Discounts are used constantly to win orders, but discounting without limits can turn a sale into a loss. Shipping speed also affects customer satisfaction and repeat purchases.

Business Problem: Sales look healthy on the surface (over $2.3M across the period), but profit is inconsistent — some product lines and regions are far more profitable than others, and some orders lose money outright. Nobody can currently answer “which products/regions should we double down on, and which discount practices should we fix?”

Project Objective: Build a Power BI dashboard that breaks down sales, profit, and discount behavior by category, region, customer segment, and time — and turn that into concrete pricing/inventory recommendations.

Key Stakeholders: - COO (main sponsor — wants the headline profit picture and the biggest risk areas) - Category Managers (want to know which sub-categories to push or discontinue) - Regional Sales Leads (want region-level performance, not just a national number) - Operations team (wants shipping-time performance by ship mode)

Final Business Goal: Protect and grow profit by identifying exactly which products, discount levels, and regions are helping — or hurting — the bottom line.

What Will the Student Finally Build?

  1. A cleaned, dashboard-ready e-commerce orders dataset

  2. A documented data-cleaning process (Power Query steps)

  3. A Power BI data model with calculated columns and DAX measures

  4. A multi-page interactive Power BI dashboard with KPI cards, charts, filters and drill-through

  5. A set of written business insights (Finding → Insight → Business Impact → Recommendation)

  6. A short final presentation / summary report of findings

Tools used in this project: Microsoft Power BI Desktop, Excel/CSV (as the data source), Power Query, DAX.