- Overview
- Project Objectives
- Key Business Analysis and Insights
- Tools and Techniques Used
- Business Value Delivered
- Challenges and Learnings
- Sample Dashboard Screenshots
- Project Status
This project focuses on delivering business intelligence solutions through an executive-grade Power BI dashboard analyzing the sales, customer, and regional performance of a global bike retailer.
The primary goal was to uncover actionable insights that drive business strategy — including revenue optimization, market expansion, product portfolio management, and customer loyalty growth — by transforming fragmented raw data into structured, decision-ready analytics.
- Build a dynamic, real-time dashboard enabling executives to monitor KPIs and identify emerging trends proactively.
- Integrate and model fragmented sales, customer, and geography datasets into a coherent star schema structure.
- Perform deep exploratory data analysis (EDA) to surface strategic business opportunities and risks.
- Enable non-technical stakeholders to independently explore insights via self-service analytics.
🔎 A full detailed business report is available here, containing extended analysis, findings, and strategic recommendations.
- Identified peak seasonality between March and June across multiple years, enabling better promotional campaign alignment.
- Tracked multi-year revenue growth patterns, highlighting areas for continued investment and forecasting opportunities.
- Analyzed profit margins over time, revealing relative margin stability (~40%) despite revenue fluctuations.
- Surface Regional Disparities:
The United States contributed 32% of total revenue, making it the primary market focus, with emerging opportunities identified in Australia and Germany. - Informed Market Expansion Strategy:
Visualization of regional trends guided recommendations for targeted expansion and marketing resource allocation.
- Volume vs. Value Product Dynamics:
Accessories like Water Bottles and Tire Tubes drove high transaction volumes, while premium bikes (e.g., Mountain-200 series) dominated revenue generation. - Return Rate Risk Identification:
Higher-than-average return rates among helmets and high-end bikes flagged operational and customer satisfaction risks. - Strategic Inventory Management:
Findings suggested optimizing inventory mix to balance volume drivers and premium revenue generators for profit maximization.
- Income and Occupation-Based Segmentation:
Professionals and Skilled Manual workers accounted for over 76% of order volume, guiding customer targeting strategies. - High-Value Customer Concentration:
The top 100 customers (less than 1% of the customer base) contributed over $615K in revenue, reinforcing the need for loyalty programs and personalized engagement. - Revenue per Customer Trend Analysis:
Detected slight declines in revenue per customer, indicating the need for upselling and retention initiatives to maintain customer lifetime value.
- Power BI: Dashboard design, DAX measures, interactive visualizations
- Data Modeling: Star schema relational model
- Data Cleaning: Handling missing values, duplicates, and standardization
- Exploratory Data Analysis (EDA): Seasonal trends, regional performance, customer behaviors
- Business Analytics: Market expansion targeting, customer segmentation, operational risk mitigation
- Enabled real-time executive visibility over a $24.9M+ revenue portfolio.
- Supported strategic business initiatives in market expansion, product optimization, and customer loyalty growth.
- Fostered a self-service, data-driven decision-making culture, empowering non-technical stakeholders to engage with key performance drivers.
- Overcame data quality challenges by implementing thorough preprocessing pipelines.
- Balanced dashboard depth and complexity to prioritize business usability over technical intricacy.
- Strengthened strategic analysis capabilities by focusing on actionable, executive-relevant insights.
✅ Completed — April 2025