Advanced Excel Bike Sales Dashboard

❓ Problem Statement

Build an interactive dashboard using Advanced Excel to analyze bike sales data. The goal is to clean and transform raw data, extract insights, and present them visually to assist in decision-making regarding customer behavior, purchasing patterns, and market trends.

📊 Data Understanding

The dataset includes the following key attributes:

• Customer: Customer ID, Name, Age, Gender, Marital Status, Education Level
• Demographics: Children, Home Owners, Cars Owned
• Purchase Behavior: Purchased Bike (Yes/No), Income , Occupation
• Geographic Data: Region, Commute Distance

⚙️ Technology Stack

• Excel (Advanced Features): Data Cleaning, Transformation, Pivot Tables
• ETL Process in Excel: Data preprocessing using formulas and functions
• Pivot Tables: Data aggregation and summarization
• Data Visualization: Charts, slicers, and dashboards

🏗️ Methodology

1. Data Cleaning & Preparation

   • Removed duplicate values
   • Standardized text format (Upper, Lower, Proper case)
   • Eliminated unnecessary columns
   • Trimmed extra spaces
   • Replaced inconsistent data points with standardized abbreviations
   • Converted currency values into numerical format
   • Handled missing values using filtering and COUNTBLANK function
   • Rectified and grouped column names

2. Data Aggregation & Transformation

   • Applied SUM, COUNT, and AVERAGE for numerical insights
   • Performed grouping and bucketing of categorical data using nested-IF statements
   • Copied cleaned data to a new sheet for structured analysis
   • Used Pivot Tables to generate key metrics

3. Dashboard Creation

   • Designed an interactive Excel dashboard
   • Inserted dynamic charts and formatted axes
   • Added slicers and linked them to all relevant charts for dynamic updates.

📈 Key Insights Generated

🔹 Customer Insights

• Average income per purchase segmented by gender
• Purchasing behavior across different education levels
• Marital status impact on bike purchases

🔹 Sales & Performance Analysis

• Most common age group purchasing bikes
• Relationship between commute distance and purchase likelihood
• Region-wise distribution of purchases
• Occupation categories influencing bike sales

📊 Business Impact & Benefits

✅ Targeted Marketing: Insights help create tailored promotions based on customer demographics ✅ Better Inventory Planning: Understanding demand across regions optimizes stock management ✅ Improved Customer Engagement: Identifies high-value customer segments for loyalty programs ✅ Data-Driven Decision Making: Visual insights enable informed business strategies

🚀 Getting Started

1. Download the dataset and open in Excel
2. Follow the data cleaning steps outlined above
3. Use Pivot Tables to analyze key metrics
4. Build the dashboard using slicers and charts

🤝 Contributing

💡 Open for feedback & collaborations! Feel free to suggest improvements and contribute to this project.

👨‍💻 Author & 📌 Contact

Dushyanth KM 🔗 LinkedIn

📢 About

This project showcases how Advanced Excel techniques can be leveraged for effective data analytics and visualization.