🧠 Crowdfunding Analytics Dashboard

Built with SQL | Tableau | Power BI | Excel
A comprehensive end-to-end analytics solution to uncover what makes a crowdfunding campaign succeed — powered by data storytelling and real-time visual insights.

---

📌 Overview

This project explores Kickstarter's crowdfunding dataset, analyzing over 365,000+ projects across multiple categories and countries to discover patterns in project success, backer behavior, funding trends, and campaign strategies.

---

🧰 Tools Used

• SQL (MySQL) – Data cleaning, transformation, and calendar table generation
• Tableau – Interactive dashboards with filters and advanced charts
• Power BI – Business KPIs and visual exploration across timelines
• Excel – Raw data analysis, pivot dashboards, data prep

---

📂 Project Structure

• Crowdfunding.sql – SQL scripts for:

  • Converting Unix timestamps to readable dates
  • Creating calendar table using CTE recursion
  • Category, location, and goal-based success analysis
  • Generating project KPIs like success rates, pledged amount, duration

• CROWDFUNDING.pptx – Final project presentation including:

  • Kickstarter overview & data understanding
  • Business insights & storytelling
  • Dashboard visuals from Excel, Power BI, and Tableau
  • Summary & recommendations

---

📊 Dashboards Included

Tool	Dashboard Types	Description
Excel	Dashboard 1, 2	Basic trend & KPI visualizations using slicers and pivot charts
Tableau	Dashboard 1, 2, 3	Interactive filtering by category, goal range, location, outcome
Power BI	Dashboard 1, 2, 3	Advanced BI visuals, KPIs with DAX, and trend analysis across time

---

🎯 Key Insights

• Projects with funding goals between $1K–$10K showed the highest success rates
• Categories like Music, Games, and Design consistently outperformed others
• Peak success observed during March and Q1 of most years
• Countries like US, UK, and Canada led in both project count and success
• Some top campaigns raised over $20M and attracted 200K+ backers

---

✅ Use Cases

• Creators: Set ideal funding goals, launch times, and category selection
• Investors: Identify credible project patterns based on data
• Platforms: Optimize search, UX, and campaign guidelines
• Analysts & Learners: Practice end-to-end dashboard creation using real-world data

---

⚠️ Challenges Faced

• Handling Unix timestamp conversion in SQL
• Resolving inconsistent and missing values in location and category data
• Optimizing heavy Power BI visuals for performance
• Managing fact-dimension joins in large datasets
• Filtering out non-credible campaigns with unrealistic goals or 0 backers

---

📬 Access Request

💡 Want to explore the files?
Feel free to reach out and I’ll be happy to share:

• ✅ Tableau files (.twbx)
• ✅ Power BI files (.pbix)
• ✅ Excel dashboards (.xlsx)

---

📎 Contact

📧 Email: [email protected]
🔗 LinkedIn: linkedin.com/in/sumitkumarss
💻 GitHub: github.com/sumit9000

---