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🧠 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: 294sumitkumarsingh@gmail.com
🔗 LinkedIn: linkedin.com/in/sumitkumarss
💻 GitHub: github.com/sumit9000