📊 Customer Churn Risk Analysis Dashboard

📊 Dashboard Preview

📌 Business Problem

Customer churn directly impacts revenue. The goal of this project is to identify users at risk of churn and recommend actions to retain them.

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📊 Dataset

Transaction-level dataset containing:

• user_id
• order_id
• order_date
• amount

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🧠 Approach

1. Data Cleaning

• Removed invalid dates
• Removed negative/refund amounts
• Eliminated duplicate records

2. Feature Engineering

• Converted event-level data to user-level

• Used SQL window functions:

  • ROW_NUMBER() → sequence purchases
  • LAG() → track previous order behavior

3. Risk Identification

Users are marked as at risk if:

• No purchase in the last 30 days (Inactivity)
• Last two purchases show declining spend

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📈 Key Insights

• 20% of users are at risk of churn
• High-value users are becoming inactive
• Some users show consistent decline in spending behavior

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🎯 Business Recommendations

• Re-engage inactive users with personalized campaigns
• Use upsell/cross-sell strategies for declining users
• Prioritize high-value users to maximize impact

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📊 Dashboard Features

• KPI cards (Total Users, At-Risk Users, % At Risk)
• Risk distribution by reason
• Priority list of at-risk users
• Key insights and recommended actions

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🛠️ Tools Used

• SQL (Data cleaning & transformation)
• Power BI (Dashboard & visualization)

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📂 Project Structure

customer-churn-analysis/
│
├── churn_analysis.sql
├── churn_dashboard.pbix
├── orders.csv
├── README.md

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🚀 How to Use

1. Open SQL file to understand data transformation logic
2. Load dataset into Power BI
3. Open .pbix file to explore dashboard

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📌 Future Improvements

• Include user segmentation (high/low value users)
• Add time-based churn prediction
• Scale analysis with larger datasets

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