AWS Customer Behavior Analytics Pipeline

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🚨 Business Problem

An e-commerce platform is facing:

• Extremely low customer retention (~3%)
• Revenue concentrated among a small group of users
• Lack of visibility into customer purchasing behavior

This creates dependency risk and limits long-term revenue growth.

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💡 Solution

Built a scalable AWS-based analytics pipeline to process customer transaction data and generate actionable business insights.

The system transforms raw data into structured insights to support decision-making around customer retention and revenue optimization.

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🏗️ Architecture

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⚙️ Pipeline Flow

Raw Data → S3 (Data Lake) → AWS Glue (ETL & Catalog) → Athena (SQL Analysis) → Dashboard (Visualization)

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🛠️ Tech Stack

• AWS S3 (Data Storage)
• AWS Glue (ETL + Data Catalog)
• Amazon Athena (Serverless SQL)
• SQL
• Python (Data Cleaning & Preprocessing)
• Tableau (Visualization)

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⚙️ Data Engineering Considerations

• Implemented layered data architecture (raw → processed → curated)
• Applied partitioning on order data to optimize Athena query performance
• Used AWS Glue for schema inference and catalog management
• Leveraged Athena for cost-efficient, serverless querying

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📊 Key Insights

• Repeat customers spend ~2x more than one-time users
• Customer retention is critically low (~3%)
• Revenue is heavily concentrated among a small segment of users
• Delivery delays have limited impact on customer retention

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📌 Business Recommendations

• Focus on retention strategies (highest ROI opportunity)
• Target high-value repeat customers
• Introduce loyalty programs to improve repeat purchase rate
• Reduce dependency on one-time buyers

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🎯 Why This Project Matters

This project demonstrates how raw transactional data can be transformed into actionable business insights using a scalable, cloud-based data pipeline.

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📸 Implementation Proof

S3 Data Lake Structure

Glue Data Catalog

Athena Query Execution

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📁 Project Structure

customer-behavior-analytics-aws/
│
├── data/
│ ├── raw/
│ ├── processed/
│
├── scripts/
│ ├── customer_data_preprocessing_eda.ipynb
│
├── sql/
│ ├── customer_segmentation.sql
│ ├── revenue_analysis.sql
│ ├── delivery_performance.sql
│
├── architecture/
│ ├── architecture.png
│ ├── s3_data_lake_structure.png
│ ├── glue_catalog.png
│ ├── athena_query_execution.png
│
├── dashboard/
│ ├── dashboard.png
│
├── README.md

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🚀 Future Improvements

• Real-time data processing using AWS Kinesis
• Churn prediction using machine learning
• Automated ETL workflows and scheduling

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📌 Author

Sujal Gupta