Retail Data Processing & Analytics Pipeline

A scalable, end-to-end retail data processing and analytics system built for the HCLTech Hackathon.
This project demonstrates the complete lifecycle of retail data — from ingestion and quality validation to analytics, customer segmentation, and automated notifications — using clean data engineering and analytics best practices.

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📌 Problem Statement Summary

The hackathon problem focuses on designing a robust retail data platform that can:

1. Ingest high-volume transactional data
2. Enforce strong data quality rules
3. Generate actionable business insights
4. Enable customer-centric analytics and engagement

The solution is structured around 6 real-world retail use cases, each mapped to a clean, modular pipeline.

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🎯 Objectives

• Build a reliable data ingestion and validation pipeline
• Generate meaningful, high-quality synthetic datasets suitable for ML training
• Enable promotion effectiveness analysis
• Implement loyalty point calculation and notifications
• Perform customer segmentation using RFM analysis
• Analyze inventory impact on sales performance
• Maintain data quality, traceability, and scalability

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🧠 Use Case Overview (Problem Statement Sections)

1. Automated Data Ingestion & Quality Validation

• Ingest sales header and line item data from CSV
• Load raw data without validation
• Apply business data quality rules
• Route clean data to staging and bad data to quarantine

2. Real-Time Promotion Effectiveness Analyzer

• Compare promoted vs non-promoted sales
• Measure sales lift by promotion and category
• Rank top-performing promotions

3. Loyalty Point Calculation Engine

• Apply rule-based loyalty logic per transaction
• Accrue and update customer loyalty points
• Maintain transaction-level traceability

4. Customer Segmentation for Targeted Offers

• Compute RFM (Recency, Frequency, Monetary) metrics
• Segment customers into:
  • High-Spenders
  • At-Risk Customers

5. Automated Loyalty Notification System

• Generate personalized loyalty emails
• Simulate email delivery via logs or tables
• Close the customer engagement loop

6. Inventory & Store Performance Correlation

• Identify top-selling products
• Analyze stock-out impact
• Estimate potential lost sales

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🏗️ High-Level Architecture

CSV Files
↓
Raw Schema (No Validation)
↓
Data Quality Rules Engine
├── Clean Data → Staging Schema
└── Bad Data → Quarantine Schema
↓
Analytics & Business Logic Layer
├── Promotion Analysis
├── Loyalty Engine
├── Customer Segmentation
└── Inventory Correlation
↓
Reporting / Dashboards / Notifications

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🗄️ Database Design

• Fully normalized 3NF relational schema
• Clear separation of concerns:
  • Raw schema – raw ingested data
  • Staging schema – clean, validated data
  • Quarantine schema – rejected records with error reasons
• Standard retail entities:
  • Stores, Products, Customers
  • Sales Header & Line Items
  • Promotions & Loyalty Rules

Design follows industry-standard retail transaction modeling.

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🔄 Data Generation Strategy

• Synthetic data generated using LLM-assisted pipelines
• Ensures:
  • Referential integrity across entities
  • Realistic sales, promotion, and customer behavior
  • Controlled injection of data quality issues
• Simulates:
  • Multiple stores
  • Multiple promotions
  • 7+ days of transactional data

📄 Detailed planning is documented separately in dataset_generation.md.

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🧪 Data Quality Rules (Examples)

• product_id must not be NULL
• line_item_amount must be positive
• store_id and customer_id must exist
• transaction_date must be valid
• Header total_amount must match sum of line items

Rejected records are preserved for auditing and analysis.

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⚙️ Technology Stack

Layer	Tools
Language	Python 3
Data Processing	pandas, SQL
Database	PostgreSQL / MySQL
Visualization	matplotlib / plotly
Orchestration	Python scripts
Data Format	CSV → Database
ML Readiness	Clean, labeled datasets

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📁 Repository Structure

├── data/
│ ├── raw/
│ ├── generated/
│ └── reference/
├── ingestion/
├── data_quality/
├── analytics/
│ ├── promotion_analysis/
│ ├── loyalty_engine/
│ ├── segmentation/
│ └── inventory_analysis/
├── notifications/
├── dataset_generation.md
├── README.md
└── requirements.txt
```````````


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## 👥 Team Responsibilities
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| Team Member | Responsibility |
|-----------|---------------|
| **Prateek** | Data generation pipeline implementation |
| **Surabhi** | Model training & ML experimentation |
| **Ravindar** | Hosting, deployment & infrastructure |
| **Manoranjan** | End-to-end pipeline orchestration |

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## 🧩 Design Principles

- **Simplicity first** – clear, readable pipelines
- **Modularity** – each use case independently runnable
- **Traceability** – no silent data loss
- **Scalability** – easy to extend for ML & real-time use
- **Industry-aligned modeling** – real retail data patterns

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## 🚀 Future Enhancements

- Real-time streaming ingestion (Kafka)
- ML-based customer churn prediction
- Promotion recommendation engine
- Cloud-native orchestration (Airflow)
- Real email/SMS notification integration

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## 📜 License

This project is developed as part of the **HCLTech Hackathon** and is intended for educational and demonstration purposes.

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## 🙌 Acknowledgements

- HCLTech Hackathon Team
- Open-source Python ecosystem
- Retail data modeling best practices