Customer Segmentation Solution Accelerator

Transform customer data into actionable business insights with modern RFM analysis and behavioral segmentation.

🚀 What is Customer Segmentation?

Customer segmentation divides your customer base into distinct groups based on shared characteristics and behaviors. This solution creates 6 distinct customer segments:

1. High-Value Loyalists - Premium customers generating highest revenue
2. Frequent Shoppers - Regular customers with consistent purchase patterns
3. Discount Hunters - Price-sensitive customers responding to promotions
4. Occasional Buyers - Sporadic purchasers needing engagement
5. New/Inactive Customers - Recent sign-ups or dormant accounts
6. Category Specialists - Customers focused on specific product categories

Each segment receives tailored strategies with 150-200% expected ROI for high-value segments.

📦 Installation

This solution uses Databricks Asset Bundle for deployment:

# Clone the repository
git clone https://github.com/databricks-industry-solutions/customer-segmentation.git
cd customer-segmentation

# Deploy to Databricks
databricks bundle deploy

# Run the complete workflow
databricks bundle run customer_segmentation_demo_install

Prerequisites

• Databricks workspace with Unity Catalog enabled
• Databricks CLI installed and configured
• Cluster creation permissions

🏗️ Project Structure

customer-segmentation/
├── databricks.yml                 # Databricks Asset Bundle configuration
├── notebooks/
│   ├── 01_Data_Setup.py          # Synthetic data generation
│   ├── 02_Segmentation_Lakeflow.py    # Lakeflow Declarative Pipelines for segmentation
│   └── 03_Business_Insights.py   # Business visualizations
└── .github/workflows/             # CI/CD automation

🔄 Segmentation Pipeline

The solution implements a 3-stage customer segmentation pipeline:

Stage 1: Data Setup

• Generates 1,000 synthetic customers with realistic demographics
• Creates transaction history with seasonal patterns and behavioral variety
• Stores data in Unity Catalog managed tables

Stage 2: Segmentation Analysis (Lakeflow Declarative Pipelines)

• RFM Analysis: Calculates Recency, Frequency, and Monetary scores
• Behavioral Clustering: Groups customers by purchase patterns
• Segment Profiles: Creates business-ready segment characteristics

Stage 3: Business Insights

• Interactive Visualizations: 5 essential charts using Plotly
• Actionable Recommendations: ROI-focused strategies per segment
• Executive Summary: Business-ready insights and next steps

⚙️ Configuration

Create a .env file based on .env.example:

# databricks.yml variables
variables:
  catalog_name: your_catalog_name
  schema_name: your_schema_name

📊 Expected Business Impact

Based on industry benchmarks, implementing this segmentation strategy delivers:

• 20% average revenue lift through targeted campaigns
• 15-30% improvement in customer lifetime value
• 40% increase in marketing campaign effectiveness
• 25% reduction in customer acquisition costs

🎨 Visualization Highlights

The solution includes 5 essential visualizations:

1. Customer Distribution - Segment size analysis
2. Revenue Distribution - Revenue concentration by segment
3. Performance Metrics - Customer value benchmarks
4. Lifetime Value - CLV projections by segment
5. ROI Analysis - Business impact projections

🔧 Technical Architecture

• Unity Catalog: Data governance and managed tables
• Lakeflow Declarative Pipelines: Declarative data pipelines
• Serverless Compute: Cost-effective processing
• Plotly Express: Accessible, interactive visualizations
• Synthetic Data: No external dependencies

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📄 Third-Party Package Licenses

© 2025 Databricks, Inc. All rights reserved. The source in this project is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below.

Package	License	Copyright
plotly>=5.15.0	MIT	Copyright (c) 2016-2023 Plotly, Inc
numpy>=1.21.0	BSD-3-Clause	Copyright (c) 2005-2023, NumPy Developers
pandas>=1.5.0	BSD-3-Clause	Copyright (c) 2008-2023, AQR Capital Management, LLC
scikit-learn>=1.3.0	BSD-3-Clause	Copyright (c) 2007-2023 The scikit-learn developers
Faker	MIT	Copyright (c) 2012-2023 joke2k

📜 License

This project is licensed under the Databricks License - see the LICENSE file for details.

⚠️ Disclaimer

Please note the code in this project is provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.