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. Champions - Premium customers generating highest revenue with frequent purchasing patterns
2. Loyal - High value customers with consistent purchase patterns and high revenue
3. Regular - Regular customers, with normal purchasing patterns and revenue
4. New Customers - New customers, only having made one purchase
5. At Risk - Customers who are at risk of churning, no recent activity
6. Churned - Customers who have already churned, need to win back

📦 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
• Ability to use Serverless compute (or Cluster creation permissions)

🏗️ Project Structure

customer-segmentation/
├── databricks.yml                 # Databricks Asset Bundle configuration
├── src/
│   ├── customer_segmentation.lvdash.json          The AI/BI dashboard. Make sure to change the catalog and schema names in this file to your catalog and schema
├── notebooks/
│   ├── 01_Data_Setup.py          # Synthetic data generation
│   ├── 02a_Segmentation_Lakeflow.py    # Lakeflow Declarative Pipelines for segmentation
│   ├── 02b_Segmentation_MLflow.py    # Unsupervised clustering with MLflow for segmentation (builds off of 02a_Segmentation_Lakeflow)
│   └── 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 or Unsupervised Clustering)

• 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

• AI/BI Dashboard: A dashboard for viewing RFM scores, trends, and customer demographics

⚙️ Configuration

Either:

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

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

or 2. Create a variable-overrides.json file under .databricks > bundle > {your target}

// variable-overrides.json variables
{
  "catalog_name": "your_catalog_name",
  "schema_name": "your_schema_name",
  "warehouse_id": "your_warehouse_id"
}

📊 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: Faker

🤝 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.