An end-to-end Customer Intelligence, Churn Analytics, Capital Allocation, and Executive Decision Support solution built using PostgreSQL, SQL, Python, and Power BI.
Organizations invest heavily in acquiring customers but often struggle to answer a more important question:
Which customers should be retained, where is future revenue most at risk, and how should limited retention budgets be allocated?
The Customer Growth Strategy Engine was developed to address this challenge by transforming customer, transaction, engagement, and support data into actionable executive intelligence.
The project combines customer segmentation, churn risk assessment, customer lifetime value analysis, retention economics, capital allocation modeling, geographic strategy, and executive dashboards into a unified decision-support ecosystem.
Unlike traditional reporting solutions that focus primarily on historical performance, this project emphasizes forward-looking business decisions by identifying where management should invest resources to maximize future customer value and protect revenue.
Organizations frequently face the following challenges:
- Limited visibility into customer value distribution
- Difficulty identifying customers likely to churn
- Inability to quantify revenue exposure
- Inefficient allocation of retention budgets
- Lack of executive-level customer intelligence
- Fragmented reporting environments
As a result, retention initiatives are often reactive rather than proactive.
The objective of this project is to build a scalable analytical framework capable of supporting:
- Customer Growth Strategy
- Churn Reduction
- Revenue Protection
- Capital Allocation Optimization
- Executive Decision Making
Raw Data Sources
↓
PostgreSQL Data Warehouse
↓
SQL Analytics Layer
↓
Python Analytics Framework
↓
Analytical Output Datasets
↓
Power BI Dashboard
↓
Executive Decision Intelligence
- PostgreSQL
- SQL
- Python
- Pandas
- NumPy
- Power BI
- Jupyter Notebook
- VS Code
- Git
- GitHub
Business data from multiple domains is collected and standardized.
Data domains include:
- Customer Subscription Data
- Retail Transactions
- E-Commerce Events
- Customer Support Activity
A PostgreSQL warehouse was developed using:
- Staging Tables
- Dimension Tables
- Fact Tables
- Reporting Tables
The warehouse serves as the central analytical foundation of the project.
Customer behavior and value are evaluated through:
- Customer Segmentation
- Customer Value Analysis
- Behavioral Assessment
The churn framework identifies:
- High-Risk Subscribers
- Revenue Exposure
- Retention Opportunities
- Geographic Risk Concentration
A retention investment model was developed to support:
- Budget Allocation
- Investment Prioritization
- Expected ROI Evaluation
- Resource Optimization
Analytical outputs are delivered through a multi-page Power BI dashboard designed for executive stakeholders.
The Power BI solution consists of six integrated reporting pages.
Provides a high-level summary of:
- Customer Base
- Revenue Exposure
- Customer Value
- Investment Performance
- Strategic Priorities
Analyzes customer composition and strategic customer groups.
Key segments include:
- Champions
- Loyal Customers
- Potential Loyalists
- Need Attention
- At Risk
- New Customers
- Lost Customers
Evaluates:
- Churn Rates
- High-Risk Subscribers
- Revenue at Risk
- Geographic Churn Concentration
Supports:
- Investment Prioritization
- Budget Allocation
- Customer Value Protection
- Expected Return Analysis
Identifies:
- Geographic Revenue Exposure
- High-Risk Subscriber Clusters
- Regional Retention Opportunities
Combines customer value, churn risk, revenue exposure, and investment priorities into a single executive decision-support environment.
The project generates several business-ready datasets.
Supports:
- Customer Classification
- Segment Analysis
- Strategic Prioritization
Supports:
- Churn Monitoring
- Retention Evaluation
- Risk Assessment
Supports:
- Regional Planning
- Market Prioritization
- Geographic Strategy
Supports:
- Investment Planning
- Budget Optimization
- ROI Assessment
Understand customer composition and value distribution.
Identify vulnerable customers before revenue loss occurs.
Measure and prioritize revenue exposure.
Allocate retention investments more effectively.
Identify market-specific risks and opportunities.
Transform analytical outputs into actionable recommendations.
Customer_Growth_Strategy_Engine/
dashboard/
├── Customer_Growth_Strategy_Engine.pbix
└── executive_theme.json
data/
├── raw/
├── processed/
└── outputs/
docs/
├── 01_Executive_Summary.md
├── 02_Business_Problem_and_Project_Objectives.md
├── 03_Data_Architecture_and_Warehouse_Design.md
├── 04_Analytics_Methodology.md
├── 05_Customer_Growth_Strategy_Engine.md
├── 06_Dashboard_and_Executive_Insights.md
├── 07_Business_Recommendations.md
├── 08_Technical_Implementation_Guide.md
└── assets/
notebooks/
└── Customer_Growth_Strategy_Analysis_Portfolio.ipynb
presentation/
└── Customer_Growth_Strategy.pptx
sql/
├── 00_create_staging_tables.sql
├── 01_create_schema.sql
├── 02_create_dimensions.sql
├── 03_create_facts.sql
├── 04_load_dimensions.sql
├── 05_load_facts.sql
└── 06_create_reporting_tables.sql
run_analytics.py
README.md
The analytical framework supports several strategic recommendations:
Protect customers generating the greatest long-term value.
Identify and engage vulnerable customers before attrition occurs.
Allocate retention budgets using customer value and risk indicators.
Focus resources on markets with elevated risk and opportunity.
Transition from reactive reporting toward proactive customer management.
Detailed project documentation is available within the /docs directory.
| Document | Description |
|---|---|
| 01 | Executive Summary |
| 02 | Business Problem and Objectives |
| 03 | Data Architecture and Warehouse Design |
| 04 | Analytics Methodology |
| 05 | Customer Growth Strategy Engine |
| 06 | Dashboard and Executive Insights |
| 07 | Business Recommendations |
| 08 | Technical Implementation Guide |
The Customer Growth Strategy Engine demonstrates how customer analytics, churn intelligence, customer value assessment, retention economics, capital allocation strategy, and executive reporting can be integrated into a single business decision-support ecosystem.
The project moves beyond descriptive reporting and focuses on answering a more important executive question:
What should management do next to maximize customer growth while protecting future revenue?
Raj Joshi
MBA (Business Analytics)
- LinkedIn: https://linkedin.com/in/rajjoshi0408
- GitHub: https://github.com/unhield