Churn Signal – Customer Retention Analysis
A customer retention analysis that identifies at-risk segments and provides actionable recommendations for reducing churn and improving customer lifetime value.
Overview
A customer retention analysis that identifies at-risk segments and provides actionable recommendations for reducing churn and improving customer lifetime value.
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Retention rate by signup cohort
This chart tracks retention rates for customers who signed up in each month. You can see how retention varies by cohort, which helps identify if recent changes to onboarding or product features are affecting customer stickiness.
Tech Stack
Key Features
- ▸Synthetic customer dataset with behavioral and transactional features
- ▸Cohort retention analysis by signup month
- ▸Churn rate analysis across multiple customer segments
- ▸Feature importance analysis using tree-based models
- ▸Risk-based customer segmentation (Low/Medium/High risk)
- ▸Business recommendations tied to data insights
Design Philosophy
Business-first approach: every analysis should answer "so what?" and connect directly to actionable recommendations. Use clear segmentation and risk scoring so the team can prioritize retention efforts effectively.
Design
Focused on clarity and speed: typography choices to improve scan-ability, high-contrast UI for readability, and a layout that highlights primary actions and content without distraction.
Development
Built with Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook. Emphasis on modular components, predictable state handling, and accessible interactions. Performance budgets guided media usage and bundle size.
Target Audience
Product managers, customer success teams, and business stakeholders who need to understand churn patterns and prioritize retention initiatives.
Deployment
Analysis pipeline built with Jupyter notebooks and reproducible data processing. Results are documented with visualizations and presented to the product and customer success teams.
Challenges
Creating a segmentation model that balances predictive accuracy with interpretability, identifying which customer behaviors are most predictive of churn, and translating findings into concrete retention strategies that the team can execute.
Outcomes
A working churn analysis pipeline that segments customers by risk level, identifies key churn drivers (payment issues, inactivity, low feature usage), and provides prioritized recommendations. The model helps focus retention efforts on high-risk customers where intervention has the most impact.