AI Churn Prediction & Retention
Python | ML | AI
In subscription-based businesses, customer churn is a silent revenue killer. This predictive analytics module shifts churn management from a reactive scramble into a proactive strategy. By combining machine learning to spot at-risk users early with generative AI to craft personalized save-offers, it helps customer success teams intervene before the cancellation happens.
Key features
Risk-scoring
AI Strategist
Live Dashboard
The how and the results
To stop subscription revenue from leaking out the back door, a fully automated predictive engine was built in Python. Here is how the system shifts churn management from a reactive scramble to a proactive strategy:
1. Spotting the Patterns (Machine Learning) Instead of guessing why customers leave, the system learns the warning signs from historical data. After cleaning and organizing thousands of raw customer records, multiple predictive models—including Logistic Regression, Random Forest, and XGBoost—were trained and tested to find the most accurate algorithm for separating loyal users from immediate flight risks.
2. Scoring the Risk (The Prediction) The winning machine learning model evaluates the active customer base, calculating a precise “churn probability score” for every single user. This data is automatically pushed into a Google Sheets database, serving as a fast, lightweight operational backend.
3. Generating the Save (Generative AI) Knowing who will leave isn’t enough; teams need to know how to keep them. The highest-risk profiles are routed directly into Gemini 2.5 Pro. Acting as a strategic advisor, the AI analyzes the user’s specific data constraints and generates highly personalized retention strategies—such as a targeted discount or a specific plan adjustment—designed to save that exact customer.
4. Empowering the Team (Interactive Deployment) To make these complex machine learning insights instantly usable for non-technical teams, the entire pipeline is wrapped in a custom Streamlit web app. Deployed via Streamlit Community Cloud, this live dashboard allows marketing and customer success stakeholders to dynamically filter high-risk cohorts and export action-ready CSVs in seconds.
The Results
This hybrid analytical system directly protects Monthly Recurring Revenue (MRR) and increases Customer Lifetime Value (CLV) by delivering:
Proactive Intervention: Customer Success teams can stop wasting time and focus their incentives exclusively on the highest-risk segments.
Personalized Action: Generic retention campaigns are replaced by AI-tailored offers driven by actual churn factors.
Operational Agility: The Streamlit dashboard turns complex algorithmic outputs into a daily, usable business tool.
Background
For SaaS and subscription providers, losing users after just a few months drastically undercuts profitability. Retaining an existing customer is always cheaper than acquiring a new one, but most businesses rely on reactive measures—trying to save the customer only after they are already out the door.
Key Challenges:
Customer Success teams lack visibility into which specific users are most likely to drop off.
Data sits in silos, making it difficult to understand the root drivers behind cancellations.
Marketing teams waste resources on generic retention campaigns instead of personalized, targeted interventions.