SaaS Churn Analyzer
PROPredict customer churn, build health scores, segment at-risk accounts, and design win-back campaigns using ML-powered analysis for SaaS businesses.
Example Usage
We have 12 months of customer data including login frequency, feature usage, support tickets, NPS scores, and contract details. Monthly churn is 4%. Build a churn prediction model that identifies accounts with >70% probability of cancelling in the next 90 days. Explain the top 5 factors driving churn risk using SHAP values. Output a ranked list of top 100 at-risk accounts with churn probability, risk drivers, and recommended retention actions.
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| Probability threshold above which customers are flagged as at-risk (0.5-0.8 range) | 0.65 | |
| Days without login/activity to trigger at-risk flag (7-60 based on product usage frequency) | 30 | |
| Scoring methodology: weighted_aggregate, rules_based, or ml_model | weighted_aggregate | |
| Cadence for at-risk customer identification (daily, weekly, monthly) | weekly | |
| Incentive magnitude for win-back campaigns (0.10-0.30 range) | 0.20 | |
| Minimum core features a customer should use within 90 days to be considered engaged | 3 |
Research Sources
This skill was built using research from these authoritative sources:
- Customer Churn Analysis and Risk Prediction in E-Commerce Comprehensive overview of ML-based churn prediction techniques, model evaluation, and business impact analysis
- Product Adoption and Customer Churn: A Data-Driven Analysis B2B SaaS-specific research on relationship between product adoption and churn
- Machine Learning Models for Customer Churn Prediction Comparison of logistic regression, random forests, XGBoost, and deep learning for churn prediction
- Advancements in Machine Learning for Customer Retention Systematic literature review of 112 peer-reviewed studies on ML-based retention
- What is a Customer Health Score in SaaS Guide to defining health scores including methodologies, weighting, and automation
- RFM Model for Customer Churn Analysis RFM-based churn system with K-means segmentation and XGBoost achieving 81% accuracy
- How to Identify and Prevent Churn Risk Factors in SaaS Practical guide covering NPS analysis, behavior patterns, and proactive monitoring
- Mastering Customer Winback Strategies Six proven win-back methods including personalized emails, incentives, and retargeting
- Best Strategies to Identify Churn Risk Factors in SaaS Research from 40+ SaaS companies on churn risk identification strategies
- Reactivated Users Guide for SaaS Companies Comprehensive guide on reactivation campaigns, personalization, and multi-channel outreach