Identify at-risk customers before they leave, understand why they churn, and deploy targeted retention strategies powered by machine learning and behavioral analytics.
Losing a customer means losing all future revenue they would have generated. For subscription businesses, 5% annual churn compounds to 40% customer loss over 5 years.
Replacing churned customers requires spending 5-25x more than retaining existing ones. High churn forces continuous expensive customer acquisition to maintain growth.
Without churn prediction, retention efforts are too late - most customers have already decided to leave before you notice declining engagement.
Without data analysis, you don't know why customers leave - product issues, pricing, service quality, or competition - making prevention impossible.
Our machine learning platform analyzes customer behavior, engagement patterns, and historical churn to predict at-risk customers weeks before they leave, enabling proactive retention.
ML models analyze hundreds of customer signals - product usage, support interactions, payment behavior, feature adoption, login frequency - to calculate churn probability for each customer. Models update daily, providing real-time risk scores.
Beyond predicting who will churn, our platform explains why - which behaviors and characteristics indicate churn risk. SHAP values and feature importance rankings identify the specific reasons each customer is at risk, enabling targeted interventions.
Churn predictions trigger automated retention campaigns via email, in-app messaging, or CSM outreach. Personalized interventions based on churn reason - product training for low engagement, discount offers for price-sensitive customers, premium support for frustrated users.
Ready to reduce churn and increase customer lifetime value? Our platform predicts churn with 85%+ accuracy and enables retention at scale.
Customer churn is the percentage of customers who stop using your product over a time period. For subscription businesses, churn directly impacts MRR/ARR and company valuation. Key metrics: Monthly churn rate (% customers lost per month), Annual churn rate (compounds monthly churn), Revenue churn (accounts for customer value differences), Net revenue retention (expansion minus churn).
Churn economics: If CAC (customer acquisition cost) is €500 and monthly ARPU is €50, you need 10 months to recover acquisition cost. A customer churning at month 8 means you lost €100 on that customer. If 20% of customers churn before breakeven, you're losing money on acquisition. Conversely, reducing churn from 5% to 3% monthly increases customer lifetime from 20 to 33 months - a 65% increase in LTV. This is why churn reduction is the highest ROI growth lever for most subscription businesses.
Most common approach: predict whether customer will churn in next 30/60/90 days (yes/no). Train on historical data with features from product usage, billing, support. Algorithms: Random Forest (interpretable, handles categorical data), XGBoost (highest accuracy, robust to imbalanced data), Neural Networks (best for complex patterns with large datasets). Output: churn probability score 0-100% for each customer. Typical accuracy: 80-90% AUC-ROC with quality features.
Predict not just if customer will churn but when. Cox proportional hazards, Accelerated Failure Time models, or deep learning survival models (DeepSurv). Advantages: handles censored data (current customers haven't churned yet), provides time-to-churn estimates for prioritization, models how churn risk changes over customer lifecycle. Use cases: prioritize high-value customers likely to churn soon over low-value customers churning in 6 months.
LSTMs and RNNs analyze sequences of customer actions over time. Detect declining engagement patterns that precede churn - login frequency dropping, feature usage decreasing, support ticket patterns. Better at early churn detection than static snapshot features. Requires sequence data (daily/weekly activity logs) and more data for training but often achieves superior early warning performance.
Different customer segments have different churn patterns. Build separate models by: acquisition channel (paid vs. organic vs. referral), plan tier (free, basic, premium, enterprise), company size (SMB vs. mid-market vs. enterprise), industry vertical. This improves accuracy and enables segment-specific retention strategies. Hierarchical models can share information across segments while capturing unique patterns.
Feature engineering is 80% of churn prediction success. Essential feature categories:
Key metrics for churn prediction models: Precision (what % of predicted churners actually churn - important for not wasting retention resources), Recall (what % of actual churners were predicted - don't miss high-value customers), AUC-ROC (overall model quality - good models above 0.80), Lift (how much better than random - top decile should have 3-5x churn rate vs. average). Business metrics: retention rate improvement, revenue retained, ROI of retention programs.
A €18M ARR project management SaaS with 2,400 customers was experiencing 6.5% monthly churn (equivalent to 56% annual churn), costing €900K MRR annually. Their customer success team reactively contacted customers only when cancellation notices arrived - too late to save most relationships.
We built a churn prediction system ingesting data from their product database (feature usage, login patterns, collaboration metrics), Zendesk (support tickets), Stripe (billing events), and Salesforce (customer attributes). An XGBoost model predicts 60-day churn probability for each customer, updated daily. SHAP explanations identify specific churn drivers per customer. High-risk customers (over 60% churn probability) trigger automated workflows: low engagement → product training emails; support issues → proactive CSM outreach; billing problems → payment assistance.
Results after 9 months: Monthly churn reduced from 6.5% to 4.4% (32% reduction), equivalent to €175K MRR saved monthly or €2.1M ARR retained annually. Model achieved 87% AUC-ROC with 73% precision and 68% recall. Customer success team efficiency improved 3x - focused on predicted high-risk customers vs. reactive firefighting. NPS increased 12 points as proactive support improved customer experience. Total impact: €2.1M retained revenue plus €600K avoided acquisition cost replacement.
Minimum: 6-12 months of customer history with at least 100-200 churn events for model training. Ideal: 2+ years with 500+ churn events across different customer segments. For newer companies with limited churn history, we can: (1) Start with simpler models using engagement metrics as churn proxies, (2) Leverage industry benchmarks and research, (3) Use transfer learning from similar companies, (4) Begin with rule-based risk scoring while collecting data for ML models. Models improve continuously as more churn data accumulates.
Product usage is the strongest churn predictor, but models work without it using: billing data (payment history, downgrades, support requests), customer attributes (company size, industry, plan), and relationship signals (CSM interactions, NPS scores). However, we strongly recommend instrumenting key product actions - it's a one-time engineering investment that dramatically improves prediction accuracy (typically 15-25% AUC improvement). We can help prioritize which events to track based on churn driver analysis.
Valid concern - poorly designed retention outreach can annoy customers and accelerate churn. Our approach: (1) Test retention campaigns on control groups to measure lift vs. harm, (2) Use non-intrusive interventions first (helpful content, product tips), escalating only if needed, (3) Respect customer communication preferences, (4) Focus on adding value (solving their problem) not desperate discounting, (5) Track intervention outcomes and retrain models to learn what works. When done right, retention campaigns increase satisfaction and reduce churn - customers appreciate proactive help.
ML churn prediction requires statistical sample size - difficult with under 500 customers and under 50 churns. For early-stage companies, we recommend: (1) Manual churn analysis to identify patterns and create risk scores, (2) Cohort analysis to understand churn by segment, (3) Leading indicator dashboards (engagement metrics, NPS) for CSM teams, (4) Start collecting data infrastructure now for ML later. Once you reach critical mass (500+ customers, 100+ churns), transition to ML models. The data collection and retention process improvements have value even before ML.
Results vary by baseline churn rate, retention program quality, and product market fit, but typical outcomes: 20-40% churn reduction for companies with reactive or no retention programs, 10-25% improvement for those with existing retention efforts, 5-15% for sophisticated existing programs. The key is combining accurate prediction with effective intervention. Just identifying at-risk customers isn't enough - you need retention playbooks that address churn drivers. We typically see 50-70% of predicted high-risk customers retained with good interventions vs. 20-30% without proactive outreach.
Reduce customer churn and increase lifetime value with AI-powered prediction and retention. Our team will assess your churn data, identify risk factors, and design a custom retention solution.
Based in Lund, Sweden • Serving businesses globally