Beyond Prediction:
The Transition to
Prescriptive Intelligence
Leverage a platform engineered for the final stage of analytics maturity. By integrating predictive modeling with contextual action benchmarking, RetentionLens moves beyond diagnostics to provide specific, AI-driven recommendations that systematically navigate and improve revenue opportunities.
No registration required for initial evaluation • Built for SaaS teams that take retention seriously
Retention analytics for SaaS revenue intelligence
Modern SaaS businesses generate vast quantities of behavioral data through customer interactions, subscription events, and billing signals. RetentionLens transforms this raw data into retention curves, cohort trends, and actionable insights you can validate and act on quickly.
The foundation of our analytics today is survival analysis (Kaplan-Meier) plus cohort retention and hazard rates. More advanced forecasting and segmentation models are under active development as we expand the model stack.
Churn insights are derived from survival curves, hazard rates, and billing event sequences. When data volume supports it, we validate and calibrate risk scoring on your own historical churn. The goal is transparent, explainable signals—not magic accuracy claims.
Revenue analytics summarizes historical trends (GRR/NRR, expansion/contraction) and supports lightweight projections. Statistical time-series forecasting (ETS/ARIMA) is under active development in the Python ML service.
When revenue shifts, the dashboard helps you slice by cohorts and segments to pinpoint where it happened (logo churn vs expansion vs contraction). Advanced clustering-based segmentation is under active development.
Advanced Predictive Modeling Framework
Four core methodologies that transform raw behavioral data into actionable revenue intelligence.
Survival Analysis & Churn Prediction
Uses Kaplan-Meier survival analysis to model retention and derive hazard rates from lifecycle and billing events. Churn risk scoring is available as early signals; more advanced models (including Cox regression) are under active development and will be validated on your own data as volume grows.
Time-Series Revenue Forecasting
Tracks revenue dynamics (NRR/GRR, expansion, contraction, logo churn) and supports lightweight projections for planning. Statistical forecasting with confidence intervals (ETS/ARIMA) is under active development.
Intervention Prioritization
Ranks at-risk accounts by expected intervention value using a decision engine that weighs estimated churn probability, gross profit at risk, intervention cost, and confidence level. Each recommendation includes a cost-benefit breakdown so you know exactly why an action is suggested — and what it is expected to be worth. Causal uplift estimation is under active development.
Advanced Cohort Behavioral Analysis
Implements sophisticated cohort segmentation using multidimensional clustering algorithms to reveal hidden user behavior patterns. Analyzes customer journey progression, feature adoption rates, and engagement evolution across time-based cohorts to identify optimal onboarding sequences, expansion opportunities, and retention strategies tailored to specific user segments.
Why retention analytics pays for itself
The research on retention economics is consistent: small improvements in churn compound dramatically over time.
Retention improvement
A 5% increase in customer retention can boost profitability by 25–95%, according to research by Reichheld & Sasser (1990) and subsequent studies — by reducing CAC amortization and increasing expansion revenue per customer.
Cost to acquire vs. retain
Acquiring a new customer costs 6–7× more than retaining an existing one. For SaaS companies with high CAC, churn is not a support problem — it is a unit economics problem that compounds with scale.
The metric investors actually look at
Net Revenue Retention above 100% means your existing customer base grows without a single new sale. Understanding the cohort dynamics behind your NRR is the starting point for improving it.