In subscription businesses, not all customers are created equal—and not all customers behave the same way over time. A cohort is a group of customers who started their subscriptions in the same period (e.g., all customers who signed up in January 2025). Understanding how different cohorts generate revenue over time is critical for forecasting, pricing strategy, and customer lifetime value analysis. But revenue doesn't stay constant; it decays as customers churn, downgrade, or cancel.

Whether you're a SaaS founder analyzing customer retention patterns, a finance professional building revenue forecasts, a product manager understanding customer lifecycle, or a business student learning subscription economics, visualizing cohort revenue decay helps you see the bigger picture. This tool lets you define multiple cohorts with different start periods, initial MRR, and retention rates, then visualize how their revenue decays over time and how they stack together to create total subscription revenue.

The key insight from cohort analysis is that revenue from older cohorts naturally decays due to churn, while new cohorts add fresh revenue. The question is: are you adding new cohorts fast enough to offset the decay of older ones? This visualizer shows you exactly how different retention rates, cohort sizes, and timing affect your total subscription revenue trajectory.

Our Subscription Cohort Revenue Decay Visualizer uses a simple exponential decay model to show how each cohort's revenue decreases over time based on retention percentage. You can define multiple cohorts, see stacked revenue charts showing how cohorts contribute to total revenue, analyze individual decay curves, and calculate lifetime revenue per cohort. This helps you understand the long-term value of different customer acquisition periods and retention strategies.

What is a Subscription Cohort?

A subscription cohort is a group of customers who started their subscriptions in the same time period. Cohorts are typically defined by month or quarter (e.g., "January 2025 cohort" or "Q1 2025 cohort"). By grouping customers this way, you can track how different acquisition periods perform over time and identify trends in customer behavior.

Key Concepts Explained

How Revenue Decay Works

Revenue decay follows an exponential model: each period, the cohort retains a fixed percentage of its previous period's revenue. This creates a smooth decay curve where revenue decreases faster in early periods and gradually slows down.

Key Insight: Small differences in retention rate compound dramatically over time. A 5 percentage point difference (95% vs 90%) results in revenue being cut in half by month 12.

Stacked Revenue: How Cohorts Combine

When you have multiple cohorts, their revenues stack together to create total subscription revenue. New cohorts add fresh revenue while older cohorts decay. The total revenue trajectory depends on:

• Retention rates: Higher retention means older cohorts contribute more revenue for longer.
• Cohort sizes: Larger initial cohorts generate more revenue, but also decay more if retention is low.
• Acquisition timing: Adding new cohorts regularly offsets decay from older cohorts.
• Cohort age distribution: A mix of old and new cohorts creates more stable total revenue than relying on a single cohort.

Lifetime Revenue per Cohort

Lifetime revenue is the total revenue a cohort generates across all simulated periods. It helps you understand which cohorts contribute the most long-term value. Cohorts with higher initial MRR, higher retention, or earlier start periods tend to have higher lifetime revenue.

Follow these steps to visualize subscription cohort revenue decay:

1. Set Time Horizon: Choose how many periods you want to simulate (e.g., 24 months). This determines how far into the future the visualization extends.
2. Add Your First Cohort: Click "Add Cohort" and enter:
   • Start Period: When this cohort begins contributing revenue (e.g., period 0, 3, 6)
   • Initial MRR: Starting monthly recurring revenue from this cohort
   • Retention Rate: Percentage of revenue retained each period (e.g., 95% = 5% churn)
3. Add Additional Cohorts: Add more cohorts to model different customer acquisition periods. Each cohort can have different start periods, initial MRR, and retention rates.
4. Review the Charts: The visualizer displays:
   • Stacked Area Chart: Shows how each cohort contributes to total revenue over time
   • Total Revenue Decay Line: Overall revenue trajectory across all cohorts
   • Individual Cohort Decay Lines: How each cohort's revenue decays independently
5. Analyze Lifetime Revenue: Review the lifetime revenue summary to see which cohorts generate the most total value over the simulation period.
6. Experiment with Scenarios: Try different retention rates, cohort sizes, and timing to see how they affect total revenue and decay patterns.

Revenue Decay Formula

This exponential decay model assumes constant retention rate per period. Each period, the cohort retains a fixed percentage of its previous period's revenue.

Total Revenue Calculation

Cohorts only contribute revenue starting from their start period. Before that, their contribution is zero.

Lifetime Revenue Calculation

Lifetime revenue sums all revenue contributions from a cohort across the entire simulation horizon.

Full Example Calculation

• Assuming Constant Retention: Real-world retention rates often vary by cohort age (e.g., higher churn in months 1-3, then stabilization). This tool uses constant retention for simplicity, but actual retention curves may be more complex (bathtub curves, seasonality).
• Ignoring Expansion Revenue: This model only shows decay. In reality, customers may upgrade, add seats, or purchase add-ons, increasing their MRR over time. The visualization shows a worst-case scenario without expansion.
• Not Accounting for Reactivations: Some churned customers reactivate later. This model treats churn as permanent, which may underestimate lifetime revenue.
• Using Wrong Retention Definition: This tool uses revenue retention (MRR retained), not customer retention (customers retained). If customers downgrade, revenue retention is lower than customer retention.
• Extrapolating Too Far: Exponential decay models become less accurate over very long horizons. Real businesses may have minimum revenue floors, reactivation programs, or retention improvements that change the curve.
• Not Modeling New Cohorts: If you only model existing cohorts without adding new ones, total revenue will always decline. Real businesses continuously acquire new customers, which offsets decay.
• Comparing Cohorts with Different Ages: When comparing lifetime revenue, remember that older cohorts have more periods to contribute. Compare cohorts at the same age for fair comparison.

• Model Best and Worst Case Scenarios: Create multiple scenarios with different retention rates (e.g., 90%, 95%, 97%) to understand the range of possible outcomes. This helps with risk planning.
• Identify Critical Cohorts: Use lifetime revenue to identify which cohorts contribute most value. Focus retention efforts on high-value cohorts or replicate their acquisition channels.
• Plan Cohort Timing Strategically: If you see revenue dips in the visualization, plan new cohort launches before those dips to smooth revenue curves. Regular, consistent acquisition beats irregular bursts.
• Compare Retention vs Acquisition Trade-offs: Model scenarios where you invest in retention (higher retention rates) vs acquisition (more/larger cohorts). See which generates more total revenue over your planning horizon.
• Use for Customer Lifetime Value (LTV) Estimation:Lifetime revenue per cohort divided by initial customer count gives you average LTV per customer for that cohort. Compare LTV across cohorts to identify high-value customer segments.
• Model Seasonal Patterns: If you have seasonal acquisition (e.g., Q4 holiday campaigns), model those cohorts separately to see how seasonal timing affects long-term revenue.
• Combine with CAC Analysis: Compare lifetime revenue to customer acquisition cost (CAC) to calculate LTV:CAC ratios. Cohorts with high lifetime revenue but low CAC are most profitable.

These are general industry guidelines. Your optimal retention depends on your specific business model, market, and customer segments.

Note: Annual retention is calculated as (Monthly Retention)^12. A 95% monthly retention equals 54% annual retention, meaning 46% of customers churn within a year.

• Simplified Exponential Decay: This model assumes constant retention rate per period. Real-world retention often varies by cohort age, season, or customer segment.
• No Expansion Revenue: The model only shows decay. It doesn't account for customers upgrading, adding seats, or purchasing add-ons that increase MRR over time.
• No Reactivations: Churned customers are treated as permanently lost. In reality, some customers reactivate later.
• No Downgrades: The model assumes customers either stay at their current MRR or churn completely. It doesn't model partial downgrades that reduce MRR without full churn.
• Constant Retention: Retention rates are assumed constant across all periods for each cohort. Real retention may improve or worsen over time based on product improvements, market changes, or customer lifecycle stages.
• No Seasonality: The model doesn't account for seasonal variations in retention (e.g., higher churn in January after holiday subscriptions).
• Educational Purpose: This tool provides estimates for learning and planning. Actual subscription performance depends on many factors beyond this simple decay model.

The information in this guide is based on established subscription analytics principles and authoritative sources:

• U.S. Securities and Exchange Commission (SEC) - Subscription metrics disclosure: sec.gov
• Financial Accounting Standards Board (FASB) - Revenue recognition for subscriptions (ASC 606): fasb.org
• U.S. Small Business Administration (SBA) - Subscription business model guidance: sba.gov
• SCORE Association - SaaS financial planning resources: score.org