SaaS Churn Forecast

A SaaS churn forecast projects customer count and revenue for a subscription business month by month, starting from a customer base, an average revenue per user, a monthly churn rate, and a monthly growth rate. Each period the model removes customers lost to churn, adds new customers from growth, and, when configured, returns a share of previously churned customers through reactivation and adds expansion revenue, producing a month-by-month trajectory rather than a single end-of-year number.

• Starting Customers is the active subscriber base at month zero, the figure the whole forecast compounds from.
• Average Revenue per User (ARPU) is the monthly subscription price or average monthly spend per customer, in dollars. Fractions (such as 49.99 or 99 1/2) and formatted numbers (such as 1,200) are accepted.
• Monthly Churn Rate is the percentage of customers lost each month: losing 5 out of every 100 customers is a churn rate of 5. Industry benchmarks range from 2 percent for enterprise SaaS to 8 percent or more for consumer subscriptions.
• Monthly New Customer Growth Rate is new customers added each month as a percentage of the current base: an 8 percent growth rate on 1,000 customers adds 80 new customers in the first month.
• Forecast Horizon sets how many months the projection runs. Typical planning windows are 12, 24, or 36 months.

Advanced settings add three refinements: expansion revenue per customer for upsell and cross-sell, a reactivation rate that returns a portion of churned customers each month, and a churn trend that models retention improving or worsening over the horizon. A step-by-step formula view shows the derivation behind each period's numbers.

Each period, the model carries forward the prior month's customer count, subtracts customers lost to churn, adds customers gained from growth, and, when reactivation is set, adds back a share of the customers who churned earlier. Revenue for the period is that customer count multiplied by ARPU, plus any expansion revenue per customer. Because both churn and growth compound on a moving base rather than the original one, small differences in the monthly rates separate into large gaps in customer count and revenue by month 24 or 36. A churn rate that looks small in isolation compounds fast: at 5 percent a month, an unreplaced customer base is down to roughly 54 percent of its starting size after 12 months.

Churn forecasting is essential for any subscription or recurring revenue business. Understanding how retention rates translate into long-term revenue helps founders, finance teams, and investors make informed decisions about growth investment, pricing changes, and customer success initiatives.

• Startup Planning: Model different churn scenarios when preparing investor pitch decks. Compare an optimistic 2 percent monthly churn against a conservative 7 percent to show the revenue spread over 24 months and justify customer success investment.
• Board Reporting: Generate quarterly forecasts showing projected MRR and customer count trajectories. Present best-case and worst-case scenarios by adjusting the churn trend parameter to model improving or deteriorating retention.
• Pricing Strategy: Evaluate the revenue impact of changing ARPU. Compare a lower price point with potentially reduced churn against a higher price point with higher churn to find the optimal balance.
• Customer Success ROI: Calculate the revenue saved by reducing churn from 6 percent to 4 percent monthly. Use the side-by-side comparison to justify investment in onboarding, support, and engagement programs.
• Expansion Revenue Modeling: Use the expansion revenue setting to model upsell and cross-sell contributions. Even small per-customer expansion amounts compound across a growing base over 12 to 36 months.
• Reactivation Campaigns: Estimate the impact of win-back campaigns by setting a reactivation rate. Model scenarios where 2 to 5 percent of churned customers return each month through targeted outreach.
• Investor Due Diligence: Validate the growth assumptions in a SaaS company financial model. Enter reported metrics and compare forecast output against claimed projections to identify overly optimistic assumptions.