Subscription MRR trajectory.

This tool forecasts subscription business MRR over time using growth rate, churn rate, and subscriber price. Each month the base grows by (growth % - churn %) net rate. Positive net growth compounds; negative net growth shrinks. The math reveals why getting churn below growth is the single biggest lever for sustainability.

1,000 subscribers at 20/month with 10% monthly growth and 5% monthly churn = 5% net growth. Over 12 months: subscribers rise to about 1,796. MRR grows from 20,000 to 35,920. That 5% net monthly compound adds up to 80% annual growth - the power of compounding.

Lower churn beats higher growth for long-term value. A business at 8% growth / 3% churn (5% net) eventually outperforms one at 12% growth / 8% churn (4% net) because the lower-churn base retains more customers over time. Churn reduction compounds in both directions: every 1% reduction in monthly churn adds roughly 4 months to average customer lifetime.

A worked example

Try the defaults: current subscribers of 1,000, avg subscription price of 20, monthly growth of 10%, monthly churn of 5%. The tool returns 35,917.13. You can adjust any input and the result updates as you type — no submit button, no reload. That's the real power here: seeing how sensitive the output is to one or two assumptions.

What moves the number most

The result responds to Current Subscribers, Avg Subscription Price, Monthly Growth %, Monthly Churn %, and Forecast Months. Frequency and unit price pull the total in different directions. The biggest surprise for most people is how small recurring amounts compound into large annual figures — that's where this calculation earns its keep.

The formula behind this

Each month: subscribers = subscribers × (1 + growth % - churn %). Future MRR = future subscribers × price. Everything the calculator does is shown in the formula box below, so you can check the math against your own spreadsheet if you want.

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.