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Structured methodology As of 2026-04-24

How Churn & Retention Calculator works

What the tool assumes, what data it pulls from, and what it cannot tell you.

Education · General business information, not legal, tax, or financial advice. Editorial standards Sponsor disclosure Corrections

1. Scope

Estimates recovered customers and revenue lift when monthly churn improves. It illustrates the sensitivity of LTV to churn; it is not a retention-programme design tool.

2. Inputs and outputs

Inputs

  • active_customers number default: 1200
  • monthly_churn_percent percent (monthly) default: 4
  • retention_lift_percent percentage points default: 1.5

    How many points you shave off monthly churn.

  • arpu_monthly number (currency/mo) default: 129
  • horizon_months number default: 12

    1–60.

Outputs

  • recoveredCustomers

    improvedEndingCustomers − baseEndingCustomers at the horizon (primary value).

  • improvedChurnPercent

    max(0, monthly_churn_percent − retention_lift_percent).

  • baseEndingCustomers

    Customers surviving at the horizon under the current churn.

  • improvedEndingCustomers

    Customers surviving at the horizon under the improved churn.

  • cumulativeRevenueLift

    Sum over the horizon of (improved − base) customers × arpu_monthly.

Engine source: src/lib/churn-retention-calculator/engine.ts

3. Formula / scoring logic

improved_churn = max(0, monthly_churn_percent - retention_lift_percent)
for m = 1..horizon:
  base_customers     *= 1 - monthly_churn_percent / 100
  improved_customers *= 1 - improved_churn / 100
  cumulative_revenue_lift += (improved_customers - base_customers) * arpu_monthly
recovered_customers = improved_customers - base_customers

4. Assumptions

  • Churn compounds monthly (memoryless exponential decay) rather than the linear ×12 approximation. Real SaaS retention curves often have a longer tail.
  • ARPU is flat; no expansion-revenue tailwind.
  • The recovered-customer figure is a survival difference at the horizon, not a behavioural projection.

5. Data sources

6. Known limitations

  • The widely-cited Reichheld "5% retention lift = 25–95% profit lift" claim is context-dependent and not peer-reviewed. We do not use it as a benchmark. Consult the underlying Harvard Business School working paper directly if needed.
  • Treats logo churn and revenue churn as equivalent; they diverge for products with tiered pricing.

7. Reproducibility

Input
active_customers = 1000, monthly_churn_percent = 6, retention_lift_percent = 2, arpu_monthly = $50, horizon_months = 12.

Expected output
improvedChurnPercent = 4, baseEndingCustomers ≈ 475.92, improvedEndingCustomers ≈ 612.71, recoveredCustomers ≈ 136.79, cumulativeRevenueLift ≈ $54,219.20.

8. Change log

  • 2026-04-24 methodology page first published.

Worked example

Run live against the same engine this site ships (/engines/churn-retention-calculator.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool
churn_retention
active_customers
1200
monthly_churn_percent
4
retention_lift_percent
1.5
arpu_monthly
129
horizon_months
12

Output

primaryLabel
Recovered customers at horizon
primaryValue
150.35
primaryFormat
number
summary
Retention lift compounds monthly, increasing both active customers and revenue.
metrics[0].label
Improved churn rate
metrics[0].value
2.5
metrics[0].format
percent
metrics[1].label
Base ending customers
metrics[1].value
735.25
metrics[1].format
number
metrics[2].label
Improved ending customers
metrics[2].value
885.6
metrics[2].format
number
metrics[3].label
Cumulative revenue lift
metrics[3].value
142895.68
metrics[3].format
currency
assumptionsEcho.active_customers
1200
assumptionsEcho.monthly_churn_percent
4
assumptionsEcho.retention_lift_percent
1.5
assumptionsEcho.arpu_monthly
129
assumptionsEcho.horizon_months
12

Frequently asked questions

What does the Churn & Retention Calculator calculate?
Estimates recovered customers and revenue lift when monthly churn improves. It illustrates the sensitivity of LTV to churn; it is not a retention-programme design tool.
What inputs does the Churn & Retention Calculator need?
It takes 5 inputs: active_customers (default 1200), monthly_churn_percent (default 4), retention_lift_percent (default 1.5), arpu_monthly (default 129), horizon_months (default 12). Outputs returned: recoveredCustomers, improvedChurnPercent, baseEndingCustomers, improvedEndingCustomers, cumulativeRevenueLift.
What formula does the Churn & Retention Calculator use?
The exact computation is: improved_churn = max(0, monthly_churn_percent - retention_lift_percent); for m = 1..horizon:; base_customers *= 1 - monthly_churn_percent / 100; improved_customers *= 1 - improved_churn / 100; cumulative_revenue_lift += (improved_customers - base_customers) * arpu_monthly; recovered_customers = improved_customers - base_customers
Can I verify the Churn & Retention Calculator with a worked example?
Yes. With active_customers = 1000, monthly_churn_percent = 6, retention_lift_percent = 2, arpu_monthly = $50, horizon_months = 12. the tool returns improvedChurnPercent = 4, baseEndingCustomers ≈ 475.92, improvedEndingCustomers ≈ 612.71, recoveredCustomers ≈ 136.79, cumulativeRevenueLift ≈ $54,219.20.
Where does the Churn & Retention Calculator get its benchmark data?
Reference data is sourced from: OpenView SaaS Benchmarks 2024 (churn percentiles) (as of 2024); Paddle SaaS Benchmarks 2024 (as of 2024).
What can the Churn & Retention Calculator not tell me?
Known limitations: The widely-cited Reichheld "5% retention lift = 25–95% profit lift" claim is context-dependent and not peer-reviewed. We do not use it as a benchmark. Consult the underlying Harvard Business School working paper directly if needed. Treats logo churn and revenue churn as equivalent; they diverge for products with tiered pricing.