Understand the growth
behind the numbers.
Explore synthetic acquisition cohorts, expected retention, recurring revenue and unit economics in one transparent workspace.
Recurring revenue trajectory
Expected month-end MRR across synthetic acquisition cohorts
Modeled MRR · 12-month view
Each bar includes retained revenue from eligible cohorts
Portfolio segments
Modeled share of current monthly recurring revenue
Illustrative results depend on synthetic churn, expansion and pricing assumptions.
Retention by acquisition cohort
How expected customer survival changes by months since acquisition. Each column represents elapsed cohort age, not a calendar month.
Expected customer retention
Percent of starting customers remaining; assumptions are deliberately simplified
Values are modeled cohort survival percentages, not observed customer-level retention or independently verified rates. Blank cells indicate unobserved future ages.
Retention diagnostics
Expected survival of cohorts old enough to reach each age
Acquisition-weighted expected survival
Comparable eligible cohorts only
Comparable eligible cohorts only
Cohort health notes
These are heuristic signals, not statistical conclusions
Scenario laboratory
Switch conditions and adjust assumptions to see how expected recurring revenue and unit economics respond.
Adjust the model
Changes immediately flow into the scenario comparison
Business assumptions
Local-only sandbox parameters
Retention and expansion shifts are in percentage points, not relative percentages. Only modeled expected values change; your original cohort inputs remain intact.
Selected scenario outcome
October 2026 · modeled results
Side-by-side scenario comparison
Same cohort inputs and slider settings, with each preset evaluated independently
| Scenario | Expected MRR | Active customers | Month-1 retention | LTV / CAC |
|---|
Acquisition cohort registry
Edit synthetic starting inputs or create your own cohorts. Archived entries stay recoverable and are excluded from calculations.
Active cohort inputs
0 active cohorts
| Acquired | Segment | Customers | ARPU / mo | Churn / mo | ARPU growth | CAC | Actions |
|---|
Archived cohorts
Historical inputs can be restored without deleting information
Data & methodology
Inspect assumptions, export your workspace and restore a validated backup. No external API or data source is required.
Data ownership & backups
Everything runs in your browser
State is kept in browser localStorage where permitted. No accounts, requests, telemetry, cookies, or third-party model inference. Export a JSON backup if you want to retain changes beyond this browser.
Demo workspace
Transparent example, not real customer data
Twelve fabricated monthly acquisition cohorts, fictional segments, starting customer counts, ARPU, monthly churn, expansion and acquisition costs are provided to demonstrate mechanics. The reporting month is fixed at October 2026 for reproducibility.
Reset affects CohortPilot only. Export your workspace before resetting if you want a copy.
Model specification and limits
Definitions, formulas and interpretation
Expected cohort survival
For cohort i at integer age a ≥ 0, expected retained customers are acquired_i × acquisition_factor × (1 − monthly_churn_i)^a. Churn is a constant hazard within each scenario. Fractional customer values are expectations, never observed headcounts.
Expected monthly recurring revenue
Expected cohort revenue at age a is retained_customers_i(a) × ARPU_i × (1 + monthly_ARPU_growth_i)^a. Expected month-end MRR is the sum for cohorts already acquired by that month; illustrative annualized run rate is 12 × MRR. The model excludes refunds, taxes, billing timing, reactivations, cross-cohort transfers and contract commitments.
Retention heatmap
Cell at age a equals 100 × (1 − adjusted_churn_i)^a. Future ages are blank. Aggregate retention is customer-count-weighted across cohorts old enough to reach that age, which changes the cohort composition by age. Direct across-age comparisons can therefore be affected by selection differences.
Unit economics
Simplified gross-profit lifetime value is ARPU × gross_margin / monthly_churn, ignoring expansion and discounting. When monthly churn is zero, LTV is undefined/infinite and displayed as ∞. CAC payback is CAC / (ARPU × gross_margin). Portfolio ratios are weighted by expected newly acquired customer counts. This is a simplified constant-hazard proxy, not recognized accounting or a validated customer LTV forecast.
Scenario transformations
Selected preset changes monthly churn by −2 pp or +3 pp, or monthly ARPU growth by +1.5 pp; sliders contribute additional percentage-point shifts. Churn is clamped into [0, 0.95] and ARPU growth into [−0.20, 0.30]. Acquisition scale is between 50% and 150%; the gross margin assumption is 30% to 95%. The models are deterministic, not predictions or Monte Carlo confidence intervals.
Scope and limitations
Dashboard revenue can change with modeled survival and ARPU growth. Historical onboarding or churn changes apply to all modeled cohort ages rather than being applied at a particular real-world intervention date. The model does not estimate causal effects, seasonality, observed confidence intervals, gross additions vs. churn accounting, or an audited subscription ledger.
Integrity disclosure: All starting data are fabricated. No real customer data, market benchmarks, real-time analytics, AI inference, statistical significance claims, or investment outcomes are supplied. Use verified first-party data and qualified financial/analytics review for operational decisions.