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Forecast revenue or growth from historical data

Explicit assumptions matter more than the number — lock the scenario logic first.

by @whitney · recommended tool Claude Co-Work · 0 unlocks

You'll need

CLAUDE SKILLS

xlsx

EXAMPLES · WHAT THIS PROMPT PRODUCES

Sample output

**MRR Forecast — next 12 months (Loomly-style SMB SaaS)**

**Inputs:** 24 months history, current MRR $182K, avg net new $9.4K/mo, 4.2% gross churn, Q4 seasonal lift (~1.3x), planned: annual-plan push (Q2) + partner channel (Q3).

**Method:** Three scenarios, explicit assumptions, applied to a month-by-month build (cells + formulas in the attached model).

| Scenario | Assumptions | MRR @ Mo 12 | ARR |
|---|---|---|---|
| **Best** | Net new +25%, churn → 3.2%, partner adds 8% | **$312K** | $3.74M |
| **Likely** | Net new flat, churn 4.0%, partner adds 4% | **$268K** | $3.22M |
| **Worst** | Net new −10%, churn 5.0%, partner stalls | **$224K** | $2.69M |

**Model logic:** `MRR_n = MRR_(n-1) × (1 − churn) + net_new × seasonal_factor + partner_contribution`. Q4 months carry the 1.3x seasonal multiplier.

**Sensitivity (key drivers)**
- **Churn:** every +1pt of monthly churn cuts Mo-12 MRR by ~$31K.
- **Net-new growth:** ±10% swings ARR by ~$430K.
- **Partner channel:** the single biggest upside lever, but also the least proven assumption — treat the "best" case as contingent on it.

**Takeaway:** Lock churn-reduction first; it moves the forecast more reliably than chasing net-new.

PRICE HISTORY0 cr · current

PROMPT TEMPLATE · v1.0.0

Forecast <METRIC> for next <N> months.
Inputs: historical monthly data, seasonality notes, upcoming initiatives.
Method: show 3 scenarios (best/likely/worst) with explicit assumptions.
Deliver: the model (cells + formulas), a chart, and a sensitivity analysis on key assumptions.