/commercial-forecaster

name: commercial-forecaster
description: "Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist)."
version: 2.8.0
author: claude-code-skills
license: MIT
tags: [commercial, forecasting, bookings, arr, nrr, grr, cohort, funnel, pipeline-math]
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]

commercial-forecaster

Purpose

Help Commercial leaders answer three questions at the forecast moment:

1. **What's the commit / best-case / pipe-only number?** (3-tier bookings forecast with disclosed assumptions) 2. **Which cohorts are leaking, and is the consolidated NRR hiding the leak?** (per-cohort NRR/GRR projection over horizon) 3. **Which funnel stages are reliable, and which are statistical noise?** (per-stage coefficient-of-variation confidence band)

The skill recommends **three forecast numbers + an explicit assumption block**. The CRO presents the number, the board sees the assumptions, the theatre dies.

When to use

• Building the quarterly bookings forecast for the board
• Preparing the QBR forecast where the CFO will ask "what's the commit, what's the best-case, what's the pipe-only"
• Projecting ARR for next 4-8 quarters using cohort retention data
• Suspecting a consolidated NRR number is hiding a leaky recent cohort
• Pipeline-coverage is shrinking and you need to know which stages are still trustworthy
• You're being asked for a "single number" and you need the structured answer that surfaces the assumption

**Do not use for:**

• Backward-looking financial close + reporting → `finance/financial-analysis`
• Strategic financial planning (multi-year, scenario, fundraise) → `c-level-advisor/cfo-advisor`
• "Should we hire a VP Sales?" / territory design / comp plan → `c-level-advisor/cro-advisor`
• Setting prices → sibling `pricing-strategist` (projects revenue *at* prices already set)
• Per-deal discount approval → sibling `deal-desk`

Workflow

Step 1 — Intake pipeline + cohort + historical conversion data

Fill `assets/forecast_intake_template.md` (≈ 20 min). Captures: opportunity list with stage/amount/close-date/age/last-activity; historical stage-to-stage conversion across last 4Q and last 12Q; per-cohort ARR + per-quarter retention + expansion data; funnel stage names with 12-quarter conversion history.

Step 2 — Run 3-tier bookings forecast

scripts/bookings_forecaster.py --input intake.json --profile saas --output markdown

Outputs three numbers — **commit**, **best-case**, **pipe-only** — each with the conversion rate applied, the data window used (last-4Q vs. last-12Q weighted 70/30), and the time-to-close probability adjustment. Surfaces variance between commit and pipe-only as the pipeline-risk indicator.

**The assumption block is non-optional.** If you remove it, the forecast becomes theatre.

Step 3 — Project cohort-level ARR

scripts/cohort_arr_projector.py --input intake.json --output markdown

Computes per-cohort NRR + GRR over the projection horizon. Flags any cohort whose NRR is declining vs. the trailing-cohort average — these are the leaky cohorts that the consolidated number will hide for 2-3 quarters before the leak surfaces in the topline.

Output includes the consolidated NRR/GRR trajectory + the cohort heatmap + a leaky-cohort callout.

Step 4 — Score per-stage funnel confidence

scripts/funnel_confidence_scorer.py --input intake.json --output markdown

Per stage: mean conversion %, standard deviation, coefficient of variation (CoV = StDev / Mean), confidence band (HIGH < 10%, MEDIUM 10-25%, LOW 25-50%, VERY LOW > 50%). Recommends treatment per stage: extend-data-window, treat-as-soft-floor, or commit-quality.

Step 5 — Assemble the forecast deck

Take the 3-tier bookings number + cohort heatmap + funnel confidence into the QBR / board deck. **The assumption block goes on the slide with the number.** If the slide has a single number and no assumption block, the slide is theatre.

Scripts

• `scripts/bookings_forecaster.py` — 3-tier bookings forecast (commit / best-case / pipe-only) with disclosed conversion-rate + data-window + weighting block
• `scripts/cohort_arr_projector.py` — per-cohort NRR/GRR projection over horizon with leaky-cohort callout
• `scripts/funnel_confidence_scorer.py` — per-stage CoV-based confidence bands with treatment recommendation

All scripts: stdlib only. `--help` and `--sample` work on all three.

References

• `references/saas_forecasting_canon.md` — Skok, Tunguz, OpenView, BVP, Pacific Crest/KeyBanc, ProfitWell, Patrick Campbell
• `references/cohort_analysis_canon.md` — Andrew Chen (a16z), Brian Balfour, Skok, Ramanujam, OpenView, Lenny Rachitsky, Reforge
• `references/forecast_anti_patterns.md` — McKinsey, Tunguz, OpenView, MIT Sloan, Bain, Forrester, Pacific Crest

Assumptions

• **Historical conversion is the prior, not the truth.** Last 4Q is weighted 70%, last 12Q is weighted 30%. The blend captures regime change (recent slowdown) without overfitting to a single bad quarter. Window + weighting are surfaced in every output.
• **A forecast without