/commercial-forecaster

name: commercial-forecaster
description: "Use when building a bookings, ARR, pipeline or NRR forecast, or a commit, best-case and pipeline board number. Forecasts with bookings_forecaster.py, projects cohorts with cohort_arr_projector.py and scores stage confidence with funnel_confidence_scorer.py, showing every conversion assumption."
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  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 a disclosed assumption block is theatre.** This is the skill's hard rule. The CLI refuses to omit the assumption block.
• **Cohort decomposition reveals leaks 2-3 quarters before the consolidated number does.** Reporting NRR without per-cohort breakdown hides the leak.
• **CoV (coefficient of variation) is the right discipline for stage confidence.** A stage with mean conversion 40% and stdev 4% (CoV 10%) is HIGH confidence; mean 40% stdev 20% (CoV 50%) is VERY LOW. The same average masks very different reliability.
• **Industry profile tunes priors, not truth.** Profile shifts default stage-conversion rates by industry; y