Commercial Forecaster

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; your historical data overrides.
• The skill emits three numbers and an assumption block. The CRO picks the commit number, owns the trade-off, and walks the board through the variance.

Anti-patterns

• Single-number forecast with no confidence band. The board asks for "the number"; the discipline is to present three with named assumptions. See forecast_anti_patterns.md.
• Using last-12-quarter conversion blindly. Hides recent slowdown. The 70/30 blend on last-4Q vs. last-12Q corrects this.
• Reporting NRR without cohort decomposition. The consolidated number can be flat while a recent cohort is leaking 15 pp; the leak surfaces in the topline 2-3 quarters later. Always decompose.
• Treating best-case as commit. The CFO will eat you. Best-case includes weighted-stage opps that have a < 50% time-to-close probability; commit only includes commit-grade stages.
• Hiding the assumption block. The skill refuses; if you remove it manually, you own the theatre.
• No leaky-cohort callout. If cohort_arr_projector.py flags a cohort and you suppress the flag in the deck, the leak owns you next quarter.
• Ignoring late-stage opp age. A "verbal" deal that's been verbal for 180 days is not a commit. The bookings forecaster downweights stalled opps automatically; do not re-up them by hand.
• No pipeline-coverage check. Industry rule of thumb: forecast > pipeline ÷ 3 is anti-pattern. The tool surfaces the ratio; respect it.

Distinct from

• finance/financial-analysis — backward-looking financial close, GAAP/IFRS reporting, variance vs. budget. commercial-forecaster is forward-looking pipeline math.
• c-level-advisor/cfo-advisor — strategic multi-year financial planning, fundraise scenarios, runway. commercial-forecaster is one input to the CFO, not the strategy.
• c-level-advisor/cro-advisor — strategic CRO judgment: "do we hire a VP Sales?", territory design, comp plan, when to add a sales engineer. commercial-forecaster is the math the CRO uses; cro-advisor is the judgment the CRO applies.
• sibling pricing-strategist — sets the price (model + range). commercial-forecaster projects revenue at those prices. Pricing comes first; forecast comes after.
• sibling deal-desk — per-deal scoring + discount approval routing. commercial-forecaster aggregates the pipeline that deal-desk operates on day-by-day.

Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by /cs:grill-commercial or the orchestrator. Recommended answer + canon citation per question. Never bundled.

1. "What conversion rate are you using, and is it last-4Q or last-12Q?" Recommended: a 70/30 blend (last-4Q weighted 70%, last-12Q weighted 30%). Last-12Q alone hides recent slowdown; last-4Q alone overfits one bad quarter. Canon: Tomasz Tunguz (Theory Ventures) — forecasting studies show single-window conversion estimates miss regime change at ~3-quarter lag.

2. "What's your pipeline coverage ratio, and is your commit above pipeline ÷ 3?" Recommended: 3x coverage is the SaaS-industry floor; below 3x means your commit is structurally unsupported. Canon: Pacific Crest / KeyBanc SaaS Survey — top-quartile SaaS companies maintain 3.0-4.5x pipeline coverage against committed bookings.

3. "Can you show me NRR by cohort, not just consolidated?" Recommended: never report a consolidated NRR without the per-cohort breakdown. Leaky cohorts hide in averages. Canon: Patrick Campbell (ProfitWell) + David Skok — cohort-driven retention decomposition surfaces leaks 2-3 quarters before consolidated NRR moves.

4. "What's the variance (CoV) on each stage's conversion rate over the last 12 quarters?" Recommended: CoV < 10% → commit-grade; 10-25% → moderate; 25-50% → soft floor only; > 50% → do not use this stage for forecasting. Canon: MIT Sloan forecasting research / Hyndman & Athanasopoulos (Forecasting: Principles and Practice) — CoV on the input series predicts forecast accuracy more reliably than mean.

5. "How long has each late-stage opp been in late-stage?" Recommended: stage-age > 2x the median stage-duration → treat as stalled, exclude from commit, keep in pipe-only. Canon: David Skok (For Entrepreneurs) — stalled-opp identification by stage-age is the #1 forecast hygiene practice in top-decile SaaS pipelines.

6. "Is your best-case forecast within 30% of your pipe-only?" Recommended: if best-case is < 50% of pipe-only, your stage-conversion assumptions are pessimistic and you're sandbagging; if best-case > 80% of pipe-only, you're hockey-sticking. Canon: McKinsey research on forecast bias + OpenView SaaS benchmarks — most teams operate in one of two failure modes: sandbagging (commit << earnings) or hockey-sticking (commit >> earnings).

7. "What assumption block accompanies the number on the board slide?" Recommended: every forecast number on a board slide names (a) the conversion rate, (b) the data window, (c) the weighting choice, (d) the pipeline-coverage ratio. No assumption block = the slide is theatre. Canon: Bain & Company commercial-forecasting practice + Forrester pipeline-coverage research — undisclosed-assumption forecasts have 2.3x higher variance against actuals than disclosed-assumption forecasts.

Walk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke bookings_forecaster.py → cohort_arr_projector.py → funnel_confidence_scorer.py in sequence.

Activation Triggers

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).