Bookings Forecast

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.