commercial-forecaster

---
name: commercial-forecaster
description: Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or a commit/best-case/pipe-only board number with disclosed assumptions.
category: Commercial
version: 1.0.0
tools: []
---

# Commercial Forecaster

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 it? (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)

Produce three forecast numbers plus 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 for commit, best-case, and pipe-only separately. Projecting ARR for the 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. Being asked for "a single number" when you need the structured answer that surfaces the assumption.

Not for: backward-looking financial close and reporting; strategic multi-year financial planning or fundraise scenarios; "should we hire a VP Sales?" or territory/comp-plan design; setting prices (this projects revenue *at* prices already set); per-deal discount approval.

## Workflow

**Step 1 — Intake pipeline, cohort, and historical conversion data.** Opportunity list with stage/amount/close-date/age/last-activity; historical stage-to-stage conversion across last 4Q and last 12Q; per-cohort ARR plus per-quarter retention and expansion data; funnel stage names with 12-quarter conversion history.

**Step 2 — Run the 3-tier bookings forecast.** Produce 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 a time-to-close probability adjustment. Surface the variance between commit and pipe-only as the pipeline-risk indicator. The assumption block is non-optional; omitting it turns the forecast into theatre.

**Step 3 — Project cohort-level ARR.** Compute per-cohort NRR and GRR over the projection horizon. Flag any cohort whose NRR is declining versus the trailing-cohort average — these leaky cohorts hide inside the consolidated number for 2–3 quarters before the leak surfaces in the topline. Output the consolidated NRR/GRR trajectory, a cohort heatmap, and a leaky-cohort callout.

**Step 4 — Score per-stage funnel confidence.** Per stage: mean conversion %, standard deviation, coefficient of variation (CoV = StDev / Mean), and a confidence band — HIGH under 10%, MEDIUM 10–25%, LOW 25–50%, VERY LOW over 50%. Recommend treatment per stage: extend the data window, treat as a soft floor, or commit-quality.

**Step 5 — Assemble the forecast deck.** Bring the 3-tier bookings number, cohort heatmap, and funnel confidence into the board deck. The assumption block belongs on the same slide as the number — a slide with just the number is theatre.

## Canon
SaaS forecasting: Skok, Tunguz, OpenView, BVP, Pacific Crest/KeyBanc, ProfitWell, Patrick Campbell. Cohort analysis: Andrew Chen (a16z), Brian Balfour, Skok, Ramanujam, OpenView, Lenny Rachitsky, Reforge. Anti-patterns: McKinsey, Tunguz, OpenView, MIT Sloan, Bain, Forrester, Pacific Crest.

## Assumptions
Historical conversion is a prior, not the truth — last 4Q weighted 70%, last 12Q weighted 30%, capturing regime change without overfitting to one bad quarter; window and weighting are surfaced in every output. A forecast without a disclosed assumption block is theatre — this is the hard rule. Cohort decomposition reveals leaks 2–3 quarters before the consolidated number does. Coefficient of variation is the right discipline for stage confidence — a stage at mean 40%/stdev 4% (CoV 10%) is HIGH confidence; mean 40%/stdev 20% (CoV 50%) is VERY LOW, even though the average is identical. Industry profile tunes priors, not truth — your historical data overrides. Emit three numbers and an assumption block; the CRO picks the commit number and owns the trade-off.

## Anti-Patterns
Single-number forecast with no confidence band — present three with named assumptions instead. Using last-12-quarter conversion blindly hides recent slowdown; the 70/30 blend corrects this. Reporting NRR without cohort decomposition — the consolidated number can be flat while a recent cohort leaks 15 points, surfacing 2–3 quarters later. Treating best-case as commit — best-case includes weighted-stage opportunities with under 50% time-to-close probability; commit only includes commit-grade stages. Hiding the assumption block. Suppressing a leaky-cohort flag in the deck. Ignoring late-stage opportunity age — a "verbal" deal stuck for 180 days is not a commit; stalled opportunities should be downweighted automatically. No pipeline-coverage check — forecast greater than pipeline ÷ 3 is an anti-pattern industry-wide.

## Forcing Questions (walk one at a time, depth-first, lock 1–3 before opening 4–7)

1. What conversion rate are you using — last-4Q or last-12Q? Recommended: a 70/30 blend. Canon: Tomasz Tunguz — single-window conversion estimates miss regime change at roughly a 3-quarter lag.

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

3. Can you show NRR by cohort, not just consolidated? Recommended: never report consolidated NRR without the per-cohort breakdown. Canon: Patrick Campbell (ProfitWell) and David Skok — cohort decomposition surfaces leaks 2–3 quarters before consolidated NRR moves.

4. What's the CoV on each stage's conversion rate over the last 12 quarters? Recommended: under 10% is commit-grade; 10–25% moderate; 25–50% soft floor only; over 50% don't use for forecasting. Canon: Hyndman & Athanasopoulos, *Forecasting: Principles and Practice* — CoV on the input series predicts forecast accuracy more reliably than the mean.

5. How long has each late-stage opportunity been in late-stage? Recommended: stage-age over 2x the median stage duration means treat as stalled, exclude from commit, keep in pipe-only. Canon: David Skok — stalled-opportunity identification by stage-age is the top forecast-hygiene practice in top-decile SaaS pipelines.

6. Is your best-case forecast within 30% of your pipe-only? Recommended: best-case under 50% of pipe-only signals sandbagging; over 80% signals hockey-sticking. Canon: McKinsey forecast-bias research and OpenView SaaS benchmarks.

7. What assumption block accompanies the number on the board slide? Recommended: conversion rate, data window, weighting choice, and pipeline-coverage ratio, always. Canon: Bain & Company commercial-forecasting practice and Forrester pipeline-coverage research — undisclosed-assumption forecasts show 2.3x higher variance against actuals than disclosed-assumption forecasts.