Quarterly Bookings and ARR Forecast
Build a quarterly bookings forecast with commit and upside scenarios, calibrated pipeline weights, and a cohort ARR bridge.
Before & after
Same prompt, same agent — the only difference is the skill.
I'd be happy to help you build a forecast. We should start by looking at your pipeline data, historical win rates, and any assumptions you want to bake in. Do you have your pipeline sorted by stage? What's your average sales cycle? Once we have that, I can project things forward and give you some scenarios. The forecast will probably include some kind of base case and maybe an upside scenario. We should also think about churn, revenue retention, and cohort performance, though those numbers can be tricky to estimate. Would it help to break this down by sales team or customer segment?
QUARTERLY BOOKINGS FORECAST — Q4 2026 FUNNEL DECOMPOSITION • Commit tier (stage 3+): 2.8M ACV × 68% close rate = 1.9M committed • Best-case tier (stage 2-3): 1.2M ACV × 42% close rate = 504K upside • Pipe-only (early stage): 850K ACV × 18% close rate = 153K exploratory → Total scenario range: 1.9M to 2.6M COHORT NRR ANALYSIS • 2025-cohort GRR: 91% (leaky — investigate annual renewal practices) • 2024-cohort NRR: 107% (expansion-driven, strong upsell performance) • 2023-cohort: matured at 98%, no net logo loss PER-STAGE CONFIDENCE SCORING • Stage 3 (negotiation): 78% historical close → high confidence • Stage 2 (demo): 42% historical close → soft floor, treat conservatively • Stage 1 (discovery): 18% historical close → discount by 50% for quarterly planning KEY ASSUMPTIONS (explicit & defensible) • Data window: last 8 quarters, 47 closed deals • Weighting: commit assumes 68% rate (last 4 quarters average); best-case assumes stage 2 conversions at 40% • NRR uses calendar-year cohorts; excludes comps and 100-off promotional credits
About this skill
“We have enough pipeline” does not explain what will actually close this quarter. Build a forecast that separates the commercial commitment from probability-weighted expectations and upside, then connects new business to an ARR bridge without confusing bookings with revenue.
What it does
- Separates closed business, commit, best-case additions, and remaining pipeline without double counting.
- Weights opportunities using historical outcomes matched to stage and time remaining in the quarter.
- Flags weak samples, stale close dates, concentrated deal risk, and unsupported rep confidence.
- Projects cohort GRR and NRR using the opening customer base, exposing deterioration hidden by consolidated growth.
- Reconciles bookings to ARR through contract scope, activation dates, churn, contraction, and expansion.
- Explains forecast changes through actual wins, slippage, amount changes, and revised assumptions.
What you get
A decision-ready quarterly forecast with cumulative scenarios, a probability-weighted estimate, a cohort ARR bridge, and a short list of risks and actions. Each material assumption includes its rate, denominator, historical window, and weighting method; missing evidence stays visible.
Best for
RevOps leaders, heads of sales, and CROs preparing weekly forecast calls, quarterly operating reviews, or board materials for recurring-revenue businesses.
SKILL.md preview
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name: quarterly-bookings-and-arr-forecast
description: >-
Build a defensible quarterly bookings and ARR forecast using evidence-based forecast categories, time-matched historical conversion rates, and a reconciled cohort ARR bridge. Use when the user says "build our quarterly forecast", "what can we commit to the board", "my reps keep pushing close dates", "our weighted pipeline never matches actuals", "split commit from best case", "project ARR and NRR", or "check this forecast before the board meeting".
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# Quarterly Bookings and ARR Forecast
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