Pipeline, history, and market signal rolled into one model. Bookings, revenue, and margin reconciled — at SKU, region, and rep level. The board sees a range, not a fake-precise point. Variance attributes back to the deal that moved the number.
P25/P50/P75 confidence cone. Bookings → margin waterfall. Variance attribution after the quarter posts.
Sales has a number. Finance has a number. Ops has a number. None of them agree, and the board meeting is in 48 hours.
Six capabilities that make this module work end-to-end. Pick any one as your starting point — they compound.
Forecast at SKU, region, segment, rep, or vertical level — and roll up consistently. No more reconciling a regional view that doesn't match the segment view.
Each opp weighted by its actual close-rate signature — segment, deal size, sales motion, time-in-stage. Not a flat 30%.
All three numbers reconciled in the same model. Bookings translate to revenue via your rev-rec rules; revenue to margin via Cost Engine.
Sales capacity, fulfillment capacity, install crew capacity — modelled against the forecast. Where the bottleneck is, before it bites.
Model the price hike, the macro hit, the competitor entry. Show the board the range, not a fake-precise point.
When actuals come in, variance attributes back — segment underperformed, deal slipped, cohort missed. Not 'we missed by 6%.'
From inputs to a defended P25/P50/P75 in two hours, not two weeks. Conviction overrides logged. Variance closes back automatically when the quarter posts.
Pipeline (live CRM), 8 quarters of close patterns, market signal (Market Intelligence), capacity (ops planning).
~860msReturns P25, P50, P75 — not a single number. Assumptions and weights surfaced for review.
~412msLeadership layers known macro tailwinds or strategic ramps. Override and rationale logged.
humanQuarter posts, actuals reconcile back. Model learns. Conviction calls scored.
~24msForecast policy in plain language — segment weights, capacity floors, override routing. No black-box ML. Every number on the deck shows the rule and the input that drove it.
Conviction overrides are first-class. Logged with rationale. Scored against actuals next quarter — you see whose calls were right.
# Demand Forecasting · Model Policy when opp.segment = "enterprise": weight_by close_rate(seg, deal_size, time_in_stage) capacity_check → install_crew when opp.stage = "verbal_yes": fast_track → p50_inclusion when override.applied: require rationale score_against actuals(+1q) when capacity_constraint > 85%: surface bottleneck → ops_lead cap forecast at_capacity # Last edited: D. Yates · 4d ago
Salesforce, HubSpot, Dynamics — live opportunities feed the model.
Snowflake, BigQuery, Redshift — 8 quarters of close patterns.
Crew rosters, install capacity, leave calendar.
Live dashboard, drill-down, export-to-deck for the QBR.
We import your last 8 quarters of pipeline and actuals, run the model, and walk you through a P25/P50/P75 forecast — defended against your real numbers.