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🎯
IMPACT
Built a fully dynamic bottom-up revenue forecast
model projecting 12-month ARR across 3 scenarios.
Best case: $3.45M ARR. Base case: $1.1M ARR.
Worst case: $189K ARR. Includes ramp curve logic
and a sensitivity table showing how win rate and
deal size interact — fully recalculates on any
assumption change.
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🔴 The Problem
Sales and leadership teams make hiring and budget
decisions based on hope, not math. A VP of Sales
with no forecast model cannot answer the board's
most important question: "If we hire 3 more reps,
what does ARR look like in 12 months?" This
uncertainty stalls growth decisions.
🔵 My Approach
Built a bottom-up forecast model that starts with
the inputs that actually drive revenue — not a
top-down guess. Every output is traceable to a
specific assumption. Three scenarios stress-test
the range of outcomes. A sensitivity table shows
exactly which lever has the most impact on ARR.
⚙️ Step-by-Step Execution
- Tab 1 — Assumptions: Built input panel with
6 variables across 3 scenarios:
• Number of Sales Reps (8 / 5 / 3)
• Ramp Time in months (2 / 3 / 4)
• Opportunities per Rep per Month (5 / 4 / 3)
• Sales Cycle Length in months (2 / 2 / 3)
• Win Rate (25% / 20% / 15%)
• Average Deal Size ACV ($60K / $50K / $40K)
- Built ramp curve logic: reps produce partial
output during ramp period — not full quota
from day one
- Tab 2 — 12-Month Forecast: Month-by-month
table for all 3 scenarios showing Effective
Reps, Opps Created, Deals Closed, New ARR,
and Cumulative ARR
- Key insight built into model: January is
intentionally low due to ramp — model
accelerates from Month 3 when reps hit
full productivity
- Tab 3 — Sensitivity Analysis: Built two
tables showing how 12-month cumulative ARR
changes as Win Rate and Deal Size each move
±5% and ±10% from base case
- All tabs fully dynamic — change any
assumption and every formula recalculates
instantly across all 3 scenarios
📊 Results
- Best case: $3.45M cumulative ARR at Month 12
- Base case: $1.1M cumulative ARR at Month 12
- Worst case: $189K cumulative ARR at Month 12
- Sensitivity table reveals key insight: a 5%
drop in win rate can be offset by a 10%
increase in deal size — deal size is a
lever worth protecting
- Model is fully dynamic — any assumption
change recalculates all 12 months instantly
- Ramp curve logic makes this a realistic
model, not an optimistic straight-line
projection
🧠 Key Learnings