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RampRamp

Revenue Forecast Model Build

Sales & GTM

What it tests

Whether a candidate can build a bottoms-up revenue forecast from raw data, document their assumptions explicitly, and defend the model under scrutiny

Format

  1. 1Candidate receives a Google Sheet with 12 months of historical bookings by rep, segment, and channel — plus company headcount plan and average ramp time by role
  2. 2Task: build a bottoms-up forecast for the next two quarters broken down by rep capacity, pipeline coverage, and historical conversion rates
  3. 3Must include an assumptions tab, a sensitivity table (what happens if win rate drops 10%), and an executive summary
  4. 448-hour take-home; followed by a 30-minute live walkthrough where interviewer plays CFO asking pointed questions

What to look for

  • Is the model bottoms-up (rep × quota × attainment) or just top-down extrapolation dressed up as analysis?
  • Are assumptions explicit and defensible — or buried in formulas?
  • Does the sensitivity table reflect real business levers (win rate, ACV, ramp time) — or generic percentages?
  • In the CFO roleplay, can they explain why their number is conservative or aggressive and what would need to change to hit the high case?

Adaptation guide

Use your actual (anonymized) historical bookings data — the more realistic the dataset, the more signal you get. For companies without reps, adapt to a product-led growth model: cohort retention, expansion revenue, and conversion from free to paid.

Full description

Format:

  1. Candidate receives a Google Sheet with 12 months of historical bookings by rep, segment, and channel — plus company headcount plan and average ramp time by role
  2. Task: build a bottoms-up forecast for the next two quarters broken down by rep capacity, pipeline coverage, and historical conversion rates
  3. Must include an assumptions tab, a sensitivity table (what happens if win rate drops 10%), and an executive summary
  4. 48-hour take-home; followed by a 30-minute live walkthrough where interviewer plays CFO asking pointed questions

Time: 120 minutes (take-home)

What to look for:

  • Is the model bottoms-up (rep × quota × attainment) or just top-down extrapolation dressed up as analysis?
  • Are assumptions explicit and defensible — or buried in formulas?
  • Does the sensitivity table reflect real business levers (win rate, ACV, ramp time) — or generic percentages?
  • In the CFO roleplay, can they explain why their number is conservative or aggressive and what would need to change to hit the high case?

Adaptation: Use your actual (anonymized) historical bookings data — the more realistic the dataset, the more signal you get. For companies without reps, adapt to a product-led growth model: cohort retention, expansion revenue, and conversion from free to paid.