Revenue Forecast Model
Budgeting & Forecasting
Quick Answer
Enter your current revenue base, pipeline data, and growth assumptions by stream. Paste into Claude or ChatGPT. Get a bottom-up revenue forecast with quarterly targets and confidence assessment.
What You Get
A detailed revenue forecast covering bottom-up build by revenue stream, quarterly milestone targets, growth driver analysis, new versus existing revenue split, and confidence assessment with upside and downside scenarios.
Who Is This For
Sales leaders building revenue plans, CFOs challenging and validating revenue forecasts, and founders preparing revenue projections for investors.
About This Template
Revenue forecasting is the foundation of every financial plan. A good revenue forecast is built bottom-up from pipeline, customer cohorts, and growth drivers — not top-down from a growth percentage applied to last year. This template guides sales and finance teams through building a bottom-up revenue forecast by stream, then uses AI to produce a structured model with quarterly targets, growth driver documentation, and a confidence assessment for each revenue component.
Fill In Your Details
Your company name
e.g. FY2027 or next 12 months
e.g. $10.2M ARR
List each revenue stream with current annual run rate e.g. SaaS subscriptions $8.5M, Professional services $1.7M
Net revenue retention expectation e.g. 115 percent NRR from upsell and cross-sell minus churn
Current pipeline value and expected close rate e.g. $4.2M pipeline at 25 percent close rate implies $1.05M new ARR
Total new logo revenue target for the period
Sales capacity, average deal size, sales cycle length, seasonality
Gather these details then use them to fill in the prompt below.
AI Prompts
Generate revenue forecast model
Paste this prompt into Claude or ChatGPT with your inputs filled in.
You are a revenue operations analyst building a bottom-up revenue forecast. Using the information below, produce a detailed revenue forecast model. Company: [company_name] Forecast period: [forecast_period] Current ARR: [current_arr] Revenue streams: [revenue_streams] Existing customer growth: [existing_customer_growth] New customer pipeline: [new_customer_pipeline] New customer target: [new_customer_target] Key assumptions: [assumptions] Produce: 1. Revenue Build by Stream — existing customer revenue plus new customer revenue for each stream 2. Quarterly Targets — Q1 Q2 Q3 Q4 revenue milestones with implied quarterly growth rates 3. New vs Existing Split — what percentage of forecast revenue comes from existing versus new customers 4. Growth Driver Analysis — what drives each revenue stream and the key risks to each 5. Pipeline Coverage — how much pipeline is needed to hit the new customer target 6. Confidence Assessment — rate each revenue stream as high medium or low confidence 7. Scenarios — base case, 20 percent upside, and 20 percent downside with the key variable that drives each Be rigorous about the math. Challenge any assumptions that appear aggressive.
Sample Output
This is an example of what AI produces when you use this template.
Revenue Build by Stream
Starting ARR $10.2M. Existing customer revenue at 115 percent NRR: $11.73M from existing base. New logo revenue target: $2.1M from new customer acquisition. Professional services at 20 percent attach rate on $2.1M new ARR: $420K. Total forecast revenue: $14.25M representing 39.7 percent growth over current ARR. Key dependency: the existing customer NRR of 115 percent requires $1.53M in expansion revenue, which implies upselling 18 percent of the existing customer base to a higher tier during the year.
Pipeline Coverage
New customer target of $2.1M at a 25 percent pipeline close rate requires $8.4M in qualified pipeline to be generated and closed during the forecast period. Current pipeline of $4.2M covers 50 percent of the requirement. The sales team must generate an additional $4.2M in new pipeline during the year — approximately $350K per month in new pipeline generation. At 3 sales reps each generating $120K per month in new pipeline, this is achievable but requires consistent execution with no capacity gaps from turnover or ramp time.