Quick Answer
To build a revenue forecast, segment your revenue by type — existing customers (renewals, expansion) and new business — then model each with appropriate drivers. For recurring revenue, start with current ARR, apply retention and expansion rates. For new business, use pipeline data weighted by stage probability. For transactional revenue, model volume and average transaction value. Layer these together for your total revenue forecast.
Revenue forecasting requires different approaches for different revenue types. A one-size-fits-all model will be inaccurate. Build each revenue stream separately, then combine.
Starting ARR: Your current annual recurring revenue — this is your most predictable base.
Gross retention: What percentage of existing ARR will renew? Model logo churn (customers who leave entirely) and revenue churn (downgrades).
Net retention / expansion: What is your net revenue retention (NRR)? This combines gross retention with expansion revenue from upsells, cross-sells, and price increases.
Formula: Next period ARR = Current ARR x Net retention rate + New business ARR
Pipeline-weighted approach: Take your current pipeline, weight each opportunity by its stage probability (e.g., Stage 1 = 10%, Stage 3 = 50%, Stage 5 = 90%), and forecast the expected value.
Capacity-based approach: Model the output of your sales team. Number of quota-carrying reps x average quota attainment = expected new business.
Historical run-rate: Use trailing 3-6 months of new business bookings as a baseline, adjusted for seasonality and pipeline trends.
Model volume x average price. Use historical patterns for volume assumptions, adjusted for growth trends and seasonal patterns. Factor in pricing changes and any expected volume shifts.
Layer each revenue stream together: - Existing customer base (retained + expanded) - New business (from pipeline and capacity models) - Transactional or usage-based components - Adjust for seasonal patterns and known timing effects
Validate your bottom-up revenue forecast against top-down benchmarks. Does your growth rate match your market position? Is your implied market share realistic? Run sensitivity analysis on key drivers: what if close rates drop 20%? What if churn doubles?
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FAQ
Forecast revenue 12-18 months ahead at monthly granularity. Beyond 18 months, use quarterly or annual estimates. Revenue forecasts beyond 12 months are inherently uncertain, so present them as ranges rather than point estimates.
Analyse 2-3 years of historical monthly revenue to identify seasonal patterns. Apply seasonality indices to your annual forecast to distribute revenue by month. Common patterns include Q4 surges for enterprise software and summer dips for B2B services.
Both, if possible. By product tells you which products are growing. By segment tells you which customer types are growing. The combination gives you the richest picture for decision-making.
Top-down starts with market size and estimates your share. Bottom-up builds from individual customer or deal-level assumptions. Use top-down for reasonableness checks and long-term planning. Use bottom-up for operational forecasting.
Yes. Grove FP provides driver-based revenue models with support for recurring, new business, and transactional revenue. Model pipeline-weighted forecasts, retention curves, and expansion assumptions in a single connected model.
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