Revenue Forecasting
The process of predicting future revenue based on pipeline data, historical close rates, and deal progression patterns. When done well, it gives leadership confidence in planning. When done poorly, it creates a false sense of certainty that leads to bad decisions.
Revenue Forecasting Is the Foundation of SaaS Planning
Hiring plans, marketing budgets, product roadmaps, and board communications all depend on revenue forecasts. An inaccurate forecast does not just miss a number — it cascades into overhiring for revenue that does not arrive, underspending on marketing that could have driven growth, or surprising your board with a miss they did not see coming.
Building a Forecast Model
Start with your pipeline: total value by stage, weighted by historical conversion rates at each stage. Add committed renewals and expansion revenue. Subtract expected churn. That gives you a bottom-up forecast. Then compare it to your top-down projection based on growth rate and market trends. The gap between these two is where the conversation starts.
Forecast Accuracy Over Time
Track your forecast accuracy monthly. Were your 90-day forecasts within 10% of actual? 20%? Measure accuracy by rep, by segment, and by deal size. Patterns emerge — maybe enterprise deals are consistently over-forecasted while SMB is accurate. Use these patterns to apply correction factors that improve over time.
The Commit vs Best Case Framework
Use three forecast tiers: commit (deals you would bet your job on), best case (deals with a reasonable chance of closing), and upside (stretch deals). Report all three to leadership. Commit should hit 90%+ of the time. Best case should hit 60-70%. This framework creates accountability without penalizing honest assessment.
Frequently Asked Questions
What is the best revenue forecasting method for SaaS?
Weighted pipeline is the most common — multiply each deal's value by its probability of closing based on deal stage. But the best forecasting combines multiple methods: weighted pipeline, historical trend analysis, rep-level commit forecasts, and machine learning models. No single method is reliable alone. Cross-referencing multiple approaches produces the most accurate forecasts.
Why are sales forecasts usually wrong?
Because they rely on subjective deal assessments from reps who are incentivized to be optimistic. Deals sit in stages too long without progression. Close dates get pushed repeatedly. New pipeline is counted at full value before qualification. Fix forecasting by enforcing stage exit criteria, tracking historical accuracy by rep, and applying regression to the pipeline rather than trusting gut feel.
