What is Sales Forecasting?
Sales forecasting is the process of estimating how much revenue your team is likely to generate over a future period, typically a month, quarter, or year. A forecast is not a guess - it is a structured estimate built from real data: the deals currently in your sales pipeline, the stage each one is at, historical conversion rates at each stage, and any patterns or trends that might affect close timing.
A good forecast gives leaders something to act on. It answers questions like: Are we on track to hit target? Do we have enough pipeline to make up for deals that will inevitably fall through? Should we invest in more lead generation now, or focus on converting what is already in the pipeline? Without a forecast, those decisions are made on instinct. With one, they are made on evidence.
How a basic sales forecast works
The simplest form of forecasting multiplies the value of each deal by the probability of it closing, then sums the result. That probability is usually derived from the deal’s current pipeline stage: if deals in the Proposal stage historically close at a 40% rate, every deal in that stage is counted at 40% of its face value in the forecast.
For example:
- Three deals in the Proposal stage worth $50,000 each: $150,000 x 40% = $60,000 forecast contribution.
- Two deals in Negotiation worth $30,000 each: $60,000 x 70% = $42,000 forecast contribution.
- One deal in Verbal Agreement worth $80,000: $80,000 x 90% = $72,000 forecast contribution.
- Weighted total forecast: $174,000.
This weighted pipeline approach is more reliable than simply summing all open deals (which would overstate revenue) or counting only deals marked as commit (which often understates it).
Types of forecasting approaches
Teams use a range of forecasting methods depending on their size, data maturity, and how their sales cycle works:
- Stage-based (weighted pipeline) - the method described above. Practical for most teams and easy to maintain in a CRM.
- Rep-called forecast - each rep commits to a number they are confident will close. Useful for accountability but vulnerable to optimism bias.
- Historical run-rate - project forward based on what the team has averaged in comparable prior periods. Works well for stable, recurring businesses.
- Trend-adjusted - similar to run-rate but adjusted for growth trends, seasonality, or market conditions.
Many teams use a combination: a weighted pipeline number as the baseline, adjusted by rep input and trend data.
Why forecast accuracy improves over time
The accuracy of any forecast depends on the quality of the underlying data. Teams that have used a CRM consistently for a year or more have reliable conversion rate data at each stage, which makes weighted forecasts much more accurate. Teams that are just starting out will have to use estimated probabilities until real data accumulates.
Keeping deal records up to date, maintaining accurate close dates, and logging all activity are the inputs that make forecasting reliable. A pipeline full of stale deals with optimistic close dates produces a misleading forecast regardless of the method used.
For more on building a pipeline that supports accurate forecasting, see the guide to sales pipeline management and the conversion rate glossary entry.