Business finance terms, explained simply.

A revenue forecast is an estimate of how much income a business expects to generate over a future period – typically a month, quarter, or year. Companies build it using historical sales data, current pipeline activity, and market conditions, then rely on it to guide budgeting, hiring, and strategic planning.

What Is Revenue Forecasting?

Revenue forecasting is how finance and sales teams predict future income before it actually shows up in the bank. Instead of waiting to see what comes in, teams project what should come in, drawing on past performance, deals currently in the pipeline, and outside factors like seasonality or shifting market conditions.

The revenue forecast itself is the output of that work – the actual number, or range of numbers, a team settles on for a given period. Some people use “revenue projection” as a stand-in for the same idea, though projection tends to describe a longer-range, more scenario-driven estimate, while forecast usually points to a nearer-term, operational one.

Why Revenue Forecasting Matters

A forecast a team can trust shapes nearly every major call a business makes.

Budgeting depends on it directly: finance teams size next year’s spending around expected income rather than guesswork. Hiring plans follow the same logic – leaders need a realistic read on revenue before adding reps, engineers, or support staff. Investors and lenders lean on forecast accuracy too, often judging a company’s trajectory by how consistently its numbers hold up over time.

Marketing and product teams use forecasts to plan spend and set roadmaps around expected growth, and a good forecast gives everyone an early warning if a shortfall is coming, rather than a surprise after the quarter closes.

None of this is trivial to get right. A recent Xactly survey found that 98% of finance and RevOps teams struggle to produce accurate forecasts, which says less about their effort and more about how much choosing the right method actually matters.

How to Forecast Revenue

Most teams work through a similar sequence, whatever model they end up using:

1. Pull the historical data. Start with past sales, win rates, deal sizes, and any seasonal patterns sitting in the CRM or accounting system.
2. Look at the current pipeline. Catalog active opportunities by stage, size, and likelihood of closing.
3. Pick a forecasting model. Choose the method, or blend of methods, that fits how the business actually sells.
4. Layer in assumptions. Account for market conditions, planned marketing spend, hiring plans, and known risks such as churn.
5. Run the numbers. Produce the forecast, ideally as a range rather than one exact figure.
6. Check it against reality. Compare the forecast to actuals on a regular cadence and adjust the assumptions as new data comes in.

Revenue Forecasting Models

No single model works for every business, so most teams choose based on how much data they have and how much precision they need.

• Top-down forecasting starts with total market size and works down to a realistic slice of it. It suits large enterprises with solid market data, though it tends to be less precise than more granular approaches.
• Bottom-up forecasting flips that logic: it builds the forecast from individual deals, reps, or product lines and adds everything up from there. It’s more detailed and usually more accurate, but it demands more time and cleaner data.
• Pipeline forecasting applies historical win rates to each stage of the active sales pipeline to estimate what’s likely to close within the period.
• Historical forecasting projects forward from past revenue trends and growth rates, assuming conditions stay roughly similar.
• Straight-line forecasting assumes a constant growth rate over time – simple to build, but it misses seasonality and market shifts.
• Regression-based forecasting models the relationship between revenue and other variables, like marketing spend or headcount, to predict where the numbers land.

In practice, mature teams rarely lean on just one of these. A common pairing is a bottom-up pipeline model checked against a top-down estimate, which helps catch a forecast that’s drifted too far from reality in either direction.

Revenue Forecast Example

Take a B2B software company forecasting next quarter’s revenue with a bottom-up pipeline model:

• $500,000 in pipeline value sitting at the negotiation stage, with a historical 35% close rate, works out to roughly $175,000.
• $300,000 at the proposal stage, closing historically at 18%, adds about $54,000.
• $50,000 in confirmed renewals at a 90% retention rate contributes another $45,000.

Adding those together puts the quarter’s forecast at roughly $274,000. Before finalizing it, the team checks that figure against last quarter’s actuals to make sure it holds up.

Final Thoughts

A revenue forecast is only as useful as the discipline behind it. The model matters less than most teams assume – what actually separates accurate forecasts from wishful ones is clean pipeline data, honest assumptions, and a habit of checking projections against real results instead of setting them once and moving on. Whether a business leans on a bottom-up pipeline model, a top-down market estimate, or some blend of both, the goal stays the same: give the people making budgeting, hiring, and growth decisions a number they can actually trust.