How To Forecast Revenue: 9 Methods To Help You Boost Growth

Revenue forecasting is one of the most important tools a small business can use to plan ahead. By predicting your future income, you can make smarter decisions about hiring, investment, and cash flow.

In this guide, we’ll break down what a revenue forecast is, why it matters, and nine practical methods you can use to predict your business’ growth.

Benefits of forecasting revenue

Forecasting future revenue gives small businesses a clear picture of what's around the corner. Here are the key benefits:

• Better financial planning: Helps with budgeting, investment decisions, and managing expenses.
• Cash flow management: Knowing when money will come in means fewer surprises when it comes to paying bills and staff.
• Setting realistic goals: A solid forecast makes it easier to measure performance against expectations.
• Investor and lender confidence: Accurate revenue forecasts can make it easier to secure funding or investment.
• Strategic decision making: Understand when you can afford to expand, hire new staff, or invest in marketing.
• Risk management: Spot potential problems early and adjust your strategy before they impact your business.

Revenue forecasting vs sales forecasting

Revenue forecasting and sales forecasting are easy to mix up. Revenue forecasting is about predicting total income, which may include more than just sales (e.g., subscriptions, services, or recurring revenue). Sales forecasting is only about predicting units sold or deals closed. Both are valuable, but revenue forecasting gives you the bigger financial picture.

Different models for forecasting revenue

There's no one-size-fits-all approach to forecasting future revenue. The best method depends on the size of your business, the stability of your income and how much data you have available.

Here, we’ll unpack some examples of revenue forecast methods and what type of business they suit:

Quantitative vs qualitative methods

Should you rely on hard data, or lean on expert judgement and experience? The answer usually lies somewhere in between. Here’s an overview of quantitative vs qualitative forecasting methods:

• Quantitative methods rely on historical data and mathematical models. They're great for established businesses with solid data but can struggle with sudden market changes or new product launches.
• Qualitative methods use expert judgment, market research, and subjective analysis. They're more flexible for new businesses or volatile markets but can be influenced by bias and overconfidence.

The best revenue forecast models often combine both qualitative (relying on data) and qualitative (relying on subjective analysis) approaches for a more accurate picture.

1. The straight-line method

The straight-line method assumes your business will grow at a consistent rate based on historical performance. It's the simplest forecasting approach and works well for stable businesses with predictable revenue patterns—but it doesn't account for market changes, seasonality or business cycles.

• How it works: Take your historical growth rate and apply it to current revenue to predict future performance.
• Data needed: At least 2-3 years of revenue data to calculate average growth rates.

Here’s the straight-line method formula:

Forecasted Revenue = Current Revenue × (1+Growth Rate)

Example: 

This year’s revenue = $500,000

Expected growth = 8%

Forecasted revenue = $500,000 × 1.08 = $540,000

2. Moving average forecast

Moving average forecasting smooths out short-term fluctuations by averaging out your revenue over a specific period. This method works particularly well for businesses with seasonal patterns or irregular revenue.

• How it works: Calculate the average revenue over a chosen period (e.g., 3, 6, or 12 months) and use this as your forecast for the next period.
• Data needed: Historical revenue data for your chosen averaging period.

Forecasted Revenue = (Revenue over X periods) ÷ X

Example: If revenue for the last 3 months is $45,000, $50,000, and $55,000, the 3-month moving average forecast is: (45,000 + 50,000 + 55,000) ÷ 3 = $50,000

3. Exponential smoothing

The “exponential smoothing” method of revenue forecasting gives more weight to recent data while still considering historical trends. It's particularly useful when recent performance is a better indicator of future results than distant history.

• How it works: Applies a smoothing factor (alpha) that determines how much weight to give recent versus historical data.
• Data needed: Historical revenue data and a chosen smoothing constant (typically 0.1 to 0.3).

Forecast = α × (Most Recent Revenue) + (1-α) × (Previous Forecast)

α (alpha) is a number between 0 and 1. You choose α based on how much weight you want to give the most recent revenue compared to the previous forecast.

• If α = 0.7, it means you’re giving 70% weight to the latest actual revenue and 30% weight to the old forecast.
• If α = 0.3, you’re giving 30% weight to the latest actual revenue and 70% weight to the old forecast.

Example:

Most recent revenue -$60,000

Previous forecast = $58,000

α = 0.7 

Exponential smoothing forecast = 0.7 × 60,000 + 0.3 × 58,000 = $59,400

4. Regression analysis (causal forecasting)

Regression analysis identifies statistical relationships between your revenue and other measurable factors (like marketing spend, website traffic, or economic indicators).

How it works: Regression analysis uses historical data to find correlations between revenue drivers and actual revenue, then applies these relationships to predict future performance.

Data needed: Historical revenue data plus data for independent variables (marketing spend, leads, etc).

Here’s how to calculate your regression analysis:

Forecasted Revenue = a + b × X

• a is the intercept, or base revenue (revenue you would get if the independent variable = 0)
• b is the slope, or the revenue increase per unit of the independent variable
• X is the independent variable (the factor you’re measuring, e.g., marketing spend)

Example:

Base revenue (a) = $100,000

Revenue increase per $1,000 marketing (b) = $5,000 per $1,000 of spend

Marketing spend (X) = $20,000

Forecasted Revenue = $100,000 + 5 × $20,000

Forecasted Revenue= 200,000 

5. Monte Carlo simulation

Monte Carlo simulation is a process, not a single formula. Instead of predicting one number, it tests thousands of possible scenarios to see a range of revenue outcomes.

How it works on a high level:

1. Identify uncertain variables (like new customers or conversion rates).
2. Assign likely ranges for each variable.
3. Use Excel or other software to run simulations and calculate revenue for each scenario.
4. Review the results to see best-case, worst-case, and most likely outcomes.

This is an in-depth revenue forecasting method. It’s a good idea to speak to your accountant or use accounting software to set it up correctly.

6. Bottom up forecasting

Bottom-up forecasting builds revenue predictions from the ground up, forecasting each product line, customer segment, or sales channel separately before combining them. It’s more detailed than simply applying a growth rate to last year’s revenue.

How it works on a high level:

1. Estimate sales for each product, service or channel.
2. Multiply by the price or expected revenue per unit.
3. Add everything together to get total forecasted revenue.

Data needed: Detailed historical data by product, customer segment, or channel.

This method is highly accurate because it sums actual expected revenue from each part of your business, rather than just applying a blanket growth rate. Using Excel or accounting software can make this calculation much easier, especially if you have lots of products or services.

7. The Delphi method (expert opinions)

The Delphi method is a form of qualitative method that collects expert opinions. It's particularly valuable for new markets or products without historical data.

• How it works: Survey industry experts, summarise responses, then conduct follow-up rounds until consensus emerges.
• Data needed: Access to relevant experts and structured survey process.

Example: You ask 10 industry experts to estimate the market size for a new product. Their first guesses range from $1 million to $5 million. After a few rounds of discussion, the group reaches a consensus of $2.5 million to $3.5 million.

8. Market research and surveys

Market research uses customer surveys, focus groups, and market analysis to predict how much demand there will be for your products or services – and how much revenue you might expect.

• How it works: Collect survey responses or industry reports, and then estimate the number of customers and their average spend.
• Data needed: Customer survey responses, market size data, and competitive analysis.

Forecasted Revenue = Estimated Customers × Average Spend

Example:

Estimated customers = 1,000

Average spend = $200

Forecasted Revenue = 1,000 × 200 = $200,000

9. Sales team insights

Sales forecasts leverage the knowledge of your sales team about likely deals and pipeline opportunities.

• How it works on a high level: Collect information on active deals and expected close rates. Multiply pipeline value by expected close rate.
• Data needed: Sales pipeline data, team forecasts, deal probability assessments.

Forecasted Revenue = Pipeline Value × Expected Close Rate

Example:

Pipeline = $500,000

Expected close rate = 60%

Forecasted Revenue = 500,000 × 0.6 = $300,000

How to forecast revenue

Forecasting revenue doesn’t have to be intimidating. Here, we’ll take you through a step-by-step guide for a basic but reliable forecast:

1. Gather historical sales data and pipeline forecasts

Start by collecting all relevant historical revenue data for your business. This includes:

• Past sales by product or location
• Monthly or quarterly revenue totals
• Sales pipeline information (pending deals and opportunities)

Why it matters: Historical patterns give you a baseline and help identify trends, seasonality, and growth rates.

Example:

2. Gather market data inc. seasonality and trends

Next, collect external market data that can affect your revenue:

• Industry trends
• Customer demand patterns
• Seasonal effects (e.g. holidays and weather)

Why it matters: Even if your historical sales are stable, market trends can impact your future revenue.

Example:

3. Account for the wider economy and any competition

Revenue can be influenced by external factors, such as:

• Economic growth or downturns
• Changes in consumer spending
• Competitor actions (new product launches, price changes)

Why it matters: Ignoring the wider context can make your forecasts too optimistic or pessimistic.

Example: Create a table or bullet chart summarising economic or competitor factors with expected impact on revenue.

4. Choose your forecasting model

Pick a forecast revenue model that fits your business and the data you have available.

• Simple methods: Choose straight-line or moving average for stable businesses
• Detailed methods: Use a bottom-up, regression or Monte Carlo method for complex or high-growth scenarios.

5. Calculate your revenue prediction

Once you’ve gathered data and chosen a model:

1. Plug your numbers into the forecasting revenue formula for your selected method.
2. Adjust for seasonality and market trends.
3. Generate a revenue forecast for the upcoming period (monthly, quarterly, or yearly).

6. Repeat the process regularly

Revenue forecasting is not a one-time task. You should update forecasts as new data comes in. Be sure to reassess assumptions and trends quarterly or monthly, and refine your model based on actual performance vs. predictions.

Regular updates ensure your revenue forecasts remain accurate and actionable.

Common challenges to overcome

Even with the best methods, forecasting revenue comes with pitfalls. Watch out for:

• Inaccurate or incomplete data: Using outdated or missing sales data can make forecasts unreliable.
• Over-optimistic projections: It’s tempting to assume your business will grow faster than reality. Factor in conservative estimates and worst-case scenarios.
• Ignoring market trends or seasonality: Past performance alone isn’t enough. You should consider external factors like economic changes or seasonal shifts.
• Neglecting external variables: Things like marketing spend, supply chain issues, or regulatory changes can seriously impact your revenue.
• Not updating forecasts regularly: Forecasts are only useful if they reflect current conditions. Check and adjust them monthly or quarterly.

Best practices to improve forecasting accuracy

Here are some strategies to help make your revenue forecasts more reliable:

• Use scenario planning: Build multiple forecasts (best-case, worst-case, most likely) to understand the range of potential outcomes.
• Make the most of accounting software like QuickBooks: These make it easy to do calculations and track historical revenue trends.
• Combine quantitative and qualitative methods: Use data-driven formulas along with insights from sales teams or market research.
• Break revenue into segments: Forecast revenue by product line or location for more precision.

 Small business example forecast

We’ve covered the most common forecast revenue formulas and best practices, but how does this actually play out in a real business? Let’s walk through a small business example to see forecasting in action.

Scenario: A small online retail store wants to forecast revenue for the next quarter.

Step 1: Gather data

• Historical sales: $40,000 per month on average
• Marketing spend: $5,000 planned per month
• Seasonal trend: 10% increase expected in December

Step 2: Choose model

• Bottom-up forecasting by product category

Step 3: Calculate forecast

Step 4: Adjust for marketing & seasonality

• Marketing expected to increase revenue by $2,000
• Seasonal boost of 10% on total

Final revenue forecast: $25,300 for the month