Revenue Forecasting Explained

A company’s revenue forecast provides the foundation for finance and operating plans throughout the business. What production capacity do we need this year, and will that require new capital investment? How much should we spend on advertising and demand generation efforts? How many salespeople do we need, and what quotas should they carry? How will the revenue forecast affect our stock price and our ability to raise capital? Building an accurate revenue forecast requires an understanding of your company’s products and capacity to execute, as well as constraints such as material inputs or labor, the competitive market, the macroeconomy, and more. Revenue forecasting is much more than laying down a target number. The forecasting process should help you evaluate the status of your business, assess your opportunities and risks, and map out the best strategies for your success.

What Is Revenue Forecasting?

Revenue forecasting is the process used to estimate how much money your company will bring in selling products or services over a certain period, such as monthly, quarterly, or annually. By looking at the state of your business and your previous performance, as well as forces outside your company, you can make educated assumptions to predict future gross sales.

A revenue forecast evaluates the business as a whole—not just the sales targets, marketing efforts, or other activities executed by revenue operations teams. It also must consider a company’s competitive landscape, its capacity in terms of production and staffing, and economic trends. The revenue forecast is one of the critical first assumptions in establishing a company’s budget. The revenue forecast is combined with estimates for expenses and investments to create profit and cash flow forecasts. Revenue forecasting is a quantitative analysis that companies depend on to make myriad other data-driven decisions, including how much advertising to buy and how many people to hire.

Key Takeaways

• Revenue forecasting is a prediction of how much money your business will bring in selling products and services. The forecast is based on historical trends, market conditions, and your business strategy.
• The business impact of effective revenue forecasting can be huge, since the forecasts drive company decisions around investments and spending, investor perceptions of the company, and even your ability to attract talent.
• To forecast revenue, analysts gather company data related to performance and financials, consider competitive and economic factors, and then apply the appropriate financial modeling and forecasting techniques that best fit your business.

Revenue Forecasting Defined

Revenue forecasting attempts to predict the sales your business will generate over some time period, typically a month, quarter, or year. A revenue forecast considers historical performance, economic and competitive conditions, and a company’s business plan, including the product, sales, and marketing strategies the company plans to apply during the forecast period. A top-down approach to revenue forecasting starts with these strategic elements and historical data, and then cascades the forecast down to operating units who are responsible for executing on the forecast.

The forecast can also be influenced using a bottom-up approach by estimating the planned revenue from individual departments. For example, a chief revenue officer’s organization can influence the revenue forecast by proposing increased sales or marketing activity that they believe can seize a new market opportunity. Or product development can shape the forecast by bringing new features or products to market. Once established, the revenue forecast is used by finance and operations teams throughout the company for budgeting and predictive planning.

Why Is Revenue Forecasting Important?

Revenue forecasting shapes how a company thinks about its future and the business decisions it makes. The assumptions that drive a revenue forecast shape a company’s short- and long-term goals, playing a significant role in preparing your organization for the future. The outlook on future revenue over the next few months or even year, shapes how much you budget for new hires, marketing campaigns, facilities and equipment, and research and product development. Finance teams will use that revenue forecast and then apply forecasts to all the elements on the cost side of the income statement necessary to achieve that sales target, thus estimating what profit and cash flow that will generate.

For example, if you forecast revenue growth at 5%, you might estimate, based on historical data and predictive tactics applied to the current competitive market, that you need to increase advertising revenue 8% and sales staff by 5% to reach that revenue. You might calculate that higher revenue will let you achieve better economies of scale, so your cost of goods sold will drop from 30% of revenue to 28.5%. Can your existing finance staff handle the accounts payable and receivables at the higher sales level, and do you have the necessary working capital to cover the inventory and payment float? The point is that the revenue forecast sends ripples through the organization.

For publicly traded companies, revenue forecasts also are often shared with Wall Street, where analysts closely watch company growth rates as part of their decisions whether to invest. Analysts will factor these revenue forecasts into their own models and may downgrade a stock—recommending that investors sell it—if a company misses its forecasts, especially if it does so repeatedly. Private companies looking to borrow, attract private equity or venture investment, or sell some or all of the business also will provide revenue forecasts. A revenue forecast is one key part of the core question of how strong a business is. Is this an opportunity worth putting more money behind?

How To Forecast Revenue

By examining the current state of your business, historical performance, and external factors, you can make an educated estimate of what future revenue will be. This forecast can help you develop data-driven strategies and make decisions that improve your business. Forecasting your revenue takes preparation and process discipline. Here are steps you can take to forecast revenue.

8 Forecasting Methods

Any forecasting method has specific strengths and weaknesses and relies on different variables in your business. Choosing the best method for your organization will depend on your current revenue growth pattern. Think of forecasting methods in two main buckets: qualitative and quantitative.

Qualitative leans on expert opinions, which might include your sales force, channel partners, outside analysis, and executives. Quantitative relies on using data and extrapolating a future value from that. You’ll most likely rely on some mix of the two inputs.

Here are some commonly used quantitative forecasting methods.

1. Straight-line forecast. For this method, you assume past growth rates will continue, so you multiply your revenue from the latest year by your company’s current growth rate. This means if you want to know your projected revenue for the coming year, you could look at revenue in each of the past two years. If you made $10 million two years ago and $10.5 million last year, then you experienced a 5% growth rate. To predict your upcoming year revenue, you multiply $10.5 million by 1.05, which results in $11.025 million. This approach is almost certainly too simple to build on entirely. It’s best for a rough estimate of projected revenue at companies with historically consistent growth and as a starting point to think about growth ranges and drivers.
2. Time series analysis. This technique uses historical data points at regular intervals to predict a future outcome. Many time series forecasting methods exist, so finance teams must figure out which is best for their industry and use case, considering factors such as seasonality and trend volatility. Below is one example of a time series forecast, the weighted moving average.
3. Weighted moving average forecast. This time series method uses the weighted average of data points to predict the next in sequence. This can be a practical way to monitor monthly revenue results and adjust near-term forecasts. As an example, you could look at your revenue from January, February, March, and April to project your revenue for May. The formula for a weighted moving average could look like the following:
   
   

   (January revenue x 10%) + (February revenue x 15%) + (March revenue x 25%) + (April revenue x 50%) = May revenue

   
   This method works best for projecting shorter, near-term time periods. It also works better for businesses that aren’t seasonal.
4. Linear regression. This method involves using the relationship between revenues and independent variables to draw a prediction. It’s a mathematical model that uses specific factors that drive your revenue to predict future revenue. Or in reverse, it can help to evaluate how much given factors can drive your revenue. For instance, if you believe advertising spending can drive revenue, you gather past data on company revenue and advertising spending. Apply a simple linear regression model to those factors to determine the relationship. To turn that into a revenue forecast, put your expected advertising spend for the coming year into the model to estimate revenue. Of course, other factors certainly drive revenue, so you’ll likely build a more complex regression model that takes more factors into account.
   
   

   Here are some commonly used qualitative forecasting methods.

5. Ask your executives. This approach could tap a panel of C-suite executives, or at smaller companies it could be a panel of one—the founder. It’s simplistic, of course, but your executives may very well bring some of the industry’s strongest expertise. And if you’re launching a brand-new product or a startup company, there might not be much data that’s relevant anyway. You don’t want to overspend on forecasting chasing a number that’s unknowable.
6. Ask your sellers. This can involve asking your salesforce for the revenue potential, in a bottom-up forecasting approach. Data might also come from resellers or other channel partners. Their input can be a good reality check on rosy top-down methods, such as asking the C-suite panel.
7. Ask outside experts. In this approach, you might rely on industry analysts and consultants, trade associations, academic researchers, and other people highly focused on your market. For example, an automaker taps numerous outside perspectives, along with their in-house experts, to forecast how many cars people will buy in a given market so they can factor that into estimates of their own revenue.
8. Ask your customers. Surveys of buyer intentions can be valuable in areas such as B2B industrial value chains, where you only have a limited pool of potential buyers. For consumer goods, you’ll probably look at broader consumer confidence surveys to gauge their overall spending intentions, and then apply those to your area, be it food, travel, durable goods, home improvement, and so on.

6 Revenue Forecasting Mistakes to Avoid

How accurately a company can predict revenue will depend on many factors, including input data quality and breadth, visibility into internal information (such as product launches and marketing efforts), sensitivity to external drivers and the volatility of those drivers, and much more. Here are some common mistakes people sometimes make while forecasting revenue and how to avoid them.

1. Expecting the past to continue. A straight-line forecast or one that relies too much on historical data to predict growth trends might be underweighting external forces, such as tougher competitors or a changing economy, or internal constraints, such as underinvestment in product or people.
2. Relying on limited or conflicting data. You need complete and trusted internal data to build a nuanced forecast. In addition to the total topline revenue number, it helps to drill down into data on those revenue sources, by geography, product, and sales channel. If these drill downs lead to conflicting or missing data points, forecasting gets much harder. Additionally, data may be hampered by seasonality or unusual circumstances, such as a retailer including annual data in a short-term forecast that doesn’t control for the holiday season or manufacturers basing demand on a period that included relevant regulatory changes.
3. Underestimating changing external conditions. People believe in their strategies and plans, so they have reason to focus on the upside. But forecasters must adequately weigh external factors and risks, such as competition, regulation, disruptive technology, and the economy, to temper any rose-colored views. It’s often best to create forecasts based on best-case, worst-case, and likeliest scenarios.
4. Underestimating the consequences of variability. Revenue forecasts ripple through an organization, influencing major decisions, such as hiring, procurement, and expansions. Financial forecasters need to understand all the decisions their numbers will drive and provide the appropriate context so people plan for variance. Do sensitivity analysis to understand how different variables affect total revenue and run scenario modeling to think through the implications of revenue swings. Forecasts for shorter periods, such as a month, might be highly accurate while longer-term forecasts, such as a year, might need to travel with more context and caution about the range of possible outcomes.
5. Overcomplicating your model. Plugging too many variables into a forecast model can cause a myriad of problems. For example, using variables that are highly correlated can put too much emphasis on one factor—so if you’re considering ice cream sales and weight variables for temperature, month, snowfall, and water park attendance, you’re probably just getting at different ways to see that you sell more when it’s hot. Likewise, you can build a model so complex that it’s hard to maintain and hard to explain to stakeholders such as executives, salespeople, and investors who need to buy in.
6. Overfitting your machine learning model: Overfitting happens when you build a model that so precisely interprets a set of historical data that it doesn’t help predict the future. An overfitted model likely comes from not having enough sample data, so your model tries to explain every outlier.

Forecast and Increase Revenue with Oracle’s Help

When your company’s financial and historical data is captured and harnessed to forecast your future revenue, these data-backed predictions can help you strengthen and expand the business. Oracle Fusion Cloud Enterprise Resource Planning (ERP) and Oracle Fusion Cloud Enterprise Performance Management (EPM) give you the data and analytical tools to create a sound revenue forecast process. The platform gives your finance team the data to understand your organization’s financial health and what drives revenue, helping you increase forecasting accuracy and improve risk management. Business leaders can access dashboards that pull data from across the company, delivering a trusted view of their organization’s finances and operations that provides a strong forecasting foundation.

Revenue Forecasting FAQs

How does revenue forecasting affect business decisions?
Revenue forecasting is core to the budgeting process, setting the tone for how optimistic the company is about the business. A strong revenue forecast, for example, will tell chief revenue officers they can invest more aggressively in marketing and sales, and CFOs can be more confident in making investments in people or production capacity.

What are the benefits of more accurate revenue forecasts?
Accurate revenue forecasting can help your organization make better-informed business spending decisions, set the right long-term goals, and win prospective investor confidence.

How can you forecast revenue?
You can forecast revenue by gathering accurate information on your company’s historical performance and financials, deciding what external and internal factors could change that growth rate direction, and then choosing which forecasting method is most relevant to use to predict your future revenue.