Intro to revenue forecasting

Looking for a way to have greater impact as a RevOps professional?

Revenue forecasting could be your ticket.

It’s one of the most strategically valuable deliverables you can produce for your leadership team.

But what is it? And how do you do it? Let’s find out…

What is revenue forecasting?

Revenue forecasting, in revenue operations, is the process of making an educated guess about how much money the company will make over a given period of time. That could be next quarter, the upcoming half, or next year. 

Revenue forecasting can be a simple or a complex process, depending on the needs (and complexity) of the business.

Why should RevOps teams care?

Even the simplest revenue forecast can help a business’ strategy. This is true even for:

• Small businesses
• Businesses without much available data
• Businesses without great internal resources

Since they’re of direct strategic benefit to your business, revenue forecasts have implications for your career (and salary) progression.

Revenue operations professionals have access to all the revenue data necessary to come up with a good top-down forecast. While sales reps themselves can make educated guesses about how much revenue they think they’ll bring in, it takes someone with oversight of the entire business to make a good overarching revenue forecast. A revenue operations team is well-placed to do that.

How are revenue forecasts of strategic value to businesses?

Going to the trouble of putting together a good revenue forecast has a number of strategic benefits:

• Better decision-making: Revenue forecasts allow leaders to make data-backed decisions, based on the amount of cash they’re likely to have available at some point in the future.
• Setting financial goals: Businesses love growth. Armed with a specific, forecasted revenue figure, your leaders can decide how much they want to stretch that target.
• Evaluate performance: Tracking industry trends, as well as your own performance, allows you to benchmark your performance. At worst, you realize you’re underperforming, and leaders can devise a strategy to get the business back on the right path.
• Manage cash flow: Cash flow is the lifeblood of any business. Accurate revenue forecasts can reveal seasonal trends that might put cash flow at risk at certain times of year. This can inform spending strategy at these times.
• Attract/report to investors: Public companies have a duty to report revenue forecasts to their investors. But, for companies looking to go public, accurate revenue forecasts are essential for painting an attractive picture of the business that can win it new investors.

Forecasting revenue vs. forecasting sales

A revenue forecast is not the same as a sales forecast. Sales forecasts are simpler. Yes, they deal with revenue. But they deal only with revenue from predicted sales over a given period of time.

For some businesses, this is perfectly adequate. And if you’ve never built out your own revenue forecast before, this is where we’d recommend you start.

But other businesses will need to incorporate:

• Renewals
• Recurring revenue
• Projected up-/cross-sells (perhaps around renewal periods)

This is where complexity starts to creep in. And, as we’ll see in later articles in this series, complexity is one of the main practical challenges you’ll deal with when creating a revenue forecast.

Revenue forecasting models and methodologies

Not only in the past, but in many companies around the world today, revenue forecasting comes down to the intuition of the sales leader.

But the “art” of revenue forecasting is no longer enough.

Today, we have models, AI, and abundant data. Together, they make the science of revenue forecasting accessible to every RevOps professional.

Below is an overview of the models, methods, and forecasting metrics you need to know to construct an accurate, data-backed revenue forecast.

Data types for revenue forecasting

When it comes to methods of data collection, you can prioritize qualitative data, or quantitative data.

• Qualitative forecasting methods

Qualitative approaches tend to lean on expert opinions and market research. Resources can include:

• Industry experts
• Internal stakeholders
• Your customers
• Third-party market research

• Quantitative forecasting methods

Quantitative methods, instead, look at your historical numeric data.

These can get very complex, especially at the enterprise level in the finance sector. Moving averages, exponential smoothing averages, regression analysis, and even Monte Carlo simulations become options.

For most businesses, though, something simpler (and faster), with a lower skill floor, is more appropriate.

Dan Leva, Senior Vice President of Revenue Operations at Cielo Talent, breaks forecasting methodologies down into the following buckets:

• Holistic historical: You look at the number of deals in your pipeline, the historical win rates of those types of deals, and similar metrics.
• Subjective historical: You have your reps take a look at their pipeline, and they come back to you with a number, based on how likely they think it is they’ll win those deals.
• Forward-looking: Rather than historical data, these tend to use activity-based outreach metrics. For example, the number of calls reps are making, or the number of meetings they’re booking.

We’ll look at each of these in more detail in the next section.

Forecasting methodologies you need to know

There are several types of forecasts.

Bottom-up and top-down forecasts

There are two types of forecasts relating to perspective that most revenue operations professionals will need to know:

• Bottom-up forecasts
• Top-down forecasts

• Bottom-up forecasts

A simple bottom-up forecast is owned by your sales team. First, your reps look at their pipeline. Then, they decide how much they’re likely to close. The numbers they come up with, taken together, make up your bottom-up forecast.

The bottom-up forecast is focused on individuals. Individual reps, products, and sales channels. It starts at this granular level, and works upwards and outwards.

• Top-down forecasts

A simple top-down forecast is owned by someone with some distance from individual deals. That could be you, as the RevOps professional. Or, it could be someone in your leadership team.

Top-down forecasts begin with a general, broad view of the market data. The size of your total addressable market, your projected market share potential, and patterns of historical growth inform this type of forecast.

Backward vs. forward-looking forecasts

There are several other types of forecasts that relate to directionality:

• Historical forecasts
• Forward forecasts

• Historical forecasts

Historical forecasts use (surprise!) historical data to create a forecast. There are a couple of different approaches:

The subjective historical forecast is a subjective approach that leans on sales reps for their own sense of what they feel they’re likely to close. They’ll decide this based on what’s in their pipeline, and their experience with past deals.

The holistic historical forecast goes much more in-depth. You’ll look at all your historical data, and perform a comprehensive analysis that gets you to a firm number. John Lorenc runs you through how to do this in his article on rolling out your own forecasting process.

Using historical data is a good approach for businesses with consistent revenue cycles. But there are always risks. Market conditions change, and historical data doesn’t account for such changes.

• Forward forecasts

Predictive analytics uses AI and machine learning techniques. It applies them to real-time data on things like market trends and your own sales pipeline. The result? A solid prediction about what’s likely to happen in the future.

Bespoke tools are likely to do the best job at this. But, you can outsource this to your favorite LLM, or do it manually.

This approach works best for businesses with revenue cycles that are very dynamic.

Subjective forward-forecasts offer you a quick-and-dirty way of getting a ballpark forecast from real-time data. These are derived from qualitative data, like expert opinions, as well as educated theories about where revenue will end up, based on the data you have right now.

They’re useful when a business doesn’t have a lot of numeric data available. For example when:

• Launching new products.
• Entering new markets.
• Operating in very changeable markets.

• Hybrid approaches

A great solution to many of the practical challenges of revenue forecasting is to combine forecasts. This almost always improves their accuracy, but it also involves more work.

This is why it’s crucial to get crystal clear on what you’re trying to achieve with a given forecast.

If you only need a vague idea of what you’re likely to close next quarter, using your reps’ opinions can be enough.

But if you need an ironclad forecast you can deliver to stakeholders, which will be scrutinized by investors and world markets, you should combine several of the more technical approaches.

Next step: How to do your own revenue forecast