Revenue Forecasting Guide: Best Practices and How to Select the Right Model

Many sales managers dread the term "revenue forecasting." This is because, for them, it implies:

Hours of tedious manual data manipulation
Messy, inconsistent data submissions by sales reps
Scrambling to find lost versions in email chains
Time-consuming error corrections

As a result, they might spend a significant part of their workweek validating numbers and correcting errors. But they might still end up with results they can't trust.

Usually, you can fix such issues by replacing spreadsheets with more reliable and effective tools and systems.

Table of contents

What is revenue forecasting?
Why is revenue forecasting important?
How to choose the right revenue forecasting model
The problems with using spreadsheets for revenue forecasting
Step up your revenue forecasts with revenue precision
Other revenue forecasting best practices to adopt
Improve your revenue forecasts

What is revenue forecasting?

Revenue forecasting is the process of using past sales data and current trends in the business to predict how much money a company will make over a specific period—usually over a quarter or a year. Accurate forecasts help businesses plan budgets and set performance goals.

Why is revenue forecasting important?

Revenue forecasting is a critical process for most businesses. Here are some reasons why it's important to forecast revenue:

Businesses rely on forecasts to plan spending. Revenue forecasts influence major budgeting decisions, like hiring and capital expenditures.
With forecasts, companies can establish targets and metrics to measure performance.
Forecasting helps businesses plan for risks and opportunities in the marketplace.
Forecasting makes it easier to manage cash flow during times when revenue fluctuates.

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How to choose the right revenue forecasting model

Choosing the right forecasting model is an important decision that affects how much money a company thinks it will make and what plans it makes for the future.

Usually, top leaders like Chief Revenue Officers (CROs) and Chief Financial Officers (CFOs) make the big decisions for the whole company. But sometimes, the people in charge of sales have to pick the best way to forecast sales for their teams.

If a company is large or decentralized with lots of separate parts, the sales teams might have more freedom to decide what's best. This means the sales leader might choose their own sales forecast models that match their area, the things they sell, and the people they sell to.
In some cases, people who lead special sales teams that focus on different markets might know the best way to predict sales for their unique situations.
In companies where being creative and trying new things is encouraged, sales managers might experiment with different forecasting methods to see which one works best.
For businesses that are growing quickly and have fluid job roles, sales leaders might need to stay on top of things by using new forecasting models that match the changing conditions.

To choose the right revenue forecasting model, consider factors like:

The purpose of the forecast and key variables to include
Data availability and relevance
Required accuracy and time horizon
Costs versus benefits

For instance, let's take causal models like regression analysis. They need a lot of past data. On the other hand, time series models can spot patterns over time, even if you don't have a ton of data. When you don't have numbers, qualitative methods work better.

Knowing the good and bad things about forecasting models helps you pick the right one for your situation.

This is where RevOps platforms come in handy.

They use smart algorithms to test various model combinations. They figure out which one is the most accurate for your specific business needs. This means they can make forecasts that get better as you get more data.

The table below provides an overview of common forecasting models and when to use them:

Model	Type	Explanation
Straight-line method	Time series	This model predicts future income by assuming a constant growth rate over time. Use it when you expect sales to increase or decrease at a steady rate over time.
Moving average	Time series	This model calculates future revenues by averaging past data within a specific time frame. Use it to smooth out fluctuations and reveal trends in data.
Exponential smoothing	Time series	This model assigns more weight to recent data to predict future revenues while diminishing the importance of older data. Use it when your data shows trends but also variability.
Trend projection	Time series	This model estimates future income based on historical trends and patterns. Use it when you have historical data showing a clear linear trend to extend into the future.
Simple linear regression	Causal	This model predicts income by considering multiple variables and their impact on revenue. Use it when the forecast depends on multiple factors that relate linearly to it.
Multiple linear regression	Causal	This model predicts income by considering multiple variables and their impact on revenue. Use it when the forecast depends on multiple factors that relate linearly to it.
Market survey	Qualitative	This model relies on collecting data through surveys or questionnaires from potential customers to estimate future income. Use it when historical data is unavailable and industry expertise can inform forecasts.
Sales force opinion	Qualitative	This model uses the insights and opinions of a company's sales team to predict future revenues. Use it to supplement quantitative methods with insights from your sales teams.
Delphi method	Qualitative	This model collects input from a group of experts in a series of structured steps to reach a consensus on future income. Use it to gain a consensus forecast from a panel of experts through iterative surveying.
Visionary forecasting	Qualitative	This model relies on the intuition and insights of visionary leaders to predict future revenues. Use it for long-term forecasts based on expert visions of the future.
Panel consensus	Qualitative	This model involves a group of experts or stakeholders working together to reach a collective prediction of future income. Use it to combine insights from a diverse panel of experts.

For short-term predictions, use time series models like moving averages. For long-term forecasts that look ahead, use causal models like regression, which consider market conditions.

PRO TIP

In general, it's often a good idea to use numbers and expert opinions.

Expert opinions can add valuable insights to the data. To figure out the best mix of models, think about what your business needs and what tools you have.

To help you forecast more accurately, identify leaks in your pipeline, and ensure that your team hits their number, we've put together a forecast checklist you can download for free.

The problems with using spreadsheets for revenue forecasting

Using spreadsheets for revenue forecasting is a common thing to do, but it has some issues. Here are five problems with it:

Spreadsheets can cause confusion when many people make changes to the same document. It's hard to keep track of who did what, and this can lead to mistakes in the numbers.
Spreadsheets aren't good for working on things together in real time. Usually, people have to send the file to each other through email, which can slow down decision-making and cause misunderstandings.
Predicting revenue often involves typing in a lot of data by hand, and this can lead to mistakes. These mistakes can impact forecast accuracy and cause financial mismanagement.
Spreadsheets can't make complex predictions automatically. Making and updating predictions takes a lot of time and effort, which isn't very efficient.
Spreadsheets might have trouble handling big sets of data. They can become slow, not work properly, and sometimes even crash when dealing with a lot of information. This makes it hard to study the data closely.

Also, spreadsheets can't give you a complete picture of how your sales are doing and what trends you can expect. You might have to gather information from different places to understand it all.

Spreadsheets are good for basic math and organizing information, but they don't have the special tools that modern sales teams need for accurate revenue predictions.

They lack things like automatic checks for data errors, finding unusual things in the numbers, studying patterns, making personalized models, and keeping track of how good the predictions are. Without these tools, making reliable predictions is hard.

Step up your revenue forecasts with revenue precision

Rather than rely on spreadsheets or old-school forecasting methods, take your revenue predictions to the next level with revenue precision.

Revenue precision means making sure that everyone who plays a part in earning money for the company can see and understand how the money comes in. This way, you can be sure that you can always count on the money coming in correctly and steadily.

With revenue precision, executives can see if the company will hit its revenue goals, fall short of them, or exceed them.

Revenue precision happens when people, the way things are done, and the tools we use all work together perfectly.

To achieve revenue precision, you must identify blind spots, simplify the tools and processes you use, and encourage teams to work together all the time.

This is where a revenue operations (RevOps) platform like Clari comes in.

Clari uses artificial intelligence (AI) to make predictions and automated data processes to keep your forecasts up-to-date based on what's happening in your business right now. Instead of spending a lot of time working with spreadsheets, your teams can see how well each deal is going and focus on making the most important deals successful.

With Clari, you can:

Merge data from your CRM, emails, calendars, and more into one central place.
Have the system automatically update forecasts as deals develop, using cues like how interested potential customers are.
Empower teams to model different revenue scenarios so they can make smart decisions.
Identify at-risk deals so the people responsible can get them back on track.
Save management's time by letting the system provide automatic insights, rather than making forecasts by hand.

This top-notch technology has made a big difference for many companies when it comes to predicting revenue.

One example is Databricks.

They used Clari's system to see potential problems in their sales pipeline way before they happened. By spotting deals that were starting to go south, they were able to increase their success rate on those deals by a whopping 169%.

We use Clari to have more intelligent forecast conversations, especially when we look farther out. By looking at historical trends, we can extrapolate where we'll be going forward. We don't have a crystal ball, but we have Clari.

Jules Gsell, RVP of Growth and Start-Up Sales Orgs, Databricks

PRO TIP

If you’d like to spot problems in your pipeline before they happen like Databricks, then you’d love our Pipeline Inspection Checklist. It’s free to download. This 5-point checklist will equip you to uncover hurdles, collaborate, and win more deals easily.

Meanwhile, Clari allowed MasterControl to drive revenue confidence and turn "the art of forecasting into a science."

The operations team relies on data and information from Clari to create reliable forecasts for their leaders. They've also learned how to foresee revenue trends for a longer period ahead.

I'm a huge fan of Clari. With my role in operations, it gives me a level of insight that drastically changes the conversation I have with sales leadership. It helps me understand what's going on, and I have much more confidence as I roll up numbers to management. Clari helps me turn the art of forecasting into a science.

Dan Alvey, Director of Sales Operations, MasterControl

If you want to improve your revenue forecasts, book a free demo today to see the product firsthand and learn how it can help you gain revenue visibility, rigor, and control.

Or, if you're not ready for a demo yet, check out this report from Forrester to learn how Clari can align revenue-critical teams and processes to deliver revenue precision and a 448% ROI.

Say Goodbye to Shot-in-the-Dark Forecasting

Forecast Checklist

Download now

Other revenue forecasting best practices to adopt

When you want to make organizations better at forecasting revenue, there are extra things to think about besides just changing from spreadsheets to better tools and systems.

Let's look at five of them.

Thoroughly validating forecast accuracy

To forecast revenue accurately, you need to check if your predictions are right. To do this, you compare what you thought would happen with what happened.

This helps you learn what works well and where you can do better. It also helps you understand why your predictions might be wrong.

For instance, if the data is wrong, the way you make predictions is not right or there are problems with the people involved.

When you do this, you can fix your predictions if they're not right. This makes your predictions more accurate.

To do this, sales leaders should set up a way to test and check their predictions often. They should look at how well their predictions did every three months and check for any mistakes.

By checking their predictions from different angles, like by looking at each salesperson, different types of products, and where the customers are located, they can find out where they need to do better with their predictions.

Accounting for seasonality trends

A vital part of making accurate revenue predictions involves considering the ups and downs that happen at certain times of the year.

Many forecasting methods rely on past data to guess what might happen in the future. To get the most precise forecasts, it's crucial to account for seasonal changes.

In most industries, it's common to see revenues go up and down throughout the year. These changes are connected to yearly shifts in how people buy things and how much money they have to spend.

To be ready for these ups and downs, you should make sure you have what you need when revenue drops. And if revenue goes up, your organization should be ready to take full advantage of the opportunity.

To deal with this, it's important to look at past sales data to find patterns that repeat every year. Additionally, include dates like holidays and annual budget times in your prediction models.

You should also keep updating your assumptions related to seasonality as your business changes. When you adjust your models for the seasons, your teams can be better prepared for changes in revenue. They know what to expect.

Relying less on rep input for bottom-up forecasting

Another good way to predict how much money a company will make is to rely less on what the salespeople think. Instead of just listening to their positive or negative feelings, it's better to look at their opinions in a more objective way.

Salespeople, like everyone else, can have personal views that might not always be accurate. Some of them might be very hopeful, while others might be more cautious.

Other things, like where a deal is in the pipeline, how much money they can earn from commissions, and recent successes, can also affect how they see their sales.

To make more precise predictions, you need to combine what salespeople say with data analysis and good judgment. Here are some good methods:

Use a set of rules that salespeople follow to decide if a deal is good or not, based on facts like where the lead came from, how the deal is progressing, and what the buyer is doing.
Regularly check what salespeople say by looking at past results, how long it usually takes to make a sale, and other information. This helps make sure their predictions are realistic.
Make sure what salespeople say is right by closely looking at their deals. Your input helps balance out their optimism or caution.
Mix what salespeople say with mathematical models that use past performance to predict future results. This combines both what people think and the numbers.

In short, the idea is to use salespeople's experience while reducing their personal opinions. By using a combination of what they say, data analysis, and reviewing their work, you can make more accurate and fair predictions.

Updating assumptions regularly

It's a good idea to frequently update the assumptions that form the basis of your revenue forecasts to ensure they are accurate.

Assumptions can quickly become outdated in today's fast-changing business world.

Many companies make revenue predictions based on a fixed set of assumptions and don't revisit them as time goes by. This causes forecasts to become disconnected from reality as the market changes.

To fix this, you can set up a regular schedule for the sales and finance teams to review and update these key ideas, like every three months.

It's also important to write down why you are changing any of these ideas. This helps keep the process transparent and honest.

In summary, regularly refreshing your ideas about the future ensures that they match the current trends in the market. This is an important practice for becoming better at making revenue forecasts.

Ensuring alignment between sales leadership and finance

One of the best ways to improve forecast accuracy is by making sure that the sales team and the finance team work together. The sales team sets goals for selling products, and the finance team keeps track of how much money the company has made.

When these two groups work well together, it leads to accurate predictions that people can trust.

To address this, organizations can take some important steps to make sure that the sales and finance teams are on the same page. The leaders of both teams must talk to each other and keep an open line of communication.

One way to make sure that the two teams work together is to create special committees with people from both teams.

These committees give the teams a chance to talk about any potential problems, unusual situations, or changes in their predictions that could affect the company's financial outlook.

Another strategy is to make the finance team aware of the money earned from sales earlier in the process.

You can do this by setting data-based goals that salespeople need to meet before counting a sale. For example, sales reps might have to complete product demonstrations or get approval for their proposals.

This helps the finance team see what's happening with the company's income sooner in the sales process.

In general, it's very important to make sure that the goals set by the sales team match up with what the finance team recognizes as actual income. This is crucial for keeping the company's financial predictions accurate.

Organizations that encourage collaboration and communication between these teams, using committees, open discussions, and data-based goals, are better prepared to make trustworthy predictions about their income.

Improve your revenue forecasts

Predicting revenue is difficult but extremely important.

Use better tools instead of spreadsheets, and check your predictions often. Try using modern technology like Clari for better predictions. Understand your business situation and pick the right models.

Talk to an expert to see how a specialized platform can help with your predictions.

Book a demo today to see how Clari can improve your revenue forecasts. We're #1 on G2 for revenue forecasting.