The Complete Fundamental Guide to Revenue Forecasting

Revenue teams

Revenue forecasting predicts the trajectory of revenue over a period of time. It has a profound impact on business strategies and decisions in the long and short term.

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Priyaanka Arora
September 30, 2022

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The Complete Fundamental Guide to Revenue Forecasting


In highly competitive markets, revenue forecasting transforms from a standard business process to one of the key defensive strategies to protect the growth trajectory of the company.

With substandard data quality and the lack of collaboration between departments, revenue forecasts can lead to missed targets, poor decisions, and losing out to competitors.

It is crucial to understand the layout of the revenue forecasting process before attempting to streamline it. This article takes you through a refresher of revenue forecasting fundamentals that help build a solid foundation or accurate and repeatable forecasting.

What is revenue forecasting?

Revenue forecasting is a process typically taken on by Revenue teams to predict the trajectory of the company’s revenue generation over a specific period of time. This process has a profound impact on high-level business strategies and influences decisions in both the long and short term.

In general, the process involves extrapolating a projection of the company's revenues from various sources in addition to revenue from sale of the core offering of the company. In this way, revenue forecasting differs from sales forecasting, which focuses purely on revenue generated by  the sales cycle.

Why is revenue forecasting important?

Put simply, revenue forecasting is important because it helps determine the company's future direction. It enables leadership to make strong and effective plans for the future. 


Revenue forecasting enables companies to determine budgets for departmental activities. It enables the company to be better prepared for the immediate future and acts as a reference point to compare performance during the given period.

Additionally, revenue forecasting also helps track expenses undertaken during the period. To enhance the utility of revenue forecasting, it is essential to ensure the accuracy of the forecast, via revenue and planning platforms like Pigment.

Cash Flow Management

Revenue forecasting is primarily undertaken to optimize the business's cash flow and credit management practices. Through revenue planning, companies can effectively monitor the cash flow of the business to ensure that payments are received and made per the schedule. 

This will help avoid unintentional delays in clearing vendor invoices, improving the business's creditworthiness. Revenue forecasting additionally helps obtain the best terms from vendors.

Informed Decision Making

Revenue forecasting enables business leaders to make informed decisions about crucial activities including recruitment. This activity empowers leadership to make knowledgeable decisions based on data. Businesses can accurately predict how many new employees must be hired and how much they can be paid based on the business's financial outlook.

Overview of revenue forecasting methods

Usually, most Revenue teams create a customized revenue forecasting model based on the features of multiple models. This process helps the company create the most accurate revenue forecasting model per its requirements.

Some of the most common revenue forecasting methods are:

Backlog Revenue Forecasting Model

This model requires Revenue teams to ascertain the expected revenue for the company based on contracts instead of the revenue received.

In this model, companies are less worried about factoring in the uncertainty. Instead, it requires distributing revenue growth over a period. The best approach under this model is to determine the standard run rate for the team during the period and then divide it by the total revenue. 

This helps the company understand how long it will take to earn backlog revenue. This model is best for companies needing a high-level view of their growth rate and is likely to be engaged in a similar line of work to the past.

Bottom-Up Forecasting

This model enables Revenue teams to create a schedule of their planned tasks and then match it with the optimal resources required for a project.

Planned tasks can be projects that are either in the pipeline or in the execution phase. This model helps the company create a time-phased revenue forecast for the scheduled work to determine the capacity of the company to complete the work on time.

As a result, the team can better understand the project pipeline and balance the resources with the projects. This model must be updated regularly based on the stage of the project.

Pipeline Revenue Forecasting Model

This model requires the tracking and measurement of the sales pipeline for a company. It is assumed that a certain percentage of the sales forecast would transform into real projects. 

Moreover, leadership must also estimate the deal size and the expected pipeline coverage. Most businesses consider the pipeline coverage gap as an opportunity to factor in the revenue forecast over a period.

How do you create a revenue forecast?

There are various revenue forecast models available for any organization to choose from. Most of them require the decision-makers to follow certain steps to successfully create a well-defined forecast model:

  • Determine the timeline for the revenue forecast.
  • Ascertain the factors that may boost or hinder the revenue growth during the period.
  • Factor in the estimated direct and indirect expenses for the period. 
  • Predict expected sales during this period based on past and present trends. 
  • Combine expected sales and estimated expenses into a forecast. 
  • Use important financial ratios to ascertain the success rate for the forecast.
  • Adjust variables to test different scenarios to complete the revenue forecast model.

What are forecasting tools?

An increasing number of businesses are looking to access sales and revenue forecasts simultaneously because of their interlinked nature.

Unfortunately, a majority of these forecasts feature errors caused by manual data manipulation. This is why many companies are now integrating the latest business planning and forecasting platforms to boost the accuracy of revenue forecasts. 

Platforms like Pigment allow decision makers to run multiple what-if scenarios in minutes to ascertain the key factors affecting the business. Moreover, it is also possible to compare scenarios based on changes in assumptions over a period. These platforms help the company boost the accuracy of predictions as all the data has been gathered in one place.

How can revenue teams improve forecasting speed and accuracy for revenue growth? 

An integrated business planning platform helps Revenue teams improve their forecasting speed and accuracy to boost revenue growth:

  • Reduce planning cycles: Revenue teams can focus on core operations and eliminate time-taking manual tasks. They can gather and enrich the data in a few minutes to boost the accuracy of predictions.
  • Accurate and relevant answers: Revenue teams can model faster with significantly fewer formulas than spreadsheets. Changes can be made in minutes with a few simple clicks. 
  • Flexible planning and forecasting: Revenue teams have the full flexibility of building models from scratch, allowing them to configure and customize the structure and metrics per evolving requirements. It is also exceedingly easy to make changes on the fly to models, with Pigment’s cellular architecture.
  • Plan in advance: Revenue teams can stay nimble and run multiple what-if scenarios within a few minutes to help plan key decisions in advance. 

Revenue forecasting can be described as an ongoing process for predicting the revenue that the company aims to generate during a given period. This forecast then forms the base for most of the financial business decisions. To improve the accuracy of revenue forecasting activities, it is essential for the company to first create an underlying planning and revenue forecasting model based purely on factual data.

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