The Revenue Forecasting Guide and Best Forecasting Models

It all boils down to your business when it comes to revenue projections. Not only the future of your company but also the precise location, industry, and services you ultimately offer customers. You must create revenue predictions to evaluate your business's status going forward to establish an accurate budget equivalent to your company's annual strategy.

What is Revenue Forecasting?

Revenue forecasting is the process of estimating future revenue based on past performance and current trends. Forecasting is necessary for any business plan because it provides direction for decision-making, including budgeting and resource allocation. Without accurate revenue projections, it would be difficult to make informed decisions about where to allocate resources to achieve desired. A revenue forecast, for example, might highlight where you're going at your current rate if you want to know how much money you'll make next month, quarter, or year.

Why Is Revenue Forecasting Important?

Revenue forecasting is an essential tool for all businesses, regardless of size or industry.

There are several advantages to predicting your revenue. Revenue forecasting is all about putting your company in a position to face whatever the future may bring so that you're not caught off guard and can make the most informed decisions possible to develop your business.

Here are a few major reasons to forecast revenue.

Create a Realistic Financial Plan

Personal and business finances are both concerned with managing your money. Personal finance involves creating a budget based on your income. You know you can't spend more than $5,000 every month if you get paid $5,000 per month from your job. You can budget for everything from food to going out and other variable expenses as long as they are within your means.

However, a company's revenue is seldom consistent each month. Your income can vary depending on how much you sell, whether or not you have churning customers—if you're a subscription firm, and market conditions. It's tough to plan for daily operations like marketing or new expenditures like recruiting employees when you're not sure how much money you'll have coming in.

Forecasting allows you to bridge the gap between your projections and reality, particularly for operating costs. Your forecast provides a prediction of how much money you'll make in the following few months or years. This will help you forecast how much money you can set aside for marketing efforts, new employees, software purchases, and other spending that fluctuates over time.

Anticipate a Ramp-up in Hires

I've already gone over new employees in the previous section, but it's worth mentioning it here. Hiring is distinctive because, unlike many other costs, it usually needs to be planned several months ahead, and your revenue significantly impacts your hiring choices.

When recruiting a new employee, you need to be sure that you'll be able to afford them long-term, not just in the near term. If your revenue forecast shows growth over the next year, you may feel more confident in being able to add members to your team.

While both are true, they aren't the whole picture. If your revenue projections drop or slow down, you may need to scale back on employee growth.

The Top 4 Forecasting Methods

The most common techniques financial analysts use to forecast a firm's future revenues, costs, and capital expenses are straight-line, moving average, simple linear regression, and multiple linear regression. While there are many different quantitative budget forecasting tools in use today, we'll focus on the top four methods: (1) straight line, (2) moving average, (3) simple linear regression," and (4) multiple linear regression.

Straight-Line Forecasting Method

When the growth rate remains consistent, this approach is frequently employed to get a simple picture of constant expansion at the same rate. It only uses basic arithmetic and past statistics. Ultimately, it gives predictions for future development that may help you with financial and budget goals.

An Example of Straight-Line Financial Forecasting

The growth rate of a restaurant chain has remained stable at 5% over the past three years. The business expects its expansion rate to continue at that level for the next two years. By adding 5% to this year's growth and 5% to the following year's and recording those results as the preceding year's growth plus one, the company may make reliable predictions about how many new workers it will need to hire in each of those years and how much additional payroll money they will require.

Moving Average Forecasting Method

A moving average is a form of trend analysis that compares the current performance in shorter time periods to that of previous periods. It isn't utilized over longer durations, such as years, because it creates too much lag to be helpful in trend following.

Using this technique, an average of variables with significant movement, like stock prices, and values with frequent but slower changes, such as inventory levels during peak retail seasons, can be continuously updated.

In a nutshell, this strategy is used to look for underlying patterns that can be used to evaluate common financial measures such as revenues, earnings, sales growth, and stock prices. A downtrend is indicated by a dropping moving average, and a rising moving average shows an uptrend.

An Example of Moving Average Financial Forecasting

A retailer wants to figure out how much product if any, he needs to reorder from a wholesaler. Sales are doing well overall because it is the holiday season, but he needs to know which goods are rising in popularity. He produces a moving average for the week to tell him the trend and guide his inventory buy orders rather than trying to watch irregular upticks and declines in a particular product's sales each day or over a week.

Simple Linear Regression Forecasting Method

The connection between a dependent and an independent variable is utilized to draw a trend line. An analysis using linear regression connects changes in an explanatory variable on the X-axis to changes in a dependent variable on the Y-axis. The relationship between the X and Y variables generates a graph line representing a trend that often swings upward or downward or remains stable.

An Example of Simple Linear Regression Financial Forecasting

Two factors crucial to every firm's success are sales and profits. If the trend line for sales (x-axis) and profits (y-axis) rises when used with simple linear regression, then everything is fine for the firm, and margins are robust. If sales are up while profits are down, something is wrong; perhaps there are increasing supply costs or tight margins. Despite this, if sales are down but profits are up, the product's value rises. This indicates that company costs/expenses have decreased and that the linear regression model is functioning well—when profits rise, margins improve as a percentage.

Multiple Linear Regression Forecasting Method

This method makes a forecast using more than two distinct variables. A model of the relationship between the main explanatory variables (parameters) and the dependent response variable is essentially created using much linear regression (MLR) (outcome).

An Example of Multiple Linear Regression

A trucking company executive wants to forecast gasoline prices for the following six months. The EIA Gasoline and Diesel Fuel Update, oil futures from a futures exchange, mileage from GPS fleet routing systems, traffic patterns from smart city open data platforms, and the number of trucks the company anticipates will be on the road during the period based on delivery orders are the independent variables used for this method. This list is provided for illustration reasons only; other factors may also impact the outcome.

In each scenario, all of the variables not only affect the outcome but are also independent of it. Based on the factors, this model makes predictions about the result, in this example, the anticipated gasoline prices for the time period.

The Do’s and Don’ts of Revenue Forecasting

Let's look at some best practices for creating an accurate revenue forecast. Here are some dos and don'ts for revenue forecasting.

Do Use Data for Your Assumptions

Data has probably been a recurring pattern in this article. Many entrepreneurs make the error of basing their income predictions on their most optimistic assumptions. The issue with that is that it may cause you to grossly overstate your sales figures, which might be disastrous for your company.

Making decisions based on those projections is just slightly worse than overestimating your revenue. Data is your friend!

Don’t Try to Design the “Ideal” Forecast

There is no flawless forecast. It's impossible to anticipate precisely how much revenue you'll have in three months, let alone 1-2 years from now, even if you go over every detail with a fine-tooth comb.

Anything could happen between now and three months from now. Your entire industry could be affected by the emergence of a new rival. If your product becomes popular, you can experience an increase in sales. You run the risk of having prolonged flat growth. The idea is that commerce is fluid.

Revenue projections aren't intended to be accurate future forecasts. They're designed to provide you with direction so you can decide more wisely.

It is not the best use of your time to attempt to forecast every cent you make during the upcoming quarter over days or weeks. Instead, make every effort to make your prognosis as accurate as possible and then make changes as necessary.

Do Update Your Forecast Frequently

The revenue prediction you create at the beginning of the year shouldn't be abandoned to gather dust.

You should adjust your revenue prediction as circumstances in your company change.

For example, your original prediction may have anticipated the execution of 3–4 targeted marketing efforts throughout the year. However, after conducting two, you might alter based on the outcomes. Your revenue prediction will be affected by anything, including trying a new channel, improving the conversion of your advertising, and lowering your performance goals.

Your business model will determine how frequently you should update your revenue forecast. While a more established business might revise its projection every three months, an early-stage startup conducting extensive testing to learn the ropes might need to make revisions every month.

The most crucial thing is to avoid treating your revenue prediction as a static record. It can be a helpful tool for progress if you frequently monitor and analyze it.

Don’t Create Your Forecast All by Yourself

Revenue forecasting includes input from several people unless you're a one-person company.

You can learn about marketing's upcoming initiatives from them to generate leads and sales. You may learn more about the funnel and sales velocity from sales. You can get advice and information from everyone active in bringing in and keeping customers. If you're not as "in the weeds" with sales and marketing on a daily basis, this can be useful.