How to Calculate Average Revenue

And what it can tell you about a business

If money makes the world go round, businesses better have a good idea of how fast it’s coming in. Monitoring and analyzing where revenue comes from creates insights allowing businesses to scale up (or back) and report earnings to shareholders.

For some, this is as straightforward as selling lemonade on the corner. But most businesses experience fluctuations in sales or revenue from seasonality, product development, the economy, customer trends, marketing campaigns, and other factors. These ups and downs make it difficult to see macro trends or offer a clear view of performance.

How do businesses level out the numbers to track the amount of income each unit, user, or subscriber generates? They use average revenue.

What is average revenue

Average revenue is a measurement that creates a single number showing sales performance over a period of time. These measurements help financial advisors move past “it’s complicated” when talking about the ups and downs of sales performance by leveling revenue and tells us a lot about how a business competes in its industry.

Average revenue is often expressed as ARPU: average revenue per unit, or average revenue per user. Average revenue per unit creates a measurable number for products (i.e., units), while average revenue per user shows sales performance for active customers (i.e., users).

Knowing your ARPU creates clarity behind your customer base and products, allowing financial advisors to project a company’s revenue-generation capabilities.

Calculating average revenue

Average revenue per unit/user (ARPU) is calculated as total revenue divided by the number of units sold, users, or subscribers over a given period. The time period is generally defined based on the business model.

For example, an app may have many users downloading and deleting the app each day, so calculating average revenue on a weekly or monthly basis may make sense for them. A B2B software company, however, may have a much longer sales cycle and subscribers will change much less frequently.

The calculation for average revenue is simple. If a financial advisor wants to analyze average revenue for the quarter, they would use the total revenue and total units sold for that quarter. So, if a company sold 100 units and had a total revenue of $10,000, the average revenue per unit would be $10,000/100 for an average of $1,000.

You may see the formula expressed as AR = TR/Q, where “TR” means total revenue and “Q” means quantity. If you want these calculations built for you, a platform such as Causal allows you to build formula and connect to databases that can populate the data you need, when you need it. This can save a lot of time gathering data from around the business systems and teams while reducing errors in transcribing information.

Remember, this formula calculates an average. It doesn’t matter if the company had zero users in the first month of the quarter and 10,000 in the second and third months — the results will only show us the average for the whole quarter.

Analyzing average revenue

Businesses should monitor average revenue on an ongoing basis and track it over time to see how the business is growing and stacking up against the competition. ****Average revenue is generally most useful for companies that don’t sell simple products and therefore can’t calculate a standard margin.

For instance, consider a telecommunications company. Each customer may generate different revenue each month based on their phone plan, devices, and any overage fees. The situation is very different from a manufacturer that produces one product and knows the margin associated with each unit.

When analyzing ARPU, it’s important to look beyond the numbers and form qualitative insights based on what you know about the business and its customers. If a company has a large number of latent users that aren’t using the product but haven’t deleted it or unsubscribed, those users can distort the estimate of average revenue. Consider any factors that could skew the data and remove those factors if possible.

ARPU is often used by companies to analyze the monetary value of each user. It enables them to understand how different subscription levels, premium add-ons, and other features affect the revenue generated by each user.

For example, it’s possible for a less expensive subscription tier to generate more revenue than a higher tier if users are purchasing upgrades or expansion packs. This information is important for companies to know, because it will influence priorities and marketing efforts.

While revenue should increase as a company grows, it’s not uncommon for average revenue ****to decrease. This is because companies are able to offer lower prices when they are generating higher sales, thus positioning the business to be more competitive or even win market share.

However, this isn’t true for all businesses. Some have a very consistent average revenue that doesn’t change based on quantity sold. If you are familiar with economics principles, you may realize that a business’s average revenue curve will generally match its demand curve. ****

A must-have for your financials

Average revenue helps executives and stakeholders analyze how the business model is evolving over time and provides a basis for comparison to competitors. It’s a tool every financial advisor needs to not only calculate, but analyze.

ARPU tells a business how much it is earning per unit sold, user, or subscriber for a given time period. It’s especially helpful for businesses with customer tiers or seasonality that brings lots of swings to revenue. It removes variables to make it easier for a business to directly compare its own revenue per unit to competitors and understand its positioning in the marketplace. When analyzing ARPU, financial advisors must look beyond the calculation to consider the full picture. An unexpected change in average revenue indicates a change in behavior that warrants further investigation. Whenever you’re using an average over time, it’s easy to miss red flags that could skew the data (such as a large base of latent users or poor sales performance).

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