Breaking Down The Startup Revenue Forecast
By Lior Ronen (@Lior_Ronen) | Founder, Finro Financial Consulting
In the course of raising funds, every business in every stage in every industry is asked to show potential investors the projected financials of the business, or “financial model” as others may call it. This makes sense, of course, because investors need to have a preliminary understanding of the financial state of the business, its growth potential, its possible ROI, and the different revenue and cost drivers.
But what information should a financial model actually include? How do you build a revenue forecast?
A FINANCIAL MODEL IS NOT A ONE-SIZE-FITS-ALL SOLUTION
The structure of a financial model should start from the top and gradually extend into the drivers and inputs of the inherent components.
From the start, we should have a clear understanding of the main driver behind the model. Let’s assume we’re looking to raise funds for our early-stage startup.
At this point, we need to think about the main focus of the model. The focus of the model, and the expectations that arise from it, vary greatly according to the specific industry, business stage, and target audience. For example, a mature enterprise software company would emphasize how it retains clients and attracts new clients while concurrently introducing new offerings and expanding into new markets. In this case, costs will play an important role in how the company’s business decisions impact clients and profit margins, in the cost of retaining clients or attracting new ones, in whether the level of R&D expenses is reasonable for the level of revenues the company generates, and more.
However, for an early-stage startup with little or no revenue streams, it’s more important to show how that startup will build its user base and generate and grow its revenues when a discussion about costs is framed around the resources needed to meet the milestones.
From time to time, founders approach me after they have worked with a “financial modeling expert” who took the information they sent and entered it into their own template, which subsequently regurgitated a model that could fit energy companies, retail chains, brick-and-mortar retail stores, and restaurants alike—basically any business in the world. While this probably saves time and effort on the expert’s side, and some costs on the client’s side, the outcome is mostly useless and fails to recognize/identify the unique selling points (USPs) of the business.
In this post, I will focus on the most important aspect of financial modeling for early-stage startups: the revenue forecast.
TWO ELEMENTS THAT DRIVE REVENUE IN EVERY BUSINESS
The most fundamental issue that I typically explain to founders might at first sound dumb or obvious, but it’s often overlooked that revenues in every business and industry across the world are driven by two elements alone: quantity and unit price. That’s it. Let’s start with a very simple example of a SaaS company offering monthly subscriptions via its website or app.
ELEMENT #1: QUANTITY OR USER BASE
The quantity is the number of users buying the license every month and the unit price is the monthly price of the license. Of course, the number of users that will buy the license every month is not stable or static: some users will leave, others will join, and the majority of the users will remain. The total of these user base elements is the quantity for calculating the revenue.
Calculating the user base gets trickier as the business model evolves. If the SaaS company works on a freemium model, then the revenue forecast should include a separate forecast for the free users according to the dynamics above and for the conversion rate (i.e., how many users that converted to a premium plan every month). If several premium plans exist, the revenue model will have to take into account different conversion rates when every one of them feeds into a different premium plan. Of course, every free and premium plan will have its own growth rate, churn rate, and conversion rate. Even though this is a slightly more complicated case, the basic assumptions and rationale remain the same.
ELEMENT #2: UNIT PRICE
In the simplistic example above, the unit price is the monthly license fee, which reflects the price every user pays each month. In real life, however, pricing is a bit more complicated and the user base metrics should be aligned accordingly.
First, most SaaS companies have different price points. Price points could differ by the features included (good/better/best approach) or by billing frequency, i.e. monthly vs. annual plans. The user base should account for users with different price points and plans as each one of these categories will have different growth rates, conversion rates, churn rates, and prices.
It might seem like the unit price is fixed throughout time, but we know this is not the case. So a good revenue model should reflect price fluctuations that are usually linked to the launch of complementary products, additional features, new price plans, and so on. Putting all of these aspects together leads us to the unit price, which, when multiplied with the relevant user base figures, will yield the revenues forecast for the SaaS company.
Building a solid revenue infrastructure constitutes the basis for every advance feature you might later wish to add to the model. For example, it would enable the addition of another layer to the model—scenarios that will reflect different revenue levels due to either fluctuations in the user base assumptions or fluctuations in the unit price assumptions.
It’s important to remember that every company is different. Every business has its unique dynamics, business drivers, costs, pricing metrics, and business model, and every business should be treated and modeled differently. Keeping in mind that simplicity is a key element to any startup’s financial model, take some time to understand how to structure a model that will be simple to follow and understand and that best reflects your business model and growth potential. Build a model that will have a clear set of assumptions that are manually inputted that will enable you to amend the assumptions as the business evolves.