Key Considerations in Modeling Early-Stage Ventures

The Startup Station was created to help you model ventures with no financial history. In this article and corresponding video, we discuss our approach in more detail.

Modeling an early-stage venture is especially hard because there is no baseline for any financial estimates. Yet, to position your startup as an attractive investment, you must show growth in revenues and profits. The question is how fast they should grow and where that growth comes from.  

In our opinion, the main difference between modeling early- and late-stage companies is the level of granularity in financial projections required to justify the resulting financials. The earlier stage a venture is, the more detailed strategy representation is required for a financial model to be credible.

Let’s consider revenues.

For a well-established company, such as IBM and Google, there is no need to model HOW exactly they generate their revenues and it’s enough to forecast revenue growth rates directly. The reason is that they have a proven record of producing results and there is enough financial history to see what those results might be. The only two questions any research analyst must answer are:

# 1 How the past trends will change given the current company’s strategy, which may include launching new products or business lines, expanding into new markets, cost cutting, or M&A activity, and

# 2 How confident they are that the company’s management team can execute on the new strategy.

Unfortunately, just modeling revenue growth rates does not work for a startup because it does not explicitly show where this growth comes from. In the absence of any financial history, the absence of revenue-generating logic makes any resulting numbers unjustifiable. 

That is precisely why you need to provide a higher level of granularity in modeling those companies and think about the concrete actions a company must take in order to generate revenues. Those actions will include its go-to-market strategy as well as other business-model specific considerations.

There are two types of assumptions you will need to make: discretionary and non-discretionary.

Discretionary assumptions are in your control and may include pricing strategy, the size, and allocation of your marketing budget, and the launch timeline of your revenue streams.

Non-discretionary assumptions are those you can’t easily affect. They may include conversion rates from different marketing channels, a conversion from a free trial to a paid customer, and renewal rates… They would eventually come from your data, but can initially be estimated based on industry averages, preferably for companies at your stage of development. If such data is not available, you can make the industry rates more conservative to match the results you may expect to realize as a young company.

The outcome

This process allows you to formulate the underlying logic for your business model and financials. The resulting financial model is not only your negotiating tool with investors which accelerates the funding process, but it is also an analytical aid you can use to build a successful business from the start.

Victoria Yampolsky, CFA, is the President and Founder of The Startup Station, a comprehensive resource for modeling and valuing early-stage startups. She evaluates the financial feasibility of business models and specializes in the financial modeling and valuation of pre-revenue companies. She also created a finance curriculum for early-stage founders and launched The Startup Station’s educational program in 2015. Since then, more than 1,000 founders have attended her online and in-person finance classes and learned the basics of financial modeling, valuation, and startup financing.

Previously, Victoria worked for the Deutsche Bank Research Department and performed IT consulting for CapGemini’s Financial Services Division. Victoria holds a Bachelor’s Degree, Cum Laude, in Computer Science, with a minor in Mathematics, from Cornell University and an MBA, with honors, from Columbia Business School. Victoria is also on the Advisory Board of the Computing and Information Science (CIS) Department of Cornell University.