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Five Most Common Financial Modeling Mistakes

January 15, 2023

Having reviewed hundreds of financial models across more than fifteen industries, I’ve identified five most common mistakes entrepreneurs make when creating their financials. In this article and the corresponding video, we discuss each of these mistakes in more detail so that you can learn how to model your company correctly and get funded faster.

  • Mistake #1: Hardcoding financial projections

Hardcoding expected financial performance is the go-to strategy for many founders with no financial experience, especially when it comes to projecting sales. This approach is incorrect because 1) you do not know where the data is coming from and this makes it impossible to validate the numbers and 2) you can’t see the financial impact of changes in your assumptions.

Only the underlying factors that impact revenues and costs, also called revenue and cost assumptions, should be hardcoded. These assumptions come from your strategy and industry standards. Please see our blog post on how to create credible financial model assumptions for more info.

You can also sign up to The Startup Station’s mailing list at www.thestartupstation.com and receive a code to download for free our Business Model Analysis Roadmap, a step-by-step guide to creating financial model assumptions.

  • Mistake #2: Using random number generation to create projections

Your financial model is a quantitative representation of your strategy and a tool to measure its effectiveness. Your financial goals, specifically sales goals, are not random and thus should not be randomly generated.

Instead, you should figure out what your goals are, think through how you are going to achieve them, and formulate metrics that will measure the effectiveness of your strategy.

  • Mistake #3: Not having a financial summary

A financial summary is a snapshot of your financial model that highlights all key assumptions and results. Usually a financial model has many parts and it can take an investor a lot of time to decipher the key findings and the underlying logic. Thus, to save investors time and to make their due diligence process easier, I recommend that you always have a financial summary. In addition, it will help you determine what to focus on when you present your financials.

  • Mistake #4: Calculating funding needs without using your financial model

Your funding needs must come directly from your financial model. In many cases, founders determine their financial needs based on the perception of what they need, rather than a robust financial plan. This is wrong and leads to a lot of unnecessary questioning by investors.

  • Mistake #5: Improperly aggregating your financial data into the three financial statements

There are three main financial statements: Income Statement, Balance Sheet, and Cash Flow Statement, and it’s important to create all of them properly. Many entrepreneurs know how to put together an Income Statement that shows how much profit they project to make, but are confused about how to generate the other two statements. The Balance Sheet is a snapshot of the company’s financial position and the Cash Flow Statement details how the cash is used between the operating, investing, and financing activities. It is critical to have your financial model built properly so that investors take you seriously and you have a complete picture of your business from the financial point of view.

  • About Author

Victoria Yampolsky, CFA, is the President and Founder of The Startup Station, a comprehensive financial resource for 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.

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