7 Common Financial Modeling Mistakes You Don’t Want To Make

Financial modeling is an essential part of running a successful startup. From budgeting to investment analysis, a solid financial model can help founders make informed, data-driven decisions about their businesses.

However, creating an accurate and reliable financial model can be a real challenge for founders, especially if they’re not experts in finance or Excel. Even experienced founders are prone to some common mistakes that can damage the accuracy, reliability, and usefulness of a financial model.

If you’re going to put in the time and effort to build a financial model, you owe it to yourself to avoid these pitfalls. Below, we’ll walk you through 7 of the most common financial modeling mistakes we see founders make. Follow our advice and steer clear of these “gotchas” at all costs!

Table of Contents

• Mistake #1: Incorrect Formulas
• Mistake #2: Data Errors
• Mistake #3: Overcomplicating the Model
• Mistake #4: Ignoring Key Assumptions
• Mistake #5: Failing To Test the Model
• Mistake #6: Outdated Data
• Mistake #7: Inadequate Documentation

Mistake #1: Incorrect Formulas

Financial models are built with math. They’re a composite of calculations and formulas, and even the smallest mistake in a formula can cascade through the model to significantly impact its accuracy.

Some of the most common errors we see include using the wrong formula altogether, using the wrong cell references, and simple arithmetic errors such as adding instead of subtracting.

If you’re building a spreadsheet financial model, the spreadsheet software probably has built-in features to identify invalid formulas, but there’s no protection against referencing the wrong cell or accidentally choosing the wrong function.

Most spreadsheet applications have a formula auditing tool that allows you to trace the precedents and dependents of each formula, and we recommend using those to identify and troubleshoot errors.

At a minimum, you should double-check all formulas and calculations before you finalize and share your model. It’s especially helpful to have a colleague or supervisor review the model - a fresh set of eyes can often identify errors that you’ve overlooked.

Pro Tip: Be sure to use consistent formatting and units of measurement throughout your model. Also, use a consistent method for data entry. It’s much easier to spot problems when all of your data is consistently formatted.

Mistake #2: Data Errors

Your data is the foundation of your financial model. Even a small error in your data will have some impact on your metrics and projections. Some of the most common data errors we see include omitting available data, incorrect data due to errors in data entry, and data entered in an inconsistent format.

To avoid data errors, it’s essential to work carefully and continually review the data you’ve entered. This includes verifying that the data you’ve entered is accurate, complete, and entered in the correct format. If you’re struggling to keep your data consistent, consider using the application’s native data validation tool, or an add-on, to identify errors and outliers.

Keeping your data properly organized and labeled is really helpful for effective data review. This is as simple as clearly labeling every data point, using consistent naming conventions, and organizing the model in a logical way. This makes it easier to identify errors and troubleshoot problems when they arise.

In addition to periodically reviewing your data for accuracy and consistency, you should also be selective about what data you allow into the model. Data from unreliable or irrelevant sources can lead to inaccurate forecasts. Just make sure that the data you use is relevant, reliable, and up-to-date.

Pro Tip: It’s very helpful to document all of your data sources and assumptions within the model. This makes it easy to track down the source of any numbers you’re not sure about, and understand how it has been used in the model. This will speed you up considerably when you’re troubleshooting issues, and when you’re updating your model in the future.

Mistake #3: Overcomplicating the Model

Financial models can quickly become complex and difficult to understand, but you need yours to be clear and insightful to investors and stakeholders who aren’t familiar with your day-to-day operations. Protect the simplicity of your model from day one. At every step, check to make sure the data is presented as simply as possible so that it’s easy for outsiders to understand.

One way to avoid overcomplicating your model is to decide early on what your key drivers and metrics are, and then take care that the model remains oriented around those key factors as you build.

Review all data before you allow it into your model and avoid unnecessary details that don’t impact those key metrics. If it adds value, add it to the model. If it adds complexity without adding value, leave it out.

One of the best ways to keep your model simple is to break it down into smaller, more manageable components. This makes it easier to understand how each component impacts the final result and also makes it easier for you to create “what if” scenarios by adjusting specific inputs.

Obviously, charts and graphs can help outsiders visualize complex data and make it easier to understand. Create visuals for the high-level, high-value metrics you want to emphasize with investors and stakeholders.

Pro Tip: The biggest thing you can do to keep your spreadsheet model simple and logically organized is to use a well-structured template. Forecastr offers free spreadsheet templates to fit any business model. Enjoy!

If you want a rock-solid financial model that will impress investors and help you make better decisions every day, consider using a dedicated financial modeling solution like Forecastr. Our team of financial analysts will work side-by-side with you to make sure your model is rock-solid and always ready for fundraising.

Mistake #4: Ignoring Key Assumptions

Every financial model is built on a set of assumptions. They drive the projections and forecasts for your key metrics, and failing to include or consider key assumptions can lead to inaccurate results.

Examples of assumptions include conversion rates, prices, and costs. If you fail to consider future price changes, fluctuating material costs, or salary raises for your team, your projections about the future will be less accurate.

To avoid this mistake, it’s important to get your team involved, document all of your assumptions, and review them regularly to ensure they’re still valid.

Financial analysts perform sensitivity analysis on the assumptions in a financial model to identify which variables have the greatest impact on future performance. If you document all of your assumptions, you can do the same thing. 

What would happen if you doubled your price, but cut your conversion rate in half at the same time? Would you be in a better or a worse position? Sensitivity analysis answers questions like this and helps you identify the most important drivers you can manipulate to improve your performance.

Documenting your assumptions also allows you to easily create “best case” and “worst case” scenarios, where you game plan your response to different situations. 

Pro Tip: Get your team together for a competitive review. Analyzing your competition’s strengths and weaknesses will reveal areas where you should include variable assumptions to game plan your strategy, make up for shortcomings, and pour fuel on the fire where you’re already winning.

Mistake #5: Failing To Test the Model

Thoroughly reviewing and testing a financial model before sharing it or putting it into use is essential to ensure its accuracy and reliability. Failing to do so can result in bad decisions and lost investment deals.

Before you call your model complete, you should carefully review and test every formula, calculation, and assumption. Test the model by changing assumption values and ensure that it is creating accurate projections for future values.

Check your data for accuracy, check your assumptions to make sure they’re set correctly, and check your formulas to make sure they’re working. While you’re building your model, it’s helpful to create a checklist of all components you want to circle back and double-check after your build is complete. 

A thorough review of the entire model can help you identify errors and omissions before you put the model in front of investors and stakeholders when it needs to shine. 

Ask your co-founders and colleagues to get involved in the testing process. By exposing the model to fresh eyes, you can do a dry run of presenting the model to an investor - see what jumps out at them, see what confuses them, and adjust the model accordingly. 

Pro Tip: Reviewing and testing your model shouldn’t be a one-time occurrence. Models can go stale if you don’t update them regularly. And, if you’re not checking your projections against your actual performance on a regular basis, you’re leaving a ton of value on the table. Much of the benefit of financial modeling comes from regular use and review.

Mistake #6: Outdated Data

Outdated data in your financial model causes inaccurate forecasts and uninformed decisions. This mistake is particularly common in long-term modeling projects where data is entered all at once and then used for a long period of time.

Here at Forecastr, we try to address this by providing quick and convenient integrations with your accounting system, so that the most current data is always present in your financial model. If you’re not using an integration, you should have a regularly recurring time allocated to update your financial model every month, or every quarter at the very least.

Market conditions change quickly. And small changes to your internal processes can create large variances in your performance data. When you have a regular process in place to update your data, you ensure that your projections are accurate and your decisions are well-informed.

If you followed our advice and added documentation about your data sources to your model, you should update that documentation when you update your data so that you can easily identify which data may have gone stale and which has been kept current.

Mistake #7: Inadequate Documentation

Failing to document a financial model can lead to confusion and errors. To avoid this, it’s important to clearly document the model’s assumptions, data sources, and formulas.

Minimal documentation can be as simple as adding notes and comments to a spreadsheet. This is better than nothing. But we recommend robust documentation, especially around the assumptions used in the model and the formulas used to calculate key metrics. Investors are likely to question these two areas, and you should be able to explain your thought process around them.

Use a standardized format for your documentation. Categorize it in tabs or sections under the headings of assumptions, formulas, and data sources. Include a field to indicate the last time the value was changed.

Pro Tip: If you’ll be sharing the model outside your organization, it’s always a good idea to include some basic instructions and a table of contents to help outsiders navigate the model.

Finding Your Stride with a Financial Model

Financial modeling is always a complex process. It requires attention to detail, a thorough understanding of the underlying data, and a strong grasp of the ins and outs of your business in order to set realistic and accurate assumptions.

By following our advice, you can avoid the most common financial modeling mistakes founders run into. The result will be one of the most valuable resources in your toolset. It can help you raise funds, and it can help you optimize your business with data-driven decisions.

If you’re in the market for a world-class financial model, reach out to Forecastr today. We’ve built a best-in-class financial modeling platform, and our team of experienced analysts will work side-by-side with you to make sure you get the most out of your model.