Financial Modeling Mistakes Startups Should Avoid

In a startup, every idea and every plan hinges on having the financial resources to make it a reality. Forecasting your company’s financial performance into the future is crucial to transforming your ideas into a sustainably operating business.

One of the leading reasons for startup failure is running out of cash or plain old bankruptcy. A financial model can help make your company economically viable. What’s more, by building different scenarios, you’ll be better prepared for the future in case things don’t work out the way you planned.

Building a financial model isn’t exactly difficult. But there are some nuances that go into creating a sound financial model. For one, financial modeling is part science and part art. The science is ensuring the formulas and numbers are correct, while the art involves making assumptions that are truthful, reasonable, and represent the reality of your business. A mistake in either part could jeopardize the integrity of your model and any insights you gather from it.

As such, it’s important to understand possible errors you’re likely to make when building a financial model. Let’s look at the major financial modeling mistakes startups should avoid.

1. Hardcoding Financial Projections

Avoid hardcoding numbers wherever possible. Hardcoding is the practice of embedding data directly into your financial model. While it may get the job done, it can be thought of as “brute force” programming. It’s inflexible and makes modifying data later on much harder.

Plus, the likelihood that hard-coded inputs will result in bottom-line errors increases with the complexity of the financial model, especially if the model is used by someone other than the developer.

As a rule, only revenue and cost assumptions should be inputted manually, as the rest of the model should adjust based on these inputs. Hardcoding only increases the complexity of the model, making it almost impossible to validate your numbers and to see the financial impact of your assumptions.

2. Wrong Assumptions, Over-Optimism & Under-Optimism

Assumptions are the bedrock of financial planning – how you manage them could make or break your financial model. Think of financial modeling as a quantitative representation of your business strategy. As such, your financial assumptions, such as sales volumes and cost of sales, should not be randomly generated but rather should be based on market research.

But managing key financial assumptions as they evolve in both volume and complexity is often easier said than done. It helps to avoid overly precise or granular assumptions as they just don’t scale. Furthermore, financial assumptions should evolve as your business grows and changes.

So, if you find yourself unable to dynamically update your financial model, you may be dealing with the consequences of having overly-complex assumptions within your financial model.

3. Inconsistent Formatting

A good financial model should be three things: consistent, efficient and clear. This means your financial model should be easy to read and interpret, as a sloppy presentation can detract from good analysis. All this is to say that proper formatting is crucial to the efficiency of a financial model. It reduces the complexity of your model, making it more accessible to different viewers.

Some basic formatting rules are:

• Always use a consistent color scheme
• Use exact figures as excel will round off for you, keeping your model neat and your calculations accurate.
• Don't embed inputs in formulas.

Taking a few extra minutes to format properly will increase the efficiency of your financial model and make the work easy to understand.

4. Long Formulas and Daisy Chains

Long formulas increase the complexity of your financial model and make it very difficult to correct your calculations. For instance, while nested IF's may be necessary in some circumstances, they're more likely to contain errors and reduce the readability of your model. And this is the same case with a daisy chain.

A daisy chain is a modeling structure where there is a series of links that do not link to the original source. If any one link is broken or deleted, then other items in the daisy chain will also fail to compute. Daisy chains increase the complexity of your model, making error tracing difficult and time-consuming as they force readers to search through the spreadsheet to follow the logic.

5. False Precision

When you first start financial modeling, there’s a compulsive urge to quantify entries to the nth detail. False precision or over-precision, occurs when the assumptions included in a model imply better precision than is justified or have data beyond what is possible to know.

It’s important to remember that financial models often require inputs that are estimates or educated guesses. At the end of the day, the efficiency of your model depends on the relevance of your financial assumptions rather than whether all your inputs are accurate to the nearest thousand.

Focus your financial model on a range of possibilities. You want it to be directionally correct – not necessarily exact.

Bottom line

It is vital for businesses to create financial models in line with their changing business needs. Above all, companies need to invest in professionals who understand how to work with different financial models.

If you need advice on building a financial model, we are here to help. Check out our services or contact us today!