What would happen if you had all the time in the world to build a forecast for the next year? Enough time so that not a single thing is left out. Site traffic, conversion rates, hiring, inventory, and every item in between factored into an elaborate prediction model.
Then, six months in — BAM! Your forecast is 100% precise. Number by number, line by line; everything is spot on.
You would likely respond by double- checking every formula, just to make sure there are not any errors. Because this never happens in the world of accounting.
Not only is this scenario highly unlikely, but attempting to achieve this level of numerical enlightenment could actually be bad for business.
You read that right.
It might seem counterintuitive, but this level of precision is not the point of financial modeling, and it can even harm your forecasting capabilities. Instead, we should aim for accuracy.
To do that, we need to understand the difference between precision and accuracy.
What is precision vs. accuracy?
Granted these two words are similar in nature and definition. That said, there is a difference, particularly for controllers and CFOs.
Here's an example from real life: I was once tasked with building a forecast for a medical device company to help pitch for a round of funding. A major goal of the forecast was to predict the impact of switching manufacturing from in-house to outsourced production.
I eagerly got to work.
In order to precisely account for every possible number of units sold, and the related cost and profitability, I created a model that tabulated every conceivable Cost of Goods Sold (COGS) in 1,000- unit increments. And I did it up to 300,000 units sold.
The result was a massive, 36 MB Excel workbook. It was so large and complex because the various levels of possible production filtered back into the financial forecast and multiple other sheets in the file.
The model was precise down to:
- The exact number of units sold
<;i>Each cost to produce those units (accounting for discounts at each 1,000-unit tier)
- The tabulation in the overall forecast
The problem with precision
There was one glaring problem with my incredibly precise forecast. The problem wasn't the number of metrics. Or the complex breakdown of costs.
The problem was that no one else understood how to update or even use it.
The model was stuffed into the supporting documentation of the overall investment presentation. And when it came time for potential investors to see how the numbers on the slides were calculated—the Excel spreadsheets wouldn’t work for them. Even if the investors could get them running, it would be nearly impossible for me to explain succinctly how they worked.
The solution? A simpler, yet still accurate model.
Long-term accuracy is far more effective than short- term precision
An accurate financial model is one that:
- Allows for everyone (who needs to) to understand how the numbers work, not just what the numbers are
- Is easily updated, so it remains useful over a set period of time, instead of quickly becoming unusable or inaccurate
- Allows for easy decision- making thanks to clarity and ease-of-use — not necessarily 100% precision
Ideally, a financial model should be simple enough for anyone on the executive team to update.
Business leaders — not just finance leaders — should be able to quickly change the quantity of goods and cost of goods assumptions without needing a detailed explanation of how things work.
This is the big problem with precision. The more precise your model, the more complex your calculations, and so fewer people (if any) will understand how you came to your conclusions.
That barrier to entry in understanding the model results in an inaccurate forecast over time.
In my own example, no one knew how to operate the workbook and even if they did, it was unreliable due to the size of the file. If I had made the model simpler, those potential investors would have been able to see the business potential for themselves instead of having to trust my slides in the pitch deck.
Here are a few more ways accuracy wins over precision:
- Makes the model more understandable and usable by those who are using it
- A model regularly updated is more reliable than a “precise” one-time forecast
- With a better understanding of the model, better business decisions can be made
Complexity might be fun, but simplicity wins in the long run
A simpler model is often a more accurate model over time. And that should be the goal — and the reason for existing — for any financial model.
Key takeaway: When building a financial forecast or financial model, you may be tempted to try to get everything perfect. This requires a lot of variables, assumptions, and guesswork to get it right. When this happens, you spend a ton of time, create a lot of complexity, and ultimately hurt your ability to forecast. So instead of trying to be so precise, you should try to be accurate. Accept that there will be a margin of error in your forecast. And make the model easy to share and update, even for non-finance experts.
How to create a more accurate forecast
There are lots of reasons for creating a financial model. You may want to pitch investors, prepare for a sale of your business, improve cash flow, or just improve overall profitability. Whatever the situation, use an accurate forecast. And be careful not to get too precise.
Less precision doesn’t mean less data
And no, sacrificing precision does not mean using fewer metrics or oversimplifying data.
For example, using a financial planning and analysis solution, such as Jirav, allows you to input any number of variables, track key KPIs, and even add custom spreadsheets to your analysis and forecasts. Reports and financial dashboards can be easily updated and make the numbers easy to understand for anyone.
Free yourself from yesterday’s financial modeling tools and see for yourself how accuracy wins over precision for financial modeling.
Evan Wells, VP BizOps & Financial Services at Jirav. You can contact him at evan@jirav.com.
This article appears in the winter 2021 issue of the Washington CPA magazine. Read more here.