The benefits and drawbacks of simple versus complex models are endlessly debated among CFOs and their finance teams. While simple models are easy to implement, the pursuit of accuracy often results in highly complex models that fail to deliver measurably better results.
This guide will challenge your thinking about accuracy in modeling and forecasting to help you develop a better financial forecasting model – one that balances the desire for accuracy with the work involved to achieve it.
Financial forecasting is one of the most complicated yet strategically essential tasks for CFOs today. When creating a forecasting model, the first question is, how detailed do you need to make your model to ensure your forecast is accurate?
Balancing simplicity and complexity in a financial model is a crucial concern for CFOs because it is assumed that the more complex and granular the model is, the more accurate and reliable the forecast will be.
This article challenges that assumption and offers another perspective that CFOs should consider when deciding on the complexity of their financial models.
Simple vs. complex financial models
Financial forecasting and modeling are the backbone of business decision-making. The correct forecast can be the difference between making a strategic leap forward and tumbling into deep operational pitfalls.
Whether you’re using cash flow forecasting software, revenue forecasting tools, or modeling to build a more strategic budget, striking the right balance ensures a functional and practical financial model. It must cater to the dynamic needs of modern businesses while maintaining reliability and ease of use.
Benefits and challenges of simple and complex financial models
The following table illustrates some of the benefits and challenges related to simple vs. complex financial models.
We should note that here’s no single definition of what a simple model looks like or what constitutes a complex model. Simplicity versus complexity in modeling describe a continuum of choice with regard to how much detail you want to build into your models. The end goal remains the same, regardless of your model’s complexity
Your financial model should represent your company’s financial situation with enough accuracy to give you confidence you need in any decisions you make based on its results. (Bonus points if you can keep it user-friendly and easy to update and maintain.)
Let’s take a closer look at some of the characteristics of each “type” of model.
Complex financial models
CFOs in the SaaS industry find complex models invaluable as they provide an in-depth and granular view of a company’s financial health and projections. The granularity allows detailed analysis of various business segments.
A more complex model would identify specific revenue and cost drivers to enable scenario analysis and would be able to simulate different assumptions about the future, from market growth rates to customer churn rates.
The outcomes can be used to forecast customer acquisition costs, lifetime value, churn rates and other key metrics to inform business decisions.
Building complex financial models takes a lot of time and effort, though. Regularly updating the data is labor-intensive and challenging for SaaS companies as market conditions and internal metrics change rapidly. The risk of errors with a more complex model is higher, too, which can impact the reliability of forecasts.
Simpler financial models
Simple financial models offer clear insights into sustainability and the company's immediate financial condition. Depending on how granular they are (i.e. where they fall on the continuum between simplicity and complexity), they can accurately predict subscription rates, churn rates, and average revenue per user (ARPU).
Most companies start with just revenue (the income statement). Then, they’ll develop their profit and loss (P&L) statement. Those that want to do more in-depth forecasting will also build out their balance sheet and their statement of cash flow.
Simpler models make quick decisions like reallocating resources, adjusting pricing strategies, or reducing costs easier. SaaS companies can also use the simpler model to identify when more detailed models might be needed. Simple financial models cannot model complex scenarios or account for multiple variables. They lack granularity and do not provide details about specific business areas to make informed decisions.
A rule of thumb for determining the level of complexity in your model
The decision about how complex to make a financial forecasting model usually boils down to how accurate you want your forecast to be. Of course, we all want highly accurate forecasts, but accuracy comes with a cost
The graphic below illustrates the prevailing perception that the more complex you make your model, the more accurate the results, and thus your projections. Theoretically, this is true. However, it misses some important considerations that can (and should) influence your model design.
When appropriately designed, simple models can be sufficiently robust, easier to comprehend, and allow a business to update more quickly than complex models.
In contrast, the time required to create and maintain highly complex financial models is significant. This approach diverts valuable resources away from strategic decision-making to maintaining the model. Complex financial models, often built in a web of interlinked spreadsheets, are also rife with the potential for human error, which imposes the additional burden of almost constant validation.
At its core, financial modeling is all about using historical data combined with some assumptions to predict future performance. So, of course it seems intuitive that the more data you bring into your model, the more accurate your forecasts will be.
However, the time burden that complexity imposes, along with the potential for error it introduces into your forecasting, are important trade offs every CFO should consider when designing a financial forecasting model.
CFOs must balance accuracy, flexibility, and practicality when choosing the appropriate financial forecasting model. There is always a strong inclination toward complexity in financial modeling. However, a good rule of thumb is to think like Albert Einstein, who said, “Make things as simple as possible, but no simpler.”
Complex models create a false sense of accuracy
Another way to look at this is to think in terms of your own personal financial model, your household budget. You could, for example, create a very elaborate categorization scheme to make sure you stay within your budget for food with separate categories for breakfast, lunch, dinner, and snacks. You could go even further and split those out into separate categories for eating at home vs. dining out.
While this may give you a strong sense of control over how you spend your money, does it actually help you? Probably not. The more granular you make your budget, the harder it is to track. And before you know it, you’ve forgotten about that latte you picked up yesterday on your way to work. It becomes just one of those small, but potentially numerous errors that will throw your budget off at the end of the month.
Complicated models are the same way. They create a false sense of accuracy that can throw off your projections, and the more complex they are, the greater those variances are likely to be. So, you always want to remember that the more granular you make your model, the harder it will be to track all the details you need to meet your target.
Simple models offer greater agility
There will always be an ongoing tension between the desire for detailed accuracy and the need for timely, actionable insights. Here, it can help to think about the opportunity costs that come with complex models, which lends a more concrete and pragmatic perspective to the debate between simple and complex financial models: Simplified models offer far greater agility than complicated models.
Simple models are more lightweight and take much less time to update with new information and assumptions. Thus, they enable faster decision-making, which is after all, one of the core reasons we develop financial models in the first place.
When you’re considering how detailed to make your model, remember, a simplified model will get you 90% of the way there, meaning you can usually be pretty confident in your results. And, it's quick.
In contrast, a really detailed model might get you that extra 10%, but it will likely take you 30 times as long to create and even more time to validate and maintain. So, it’s important to ask yourself what that extra level of accuracy will give you in terms of the decisions you’ll make based on your results. One thing is certain, though. You cannot make those decisions as quickly as you could if you keep it “as simple as possible, but no simpler.”
How do we achieve the right balance of complexity and simplicity?
Achieving the right balance between complexity and simplicity in financial modeling is essential for FP&A teams and CFOs. It is important to understand that adding layers of complexity to a model does not guarantee increased accuracy or precision. Simpler models often outperform complex ones. Therefore, prioritizing simplicity without undermining the model’s robustness leads to more effective financial planning and analysis.
Fortunately, finding that balance is easy with Drivetrain because you get the best of both worlds. Drivetrain offers robust features for SaaS financial modeling and forecasting that significantly streamlines the process.
With automated data consolidation and a large selection of versatile, ready-made modeling templates to jump start your process, you can effortlessly construct even the most intricate financial forecasting models in very little time. Keeping them updated is easy, too. With the ability to connect to all the data systems you use in your business, you can update your models with the click of a button.
With a financial modeling software like Drivetrain, you can make your financial forecasting models as simple or complex as you want and get fast, reliable results to inform your business decisions. Contact us for a demo!
.svg)
