Best Practices to Improve Financial Modeling Accuracy
Your yearly annual review process presents a critical opportunity to make improvements—to learn what went wrong, what went as planned, and what worked better than expected. This is especially vital now, in the wake of a year marked by tough and unexpected challenges.
Every organization wants to minimize mistakes and optimize progress toward its longer-term strategic goals. Executive management must acknowledge the role of planning and performance assessment in achieving these objectives. To do so, it must rely on financial modeling—the backbone that drives strategic decisions and outcomes.
As the foundation of intelligent decision-making, financial modeling can have broad-reaching impacts on your organization’s success. Improving the accuracy of your financial models is essential to improving your forecasts, decisions, and operations. This, in turn, increases more qualitative metrics, such as marketing efficiency, customer satisfaction, and loyalty.
Long-term users of Synario know that the beauty of financial modeling truly shines after iterative improvements and assessments over time. Here, we will examine methods of evaluating your forecasts’ accuracy and creating a plan to monitor and improve your model in future years.
Compare actual performance to the plan
A formal review of your financial model is critical. After all, there’s no point in having a financial or strategic plan if you do not compare actual performance to what you set out to achieve. Your review protocol should be both efficient and valuable. Focus on the most significant areas of the business or the most strategically important areas.
According to a McKinsey survey of 127 CFOs, 43% of respondents are looking for ways to streamline their overall budgeting process to react more quickly and efficiently to unpredictable black swan events. Meanwhile, 65% of respondents anticipate that the use of rolling forecasts will increase in 2021 and beyond.
To better streamline the process, the way we set forecasts and goals and compare performance metrics should be identified. The goal should be to focus instead on practical evaluation and continual improvements.
There will be variances in your projections — you can never truly predict the future. What’s more important than these variances themselves (especially from the perspective of continual improvement) is identifying the drivers behind each miss.
Why were your results misaligned from targets?
Were they better than expected in some areas? If so, why?
In most cases, flawed assumptions have led to misaligned results. A model is only as effective as the quality of inputs given, and the logic applied. Review your organization’s historical performance and be realistic about the future.
If you experienced a positive variance in your target, question whether it is sustainable or merely a cyclical one-off. Look to the external factors and consult market and industry data to understand what broader trends are taking place. Do your assumptions make sense in the context of observed consumer trends?
Additionally, the logic of the model itself may be lacking. For instance, most casual users implement basic percentage growth rates. Projections of this nature may not be complex enough to capture the multivariate sales components, such as pricing and unit volume. Do changes in one area of the business impact another? If there are correlations, relationships, or dependencies between different business units and confluent factors, they must be accounted for to maximize accuracy.
While your goals and projections must be realistic, it’s not ideal to be too conservative, either. Overshooting targets presents an opportunity to improve. Planning lower than your potential is not an effective means to achieve it. The organization may miss market opportunities by not considering additional upside.
Bain & Company often discusses the perils of success and resting on your laurels: “larger corporate planning processes often discourage the insurgent mindset by encouraging ‘smaller’ thinking.” As with any model, retain the flexibility to achieve the upside and consider the likelihood of avoiding constrained resources when the chance does arrive.
One strategy your organization can look to implement is creating a more aggressive or optimistic scenario to capture the likelihood (or possibility) or any potential upside. While not a driving factor of the base case, a range of positive scenario outcomes can help organizations and management ‘think big,’ capitalizing on opportunities that may present themselves in the macro-context of the industry.
Plan for improvement and execute
Evaluating discrepancies doesn’t change the future; it simply tells us why what we did was not optimal. To make improvements, you need to formulate a plan of action, then execute it. Identify all the factors that play a role in the financial model. Establish which variables are most important and focus your efforts on making small, measurable changes. Incremental benefits add up over time. You can read more about this concept here.
Make a plan to improve the model by executing efficient, active change. This may involve shifting your review timeline from annually to monthly or recalibrating components (including certain logic) of the model itself. Repeat the improvement process each year or as often as you decide (KPMG research shows us that the most accurate firms review their forecasts and models monthly). The idea is to develop a sustainable framework that benefits the organization strategically and financially over time.
Improve your predictions with processes
Improving your financial models—and any strategic predictions or processes, for that matter—is a process in itself. When doing so, you must evaluate your current results, identify gaps, investigate improvements, adapt and plan for the future, and monitor and assess your progress on an ongoing basis.
What’s the best way to do this? Put the planned process in writing, with specific time frames, and stick to it. Then, set aside the time at the end of each year to review your progress in full detail.
The hardest part of any future-oriented outlook is making intelligent predictions in the first place. We can indeed improve our models’ accuracy over time. However, implementing fundamental principles for estimation is often the most critical step. According to Mckinsey, you can enhance your forward-thinking accuracy by implementing these three principles:
- Build on what is already working. Instead of starting from scratch, think about what areas or processes in your organization are working well. Why do they work well? What does that tell you when it comes to making assumptions and inputs for your model?
- Understand your context. Look at your organization in the context of your industry and the global market. What has been happening in other regions of the world? Are there learnings from consumer behaviors in other sectors? Any changes in your customer’s conduct may be structural (ongoing) or cyclical (likely to reverse). You need to know what each model variable is most likely to resemble.
- Always consider the bigger picture. Whether you’re at the stage of evaluating, planning, or monitoring, you need to maintain a birds-eye view. Predict both granularly and holistically and make sure they align.
Accuracy starts with the right tools
The process of improving the accuracy of your financial model is never-ending. The world is dynamic, and information changes daily. We cannot predict what the future holds. However, we can leverage the power of Synario to enable meaningful insights that allow us to plan accordingly.
If you would like further assistance or tips regarding improving the accuracy of financial modeling, reach out to your designated client success representative today.