As our access to information and our ability to manipulate data to effectively predict the future grows, so does our dependence on reliable forecasting. Executives no longer have to make educated guesses about how a decision will impact the future of the business. Instead, financial advisors can use forecasting to provide highly accurate projections.
Most principles of financial analysis have been around for many years. But, new forecasting methods and technologies continue to emerge – as business challenges become more complex, forecasting methods evolve to meet their needs.
It can be difficult to determine which method is best to use in a given situation. To give you a feel for all the options out there, we put together an overview of forecasting methods you’ll likely need when it’s time to nail down resource allocation and growth expectations.
There are many forecasting methods, and it’s up to financial advisors to determine which is most appropriate in a given situation. Ideally, financial advisors will wield the right method for the right project. Here are staples financial advisors often turn to for forecasting.
This simple forecasting method uses historical data trends to predict future results. If a company has a constant growth rate, straight line forecasting can be used to get a view of continued growth at the same rate. It’s often used to predict revenue and additional resource requirements as a business grows. It’s fairly basic and limited, but can be helpful for quick financial decisions.
A moving average usually focuses on a specific metric to make shorter term decisions. It’s often used to predict short-term trends for the days, months, or quarters ahead. This method is used to construct a continuously updated average of values that fluctuate. You’ll see this model utilized for stock prices, or to predict inventory needs and demand during peak selling periods.
This method is used to chart the relationship between an independent variable (such as sales) and a dependent variable which is affected by the independent variable (such as profits). This method provides an easy way to quickly notice concerning trends. For example, if sales are rising but profits are decreasing, there is a deeper operational issue occurring.
Business outcomes are often affected by more than one variable. Multiple linear regression (MLR) allows for multiple independent variables, making it easier to get a full view of the situation. For example, the margin of a particular product might be affected by the cost of labor, materials, and machine efficiency. MLR allows you to view all of these variables at once. The downside of this method is that additional variables also leave more room for error.
Qualitative forecasting uses soft data, such as estimates from experts that can’t be corroborated by historical data to make predictions. For example, if a consultant predicts that your company will incur additional costs due to a change in compliance requirements. The prediction could be correct, but it’s less reliable and accurate than other forecasting methods because the model has no historical data.
Market research and the Delphi method are two commonly used qualitative forecasting methods. The finance department will likely not be tasked with running such models, since they don’t use existing financial data, but may assist or interpret.
There is no such thing as a perfect financial forecast. No matter how robust a forecasting system you adopt, there will always be a margin for error because your business is attempting to make future forecasts based on data anchored in the past or projected estimates. A financial advisor’s job is simply to choose the forecasting method that is best for the situation at hand while keeping limitations in mind.
The context of the forecast, the relevance and availability of past data, the degree of accuracy needed, the time period to be forecasted, the cost/benefit (or value) of the forecast to the company, and the time available for conducting the analysis all influence which method is used.
Keep in mind what the forecast will be used for. This will determine the best forecasting model and how accurate the results must be. The purpose of the forecast will also affect what variables should be included.
For example, if a forecast is meant to give an overview of standard business operations, it shouldn’t take into account special events like one-time marketing campaigns. However, if a model is trying to determine how a certain marketing campaign affects sales, it should obviously include all of the one-time expenses that went into the campaign. In short, the scope of the forecasting model will be determined by its ultimate goals.
Successful forecasting requires transparency. Executives may request a forecast without understanding what is truly involved in making such a projection. It’s up to the financial advisor to be honest when more information is needed.
It’s also important to point out limitations and potential blind spots when explaining the results to executives. Of course, you should always strive for the most accurate results possible, but in some situations speed is more important than accuracy and a rough estimate is all you need to make a decision.
Hopefully you now have the knowledge to select the best forecasting methods for your projects. But, the technology used to analyze data could be just as important as your forecasting method. Causal helps you get to insights faster by allowing you to plug directly into data, build models faster with our templates, and share user-friendly outputs directly with stakeholders.
Every organization relies on financial forecasting for planning, budgeting, and a variety of other financial tasks. The forecasting methods you use will have a direct impact on the decisions you make in each of those operations, whether you're managing cash reserves, creating marketing budgets for the coming year, evaluating payrolls, or looking for areas to grow your organization.
Data is crucial, especially in today’s modern world, but it doesn’t replace the need for an expert. The data is only as valuable as the insights a financial advisor gleans from the information. Selecting the right forecasting methods (or running analysis using multiple methods) is just the start. You’ll need to interpret the results to relay potential risks and opportunities to stakeholders.