An Outline of the Forecasting Methods You Need
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 are no longer required to make educated assumptions about how a decision will affect the company's future. Instead, financial advisors can use forecasting to provide highly accurate projections.
The majority of financial analysis principles have been around for a long time. New forecasting methods and technology, on the other hand, continue to emerge as business challenges become more complex, and forecasting methodologies evolve to match their requirements.
It can be tough to know which method to apply in a particular situation. We have prepared an outline of forecasting methods you'll likely require when it's time to nail down resource allocation and growth expectations to give you a sense of all the options out there.
The Most Common Used Forecasting Methods
There are a variety of forecasting methods available, and financial advisors must determine which is appropriate for a given situation. Here are some of the most common forecasting tools used by financial advisors.
Straight Line Forecasting
This straightforward forecasting method makes use of historical data trends to determine future outcomes. Straight-line forecasting can be used to gain a view of ongoing growth at the same pace if a company's growth rate is constant. It's frequently used to predict revenue and additional market demand as a business grows. It's simple and limited, yet it's useful for making quick financial decisions.
Moving Average Forecasting
A moving average is used to make short-term decisions by focusing on a specific metric. It's frequently used to forecast short-term trends for the next few days, months, or quarters. This method is used to create a constantly updated average of fluctuating variables. This model is used to forecast inventory needs and demand during peak selling seasons and estimate stock values.
Simple Linear Regression Forecasting
This method is used to illustrate the relationship between an independent variable (such as sales) and a dependent variable that is affected by the independent variable (such as profits). This method makes it simple to spot concerning tendencies fast. If sales are increasing but profits are dropping, there is a deeper operational issue at hand.
Multiple Linear Regression Forecasting
More than one factor can influence the outcome of a business. Multiple Linear Regression (MLR) allows for multiple independent variables, making it easy to acquire a comprehensive picture of the problem. The cost of labor, materials, and machine efficiency, for example, may have an impact on a product's margin. With MLR you can see all of these variables at once. The disadvantage of this method is that it involves several variables, which increases the possibility of error.
To produce forecasts, qualitative forecasting relies on soft data, such as expert estimates that aren't backed up by historical data. For instance, suppose a consultant estimates that your organization would suffer increased costs as a result of a change in compliance standards. Because the model lacks previous data, the prediction may be correct, but it is less dependable and accurate than other forecasting methods.
Two widely used qualitative forecasting methodologies are market research and the Delphi method. Because such models do not use existing financial data, the finance department is unlikely to be tasked with running them, but it may be asked to assist or interpret them.
A flawless financial forecast is impossible to achieve. Because your organization is attempting to generate future forecasts based on facts rooted in the past or anticipated estimates, there will always be a margin for error, no matter how rigorous a forecasting system you implement. The goal of a financial advisor is to choose the forecasting approach for the situation at hand while keeping constraints in mind.
Important Factors to Consider When Choosing a Forecasting Method
The context of the forecast, the relevance and availability of past data, the degree of accuracy needed, the time 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 the purpose of the forecast. This will help you establish the optimal forecasting model and the level of accuracy required. The purpose of the forecast will also affect what variables should be included.
For example, if a forecast is intended to provide an overview of standard business operations, unique events such as one-time marketing campaigns should be excluded. However, if a model is trying to determine how a specific marketing campaign affects sales, it must incorporate all of the one-time expenses involved with the campaign. In other words, the forecasting model's scope will be determined by its end objectives.
Transparency is required for successful forecasting. Executives may request a forecast without fully comprehending what goes into producing one. If more information is required, it is essential that the financial advisor be straightforward.
When communicating the results to executives, it's also critical to bring out limitations and potential blind spots. Of course, you should always aim for the most precise results possible, but in some cases, speed trumps precision, and a rough estimate is all you need to make a decision.
The Right Tools for the Job
Hopefully, you now have enough information to choose the most appropriate forecasting strategies for your projects. However, the technology you use to evaluate data is just as significant as the method you use to forecast.
DataRails, for example, is an enhanced data management tool that can help your team create and monitor financial forecasts faster and more accurately than ever before. By replacing spreadsheets with real-time data and integrating fragmented workbooks and data sources into one centralized location, you can work in the comfort of excel with the support of a much more sophisticated data management system behind you.
Financial forecasting is used by every firm for planning, budgeting, and a range of other financial responsibilities. Whether you're managing cash reserves, setting marketing budgets for the future year, analyzing payrolls, or searching for ways to grow your company, the forecasting methods you employ will directly impact the decisions you make in each of those operations.
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 first step. You’ll need to interpret the results to relay potential risks and opportunities to stakeholders.