When it comes to choosing a financial forecasting model that offers accurate, reliable data to inform strategic decision-making, businesses face a spectrum of options. You can choose between simple forecasting options and complex methods requiring business software to account for all of the different variables being considered
Each approach offers its own strengths and weaknesses, so businesses have to choose the forecasting method that best suits their needs and their goals in projecting future finances.
It’s important to keep in mind that no financial forecast is foolproof. Because your organisation is attempting to make future predictions based on information and insights rooted in the past, there is always a margin for error, no matter how robust of a forecasting method you choose. Instead, businesses are seeking a forecasting method that offers general reliability in helping frame and inform financial decisions such as budgeting and hiring.
Forecasting methods typically fall into one of two categories: qualitative and quantitative forecasting. Here’s a look at each school of thought, along with some of the leading forecasting methods used by businesses today.
Quantitative financial forecasting takes a straightforward approach to generating forecasts based on hard data. Typically, quantitative forecasting is more effective when dealing with data points such as future sales growth and tax topics, rather than subject matter that has less concrete data to guide these forecasts.
Although quantitative forecasting takes the guesswork out of the process, it can also be limited by a lack of human expertise, resulting in a deficit of important context that could alter forecasts significantly. Still, it’s a useful tool in a number of business scenarios, especially in cases where your historical business data is a reliable resource for projecting future results.
Here are some of the top quantitative models used by businesses.
A straight-line forecasting method is one of the easiest to implement, requiring only basic math and providing reasonable estimates for what businesses can anticipate in future financial scenarios. Straight-line forecasting is commonly used when a business is assuming revenue growth in the future.
Your business may use its past revenue growth rate as a standard for growth in the future. If revenues have grown by an average of seven percent over the past three years, for example, you could assume a similar growth rate for the next three to five years with the straight-line method.
Of course, many variables will affect not only your revenue growth, but also your net profits over that period of time. But this method can still be effective when you’re setting financial goals and budgets, and creating plans for the future based on where you expect your company to be.
A moving average is the average performance of a specific metric over a specific period of time. Typically, a moving average is used to evaluate on monthly time frames, rather than yearly time frames. It’s often used to evaluate revenues, profits, sales growth, stock prices, and other common financial metrics.
A moving average is great for smoothing out performance over time to get a better understanding of your company’s financial trends. If you’re in an industry where sales and revenue can fluctuate over time, a three- or five-month moving average can help you make sense of the peaks and valleys that take place from one month to the next.
Time series is an umbrella term representing a few different approaches to financial forecasting. The strategy behind this forecasting method is to identify patterns in historical data that will repeat in the future, enabling data-driven forecasting across a range of financial metrics.
You can use this in cases where performance is expected to be fairly steady. For example, time series forecasting can use sales and revenue growth from prior months to estimate performance in the upcoming month. Or, if a straight translation of these trends isn’t accurate to your business results, you could embrace a smoothing approach to time series forecasting, using averages of these numbers to eliminate extremes and forecast a time-bound pattern for performance into the future.
In cases where seasonal or other trends can affect forecasts, time series forecasting also allows for historical data to be adjusted based on these trends. If you’re a retailer experiencing peak sales and revenue in the fourth quarter of every year, for example, this can be accounted for in time series forecasting.
Linear regression is a graphical representation of the relationship between two or more data points. It uses the relationship between x and y variables to chart a trend line illustrating the relationship between the two.
Sales and profits serve as an easy example. If sales increase, profits are likely to increase, creating a linear regression that shows a positive correlation between the two. But if sales increase and profits decrease, it can indicate other problems, such as rising expenses that are cutting into profits—including, potentially, an increased cost per conversion that is reducing the value of your company’s sales efforts.
The trend line produced by this linear regression can be used to forecast future results, supporting better budgeting and helping business leaders make strategic decisions that improve business performance.
Qualitative forecasting is an inexact science. It uses soft data, such as estimates from experts that can’t be corroborated by historical data. An example of qualitative data is when an executive predicts the costs a company will incur due to a new regulatory law. The expert could be correct in their prediction, given their vast experience and insight, but there is limited data available to support any prediction when such circumstances have never been faced before.
In general, qualitative forecasting becomes less reliable the further into the future it attempts to predict. But qualitative forecasting remains popular, and effective, when applied in short-term situations.
Here’s a look at the two leading models for qualitative forecasting:
Market research is widely used in the business world to evaluate potential scenarios a company hasn’t faced before. One well-known example of this is when a business is choosing where to open a new location, or when it’s testing the marketing and packaging for an upcoming product.
Market research does generate data to inform financial forecasts, but there are many variables and unreliable circumstances that make it hard to rely too heavily on the accuracy of this data. In general, the more data gathered through market research, the more trustworthy that data will be. But gathering market research is time-consuming and costly, and even the deepest investment in this process won’t guarantee that small research biases, inconsistencies in data collection, or other uncontrolled variables won’t mess with your results.
Similar to market research, the Delphi method of financial forecasting sources its data from experts who can speak knowledgeably on the subjects being evaluated. Your company will seek outside sources, as well as in-house expert insight, to compile data through questionnaires that can be used to identify consensus opinions about various financial matters.
This can be a great tool for performing qualitative long-term forecasting, such as discussing the growth of a certain industry or market, or attempting to project the value of real estate investments as the market changes over the next few years.
Financial forecasting plays an important role in planning, budgeting, and many other financial activities in your company. Whether you’re managing cash reserves, setting marketing budgets for the next year, managing employee payrolls, or looking for opportunities to expand your business, the forecasting methods you choose will have a direct impact on the decisions you make in each of those processes.
Want to accelerate your financial planning by improving the data used in these forecasts? Download the Longview Continuous Planning e-book today.
