Financial forecasting provides the basis of key business decisions. By painting an accurate picture of foreseeable business in the future, you can help to identify strategic initiatives to improve on your businesses weaknesses and capitalize on your strengths.
Forecasting is seen as hugely significant. In a Brainyard survey in 2020, 72% of business leaders said financial planning and analysis was becoming increasingly important to them.
But it’s not a straightforward task. There are a range of financial forecasting methods you can use, depending on the type of information you have and the kind of insights you want to generate. You can use different methods in isolation or in combination with each other.
Quantitative financial forecasting is based on real data, such as bank balances, financial statements and sales. It’s most commonly used to analyze business performance and scenarios, and is particularly helpful when you have a good record of historical information.
For example, you would use a quantitative forecasting method to predict the value of a regular client’s sales order, or to analyze how much of your bank loan you will have paid off by a certain point in the future.
No forecast is ever a guarantee, but you can have a good degree of confidence in these types of financial forecasts because they come from hard numbers that you know to be true.
Qualitative forecasting methods deal in values that are a little more vague or ambiguous. They’re used when you don’t have access to the full information, and you need to make estimates about certain aspects of your business.
For example, if you’re looking to expand your retail business into a new area, you might do some qualitative forecasting to see how long it would take for the new area to become profitable. These forecasts could only be based on estimates.
Qualitative forecasting can end up being quite accurate, especially when you only have to make predictions about minor aspects of your business that don’t have a large bearing on things. However, they are generally considered less reliable than quantitative forecasts.
The straight-line forecasting method is relatively simple to execute, although that in itself doesn’t always mean the forecasts are more reliable.
It’s most commonly used to forecast business revenue growth. For example, you might be able to see that over the last five years, your revenue has increased, on average, 5 percent each year. Knowing that, you can forecast for 5 percent revenue growth next year.
This is a relatively blunt tool because it doesn’t account for any of the context behind those numbers. However, if you don’t foresee any unexpected challenges in the upcoming period, it does provide useful information.
A moving average is similar to a straight line forecast, except it’s generally used in shorter time periods such as monthly, quarterly or half-yearly intervals.
You’d use the moving average forecasting method to calculate the performance of a specific metric over time. It’s useful because it smooths out the overall performance to mitigate peaks and troughs and give you a clear picture of how it’s tracking.
For example, you may have seen sales double in the past month. If you consider that in isolation then you’d think you’re growing rapidly. But your moving average may help you to identify that sales halved the month before that, so your rate of growth is actually fairly flat.
Businesses commonly use the moving average method to forecast sales, revenue, profit and other financial metrics.
The simple linear regression method provides a little more context to your forecasting by allowing you to see the relationship between two metrics. Linear regression is plotted on a graph, with one metric on the x axis and the other on the y axis.
It’s commonly used to look at the relationship between sales and profit. Your linear regression graph would likely show profit increasing as sales increase. But it can also illustrate the rate of increase, and how that fluctuates, and allow you to set business goals that maximise profit.
For example, you could see that you make $1 in profit for every hot dog you sell. But, you might see that once you sell more than 100 hot dogs, your profit margin drops to 80 cents per hot dog, because you need to hire another staff member.
This is one way linear regression forecasting enables you to make informed business decisions.
The multiple linear regression model, as the name suggests, takes the approach of simple linear regression and applies it to a number of variables. It allows you to produce forecasts when there are a range of factors at play.
For example, if we take the sales and profit example from the hot dog business. There may be other factors at play that impact profit margins beyond the number of staff you have. Fluctuating power prices and the cost of buns impact your expenses, and you may be coming into winter, where demand for hot dogs is lower than in summer.
Say you’ve had your hot dog business for a while, so you have a good idea of how these things will impact your sales and expenses. You can use that knowledge to make a profit forecast that takes into account all these relevant variables.
These financial forecasting methods all help to provide real-time context into your business performance to help you plan and make decisions.
Plainly, methods such as straight line and moving average forecasting are relatively simple to calculate yourself. However, more complex forecasting methods such as multiple linear regression are hard to do without help.
Runn financial forecasting helps to automate forecasting and reporting to give you the most accurate data possible. Forecasts can be generated at the click of a button, so you can constantly monitor your performance in all key metrics to stay on top of everything you need to know.
With an easy to use platform and reliable reports, you’ll be able to identify priorities and make informed decisions that have the biggest impact on growing your business.
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