Credit: Carlos Muza
There are a number of budget forecasting methods to choose from. Knowing which one is right for you, and making it work, can be a confusing challenge. This article introduces and discusses three techniques used for budget forecasting.
What is Budget Forecasting?
Your budget is the lifeblood of your business. The circulation of your finances ensures continued operations to meet the needs of your clientele. Your budget details the flow of money in, out, and through your organization.
Forecasting is the prediction of the future, usually with a sensible rationale behind it. The budget forecasting definition, then, is the attempt to predict your future budget.
This can be tricky, though, because while many things about your budget are within your control, many are not. The most refined forms of budget forecasting seek to forecast these latter elements, to enable you to model the outcome of different choices your business can make.
Budget Forecasting Methods
There are lots of different methods used to forecast a budget. Generally speaking, the more powerful budget forecasting methods require more technical knowledge about statistics. While there are many more techniques than this, we will discuss the three most common ones.
Projection
This is perhaps the simplest method. A projection plots data from a budget element against time and uses a mathematical formula to extend the data into the future.
For instance, if you were doing a linear projection, you would calculate a line of best fit, and simply calculate the value of the budget element for a given point in time.
If the budget element you are looking at appears to follow a regular trend, whether it’s linear, exponential, etc. projection may be good enough. However, if the data doesn’t trend smoothly with time, a projection may be a poor way of predicting that budget element.
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What About Changes to the Status Quo?
Everything we are talking about here assumes things are more or less the same going forward. The more complex methods work to accommodate past changes into the future. But we also may have some inside knowledge that we can use to augment the math.
The math is still important, however, as it provides the formulas to show how we project further. We’ll use a combination of this inside knowledge on upcoming business events along with the formula to produce that projection into your business’ future!
Moving Average
One way to avoid problems with noisy data – that is, data that doesn’t trend smoothly – is to use a moving average. Moving averages take an average of a certain number of previous data points to plot a “smoothed” value of the data.
A moving average can be useful for smoothing out noisy data in order to do a projection. They can also be useful if your data is smooth, but appears to move in cycles (think seasonal fluctuations). In either case, moving averages are a rough way to capture the underlying trend.
Regression Analysis
The problem with projections and moving averages is that they presume that the only thing affecting your data is time. But as a business owner, you know that the choices and the conditions of the market impact the budget elements that are outside of your control.
A more advanced method of budget forecasting is regression. Regression analysis allows you to integrate other data that you believe impacts the budget elements you’re interested in forecasting.
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Single Linear Regression
With single linear regression, you are attempting to determine the impact of one factor on the budget element in question. For instance, how does your advertising spending impact your revenues?
A single linear regression will generate a formula for a line of best fit in the form Y=β0+β1X. A line of best fit minimizes the average squared vertical distance from the actual data points to the line itself. In other words, it ensures that the data points are on average as close to the line as possible.
The one pitfall to estimating a line of best fit is just because you can estimate such a line doesn’t mean that you should. If the relationship between your budget element and your other data does not look like a line, single linear regression would not be the way to go.
To provide some objective measures of the goodness of fit, statisticians consult the estimated t-scores, which indicate how well the β terms describe the relationship of the data, and the R2, which indicates the overall goodness of fit.
With the estimates for the β terms, you can plug in hypothetical values for X to generate projections for what your budget element Y might be.
Credit: Kelly Sikkema
Multiple Linear Regression
In all likelihood, you’re not going to be satisfied explaining your budget elements with just one other set of data. For this, budget forecasters use multiple regression analysis.
Like single linear regression, a multiple linear regression estimates a line of best fit for the data you plug in. Unlike single linear regression, the line of best fit is estimated over more than two dimensions.
The line of best fit for a multiple linear regression takes the form Y=β0+β1X1+β2X2+…+βnXn, where you have n causal factors that you are looking at.
Just like with single linear regression, you will need to check the t-scores for each coefficient β and the R2 to determine how good of a fit the line is. In addition, a statistician analyzing a multiple linear regression will look at the F-statistic, which checks whether all of the coefficients β are good estimates.
Multiple linear regression introduces several challenges. First, if the causal factors are correlated with each other, your regression analysis won’t be able to determine which is causing the budget element to change. This will require you to mathematically transform one of the factors.
Second, if there is a trend in how your data deviates from the line of best fit, it may indicate that a line is not the best form. This is also the case if there is a trend between one of your causal factors and the data deviations.
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The Right Budget Forecasting Method
Choosing the right budget forecasting method isn’t easy. Whereas projection and moving average don’t give you much insight into what you do to impact your budget, regression analysis requires technical knowledge typically learned in a year long statistics course.
In the companies that have one, budget forecasting is something typically handled by the chief financial officer. A CFO can improve your business by giving you insights into how you can improve your financial operations to increase your bottom line.
Many business owners think that budget forecasting is too complicated, and that the professionals to do it for them are financially out of reach. However, with fractional CFO services, businesses of all sizes can enjoy all the benefits of a CFO at a fraction of the cost.
A fractional CFO is an outside consultant who works with your business part time. Because a fractional CFO works with many businesses, they will have insights and connections that most CFOs lack.
indinero offers CFO, controller, and tax preparation services for businesses just like yours. Our specialty is businesses at the level of Seed funding through Series B funding. To get started with indinero’s financial services, contact us today for a consultation.
