Types of Sales Forecasting and How They Can Benefit Your Business

a picture of avocados, which are mentioned in the article as part of the explanation on sales trends and fads that can be accounted for with certain types of sales forecasting

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In business, the most common type of forecasting is sales forecasting. And for good reason, too. Sales forecasts help set expectations and sales goals on where you hope to be in the future. Because sales forecasting is so important to business, many new forecasting techniques, types of sales forecasts, and applications to create them have come into existence.

At some point in your career, you’ve probably come across long-term sales forecasts, which typically predict quarters or years ahead. These forecasts likely come from the finance or sales team. Creating these forecasts requires looking at historical data, marketing efforts, and other data points the company can leverage. The right tools bring all these data together to create powerful and accurate sales forecasts for the distant future. 

Not all forecasts are for quarters or years ahead, however. Sometimes forecasts predict the near future or just weeks ahead. In this article, we discuss a variety of sales forecasts, what goes into them, and how they can benefit business.

Forecasting Sales for the Near Term

There are numerous forecasting tools out there, some simpler than others. Some of the more complex tools leverage artificial intelligence (AI) and large amounts of historical data to make accurate, short-range forecasts for the next few days or weeks. AI-based forecasting tools can be especially useful for sales managers or operators who may need to make rapid decisions to optimize their business in the coming days. However, they aren’t necessarily useful to large-scale planning or a company’s overall finances.

a photo of the TimeForge manager mobile app showing projected sales data
With the TimeForge Manager mobile app, users can see their forecasted sales alongside real-time sales figures.

For example, TimeForge’s robust sales forecasting engine can use historical sales data to predict labor needs. It can then automatically generate staff schedules that will meet the expected demands. It can do this for hourly sales and across 15-minute increments, nearly in real time, with 98.87% accuracy. This level of granularity is great for optimizing your labor, but it’s not what you want when you’re looking at data from your finance department about how the business is doing. Each type of sales forecasting has areas where it’s better suited for making predictions, and areas where it’s not.

How Important are Historical Sales Data?

Pretty important. Sometimes a business does not have large amounts of historical data. This can be due to many reasons, such as it’s a new company or a new initiative within the company. Or in some cases, they just didn’t keep the required data for long enough periods. When this happens, you may not be able to use statistical forecasting methods, or if you do, the accuracy may not be as high as you’d like.

With very little data, you’ll likely be stuck with mediocre forecasts until you’ve amassed at least a couple of months’ worth of sales. In this situation, depending on the data you do have – for example, a few weeks’ worth of historical sales data and related information (such as pricing and promotion data), you may find some success forecasting in the short term using some of the more novel and advanced AI methods.

Using a Range of Sales Forecasts

Another option when forecasting is to use a range of forecasts. In other words, run a large number of different types of forecasts. Then, utilize a combination of each to generate a forecast based on weighted averages. This method allows you to potentially pick the best features of each forecast and have them represented in the final forecast. It also allows you to wash out some of the shortcomings that each method might present. A combination of forecasts is popular when using AI models. By creating a so-called “ensemble of forecasts,” you achieve a robust and accurate prediction of the future. 

If you are fairly confident in your sales, and your business isn’t strongly affected by season, you may be able to use a weighted average forecast to predict your future sales numbers. In selling software licenses, for example, you are less likely to see effects caused by a certain season. However, you might see a relatively steady stream with spikes for bulk business deals. By running a weighted average forecast, you can set a goal of where you’d like your sales to be in the future. Whether your business under or over performs during that period of time, you can look back on the forecast in order to internally find out what your sales reps did that resulted in those numbers.

When it comes to statistics, the more data you have, the better your predictions will be. A more traditional and well-founded kind of forecast uses statistical modeling and large amounts of historical data to attempt to break down how your business behaves over weeks or years. These methods then can project fairly far into the future with impressive accuracy, especially compared to simple rolling averages. To improve forecasting accuracy further, you should collect as much data as you can over a long period of time. At least a year, if possible.

For example, if you wanted to predict sales of produce in your grocery store, you could use statistical modeling to look back over many years and find all the trends in peoples’ purchasing patterns of produce. By looking at things like season, peak times of day, holidays such as Thanksgiving, and other factors influencing sales, you can predict well into the future what your Produce department sales will be. And with at least 95% accuracy across the entire period. When TimeForge predicts sales, it looks at all of these trends to ensure that schedules will have the appropriate coverage. Even during a holiday rush!

These graphs, based on 2 years’ worth of sales data for a retail grocer, show how much more accurate TimeForge sales forecasts are for predicting holiday sales than the 6-week rolling averages often used by managers.

Often, the statistical forecasting methods are able to detect changes in your historical data, aptly called “change points,” where the trend of your forecast changes. Furthermore, given sufficient historical data, you can extract how your sales behave across an entire year. You could even do so across a week or throughout a single day. Given all this information, forecasting tools can project the trend forward in time and simply add information about how sales behave across weeks, years, and even days to form a forecast. This kind of forecasting is useful when your sales aren’t necessarily steadily increasing or decreasing but just changing over time.

a picture of avocados, which are mentioned in the article as part of the explanation on sales trends and fads that can be accounted for with certain types of sales forecasting
If avocados suddenly receive a celebrity endorsement and become popular, you want to account for the effects in your forecasts.

For example, consider when a product is going through a “fad” due to celebrity endorsement or something similar. Forecasting models will notice the increased sales, and still utilize the historical seasonalities, to generate accurate forecasts. For instance, if Oprah endorsed avocados, you might see an increase in avocado sales during the weeks following the endorsement. The models will quickly recognize that sales are increasing and will account for the change in trend. After a while, when everyone has moved on from the avocado fad, sales will trend downward until they stabilize back to where they were previously.

Picking out the start of the fad, the turning point of the fad, and the end of the fad gives us 3 different points where our sales didn’t behave as they had previously. This allows us to account for those periods without messing up our future forecasts. 

Forecasting Sales and Market Saturation

Similar to the example above, not all kinds of forecasts deal with data that constantly increase or decrease with regular behavior. Sometimes, we will have a forecast that saturates at some market cap.

When sales are expected to plateau at a certain point, you can generate a forecast that predicts how you are going to reach that saturation point based on how you have been growing toward it historically. The downside to this method is that, either intuitively or from market knowledge, you need to know at what point your sales are going to saturate. If that saturation point isn’t known, you may vastly overestimate or underestimate the point. This can lead to a highly inaccurate forecast and potentially drive your company in the wrong direction. On the other hand, if you have a good idea of the sales saturation point, this form of forecasting can be quite useful for predicting how you will reach it over time.

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