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Sales Forecasting Types and Benefits Explained

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 will cover:

  • what sales forecasting is
  • why it’s important
  • different types of sales forecasting
  • benefits of sales forecasting

Let’s start with the basics:

What is sales forecasting?

Sales forecasting is the process of estimating future sales, usually by predicting how much product or service you’ll sell over a given period of time.

There are many ways to forecast sales, but they all have one thing in common: they all rely on having information about the past (historical data), which can be used to make predictions about the future.

Why is sales forecasting important?

Accurate sales forecasts are important because they allow businesses to make useful predictions about the future.

Every transaction contains useful data points, and if you don’t take advantage of these data points, you ultimately lose out. When you can’t make accurate predictions about the future, your plans may not always align with available resources.

In the retail industry, we see this most often in the day-to-day operations of store managers.

Without accurate data, managers can’t easily predict how many employees and what positions to schedule in advance. Schedule too few employees, and you end up with long lines and frustrated customers. Too many employees, and you waste valuable labor dollars on staff you don’t need.

Over time, the results of both overstaffing and understaffing can add up to noticeable and significant losses in revenue and profit. This is why sales forecasting is so important to our customers – and why we work so hard to engineer the best forecasting system possible.

Forecasting in areas with secure scheduling laws

In cities and states with secure scheduling laws (also known as predictive scheduling laws or fair workweek laws), the stakes are higher and sales forecasting becomes critical. Why? Two reasons.

  1. First, because these laws require employers to post work schedules weeks in advance, and
  2. second, because the laws penalize employers for making changes to posted schedules.

Managers in these situations must therefore accurately predict their labor needs 2-3 weeks out or else get “nickeled and dimed” by fees. Over the course of a year, these fees can add up across a business.

Think: the cost of a luxury sports car or your kids’ college education. They’re no joke.

Types of sales forecasting and uses

Let’s look at different situations where businesses use sales forecasting to get ahead.

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 to 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 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, 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.

Sales forecasting with TimeForge

Let’s talk a bit more about TimeForge. TimeForge’s sales forecasting system is pretty amazing, and for good reason. We leverage heavy-lifting artificial intelligence (AI) and machine learning algorithms to build the best sales forecasts possible.

It sounds complicated, and it’s certainly complex, but the results are straightforward. Sales and labor metrics are presented in easy-to-read charts and graphs that managers can view from a computer or even from a mobile device.

Most often, we forecast sales in order to help managers build better labor schedules much faster than they could by themselves, but the engine is quite robust: it can also forecast almost any metric that a business might care about, just as long as there are historical data available. This includes things like customer counts, transactions, and even online orders.

Below, let’s look at what sales forecasting is and some advantages to using sales forecasts. Then, we’ll take a look at what makes TimeForge’s forecasting system stand out. Fair warning: I’m going to nerd out a little bit, but I’ll try to keep things fairly simple.

How is TimeForge’s sales forecasting different?

Here at TimeForge, we have the benefit of over 15+ years’ worth of experience in both retail and hospitality labor management. We also employ some pretty smart folks.

Our forecasting system uses a wide variety of machine learning and AI methods to calculate sales on a per-week, per-day, per-hour, per-15-minute, and per-department basis!

Many forecasting applications can’t get that granular and precise; TimeForge can.

3 Benefits of TimeForge’s forecasting system

In the past few years, we released an even more powerful version of our forecasting engine, which has three major advantages:

  1. Improved accuracy during holidays, including the days leading up to and after. Our system can accurately account for and predict the effects that holidays will have on sales based on previous years’ performance during the same time period.
  2. Increased speed. Our system can run a full forecast on the fly in only 20 minutes. This leaves more time for addressing issues ahead of schedule.
  3. Improved accuracy of department sales distributions across the workday. Our system has become even better at predicting the distribution of sales across the workday within a department (e.g. produce, meat, etc.).

Because our improved forecasting engine uses AI modeling, the more sales data you have, the more accurate your forecasts will be. This is true for all systems that use best-fit statistical models to predict future trends.

How does TimeForge’s sales forecasting engine work?

Okay, so we can’t give you the full recipe for our special sauce, but I can tell you a few things about how the system works:

  1. Using API calls, the forecasting engine retrieves each store’s sales data from TimeForge. (Sales are typically pulled into TimeForge through one of our many POS integrations.)
  2. The engine filters and pre-processes the data to make sure everything is good to go. We wouldn’t want to run forecasts on bad or incomplete data!
  3. The engine then selects a statistical model to create the best fit for the data across time. This involves cross-validation and fine-tuning to achieve the best fitting model for each store.
  4. The engine looks at the data for things like sales trends and seasonality. The model extends these forward in time to achieve a statistically sound forecast.
  5. The forecast is then sent to TimeForge for use in generating amazing schedules and reports.

Ow, My Brain Hurts

Too much jargon? That’s fair. Let’s look at a specific use case: predicting how much extra labor you will need for an upcoming holiday. Holidays are super important times for business in retail and food service, so it’s important to get them right.

For example, let’s say we’re building a schedule for the week of Thanksgiving. TimeForge’s forecasting system looks at all the Thanksgivings and calculates the effects that the holiday had on sales. It will then account for those effects into the forecast.

Then, you can either use those forecasted sales to build a work schedule, or TimeForge can auto-generate the schedule for you with all the staff you’ll need.

The key, of course, is having sales data for at least one Thanksgiving in the past (and more is better). Otherwise, any system would have a hard time accounting for it.

Actual Sales and TimeForge Forecasted Sales for the Week of Thanksgiving

a graph showing how TimeForge's forecasting has improved for holiday forecasts
A comparison between actual sales figures and TimeForge forecasts, showing how our forecasting engine has improved around its ability to accurately predict holiday sales. This graph is based on at least 2 years’ worth of sales data from a grocery retailer.

How do sales forecasts compare to manager projections?

This is one of those instances where a picture (or in this case a diagram) is worth a thousand words. Often, manager projections are based on simple rolling averages that look at the past 6 weeks’ worth of sales.

Let’s look at our Thanksgiving example again, except let’s compare manager projections to TimeForge’s latest forecasting engine.

In the diagram below, we can see that the manager would have underpredicted sales leading up to the holiday (11/22). They also would have overpredicted sales on the holiday and days to follow. This makes sense, as shoppers are usually in a rush to buy their turkeys and pie fixins before Thanksgiving, but they’re generally going to be coasting on leftovers a few days after that.

A rolling 6-week average wouldn’t be able to account for the effects that the Thanksgiving holiday has on sales.

Thus, a schedule based on the average would have resulted in first an understaffing situation and then an overstaffing situation.

Sales for the Week of Thanksgiving: Manager Projections vs. TimeForge Forecasts

forecast vs manager projections
A comparison of actual sales, TimeForge forecasted sales, and 6-week rolling averages commonly used by managers (manager projections).

Now let’s take a look at accuracy. On the days leading up to and directly following Thanksgiving (11/22), the rolling 6-week average becomes less and less accurate, dipping to under 20% on the Thanksgiving holiday.

TimeForge’s forecasting system, on the other hand, maintains a high accuracy throughout the week. Holidays can be one of the trickiest times of the year to predict, and yet TimeForge can do it with incredible accuracy.

Accuracy of Manager Projections vs. Sales Forecasts for the Week of Thanksgiving

accuracy percent forecast
The accuracy of TimeForge forecasts compared to 6-week rolling averages (manager projections). We can see that TimeForge is extremely accurate in predicting sales across the entire week of Thanksgiving.

And there you have it! TimeForge can accurately predict holiday sales, which means TimeForge can build a better holiday schedule. And not just for the day of Thanksgiving but for the days leading up to and after the holiday.

One of the amazing things about our forecasting system is that it’s independent of platform. Meaning, it’s POS-agnostic. It doesn’t matter what point of sale solution you use. As long as we can integrate it and pull sales, we can make our forecasting engine work hard for you.

Curious to learn more? Contact us – we’d be happy to nerd out even further!

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