What is forecast Accuracy?

Forecast Accuracy: When used in business, forecast accuracy is how well the sales and income teams guess how much growth will happen in the future. Companies depend on accurate sales forecasts to help them make better budget choices, change their sales strategies, and use their short- and long-term resources.

Companies check how accurate their forecasts are by comparing how well their goods and services actually did financially with what they thought they would do in the past.

Forecast error, which is the difference between what was expected and what happened during the set time, is a number that shows how accurate a forecast was.

A lower average mistake means sales forecasts are more accurate and the company is growing steadily.

On the other hand, wrong sales predictions show possible problems in the sales process, like data that isn’t consistent, correct, or stored in different places, revenue growth that isn’t planned, or sales outlets that aren’t profitable.

Synonyms

• Forecast error
• Sales forecasting accuracy

Why Check the Accuracy of Sales Predictions

Forecasts help sales teams, company leaders, and investors make choices that shape the future of their business. If they don’t have enough information or their predictions are wrong, these choices could be based on false ideas that cost money or other resources.

When forecasts are wrong, terrible things happen. Companies may miss out on chances, put too much money into areas with low returns, or try strategies that are doomed to fail if they don’t have accurate forecasting information.

Some risks come with not checking the correctness of forecasts, but there are also many benefits to doing so.

Better Predictions of Demand

Sales success is a good indicator of demand; better sales metrics typically indicate more interested customers.

In the same way, sales predictions tell us a lot about what customers will want in the future. Executives can better help sales teams take advantage of chances and increase sales by knowing what people are interested in and how they feel about it.

If actual sales differ from planned, the company’s leaders can make changes to meet customer needs better and ensure long-term profits.

Setting goals for sales performance

Meeting sales goals and keeping employees motivated are two sides of the same coin. Performance goals need to be attainable to keep sellers motivated, and high participation levels lead to higher quota attainment rates.

A 2022 study from Sales Insights Lab found that less than 25% of sales reps exceeded their quota in the last year. This shows that sales leaders need to do a better job of setting realistic goals and standards.

The first step in setting goals is making accurate sales forecasts. These tell leaders what the sales numbers should be (if the selling process is good).

With this knowledge, they can set realistic quotas and help their sales reps while promoting long-term growth.

Get in touch with investors

When people put money into a business, they expect to get something back (or, at least in the short term, clear information). Part of the process is ensuring that sales reports are correct and sent in on time.

Sales leaders can build trust with investors, keep business relationships going, and get more investment rounds by keeping track of how accurate their forecasts are over time.

These reports can help investors make intelligent choices about their money and figure out where to put their money to get the best results.

Plan your money better.

FinOps teams use accurate forecasts to decide how much money to spend. Unexpected sales numbers can cause resources to be wasted, which can throw off the whole budget plan.

Teams can change their budgets based on how well they think different goods and services will do if they have accurate forecasts.

For instance, if executives want to expand the business but think sales will drop, they can change the budget to meet the extra business costs.

Keep an eye on market trends

There isn’t a good fit between the product and the market, says a CB Insights study. In 42% of cases, founders got market demand wrong because they didn’t understand the wants of their target customers.

When and how to share a product or service is a big part of growing a business, whether B2B or B2C.

Sales forecasts help leaders get a better idea of what customers want and need in the market, and they also help sales reps figure out who the best people are to talk to about making a sale. And making them more accurate means getting more solid information to help your business grow.

Keep inventory levels at a reasonable level.

Keeping the proper inventory on hand is essential for retailers, wholesalers, distributors, and business-to-business makers. Inventory costs more than just what you pay for it at first; over time, it gets more expensive.

Demand will always differ from predicted, but businesses can get closer to the right amounts with accurate sales forecasts.

Improve how resources are used

Companies with few resources may find it hard to decide how to use them, mainly if those resources are spread out among many offices or business units.

For leaders to figure out how many people and how much money is needed for each business area, like marketing, R&D, and customer success, they need accurate sales forecasts. This lets them move resources to the most likely return on investment (ROI) source.

Make strategic plans for your products

Making plans for the future with sales figures is very helpful. They show how the market changes and help business leaders guess what will happen next.

Product developers can make strategic product roadmaps that take advantage of chances and reduce risks once sales leaders know their markets well.

Even small changes to how sales are done can significantly affect how healthy predictions are made and how well-planned projects go.

Make sales strategies as effective as possible

Cutting down on the time and effort spent on activities that don’t bring in sales is one way to improve sales efficiency.

Managers can learn more about which sales strategies work, which types of customers bring in the most money, which leads are the most qualified, and how long sales reps spend on deals before finishing by measuring their predictions’ accuracy.

Formulas for Predicting Accuracy

An organization can check how well its prediction models work in three ways: mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE).

MAPE stands for Mean Absolute Percentage Error.

MAPE gives a clear picture of the average error as a percentage, which is very helpful when checking how accurate forecasts are for different goods, times, or units of measurement.

Calculation of MAPE

This is how you find the mean absolute percentage error:

MAPE = (100%/N) * Σ (|(Actual Sales – Forecasted Sales) / Actual Sales|)

Where:

• N is the total number of observations.
• Σ is the summation symbol, which implies each absolute percentage difference between actual and forecasted sales must be summed up.
• |(Actual Sales – Forecasted Sales) / Actual Sales| is the absolute percentage difference between actual and forecasted sales for each data point.

We must remember that the numbers are increased by 100% since they are percentages.

A case of MAPE

Let’s make up a small company that wants to see how accurate its forecasts are and has the following sales data from the last five days:

• Day 1: Forecasted Sales = 100 units, Actual Sales = 120 units
• Day 2: Forecasted Sales = 120 units, Actual Sales = 110 units
• Day 3: Forecasted Sales = 110 units, Actual Sales = 115 units
• Day 4: Forecasted Sales = 130 units, Actual Sales = 140 units
• Day 5: Forecasted Sales = 140 units, Actual Sales = 130 units

How would you figure out MAPE?

MAPE = (100%/5) * (|(120-100)/120| + |(110-120)/110| + |(115-110)/115| + |(140-130)/140| + |(130-140)/130|)

MAPE = 20% * (16.67% + 9.09% + 4.35% + 7.14% + 7.69%)

MAPE = 20% * 45.94%

MAPE = 9.19%

It turned out that the sales predictions were off by an average of 9.19% in this case.

MAPE gives stakeholders a relative way to measure error percentages, which makes it easy to compare forecasting errors across different units or scales.

What MAPE Can’t Do

MAPE is often used in sales management, but it cannot be used to predict demand accurately.

MAPE figures out the error as a percentage of the real numbers, meaning that times when low demand can significantly affect it. This is because the MAPE formula divides each error by the actual demand, one by one. This means that significant errors during times of low demand will make it seem like demand is higher than it is.

Let’s look at two predictions that are off by ten units each. If one of these errors happened when the actual demand was 20 units and the other when it was 200 units, the first error would add a lot more to the MAPE (a 50% error vs. a 5% error), even though the absolute error would stay the same.

The mean absolute error, or MAE,

Mean absolute error (MAE), also known as mean absolute deviation (MAD), is helpful in business situations where it’s essential to figure out how big a mistake is without looking at its direction.

Calculation of MAE

This is how to find the Mean Absolute Error:

MAE = (1/N) * Σ |Actual Sales – Forecasted Sales|

Where:

• N is the total number of observations.
• Σ is the summation symbol, indicating that each absolute difference between the actual and the forecasted sales needs to be added up.
• |Actual Sales – Forecasted Sales| is the absolute difference between the actual and forecasted sales for each data point.

Taking the absolute value of the difference ensures that all deviations are treated the same, regardless of whether they are above or below the mean. Otherwise, it would not be possible to figure out a percent mistake.

An example of MAE

Look at the sales numbers from the makeup business for the past five days.

To find the Mean Absolute Error (MAE), do the following:

MAE = (1/5) * (|120-100| + |110-120| + |115-110| + |140-130| + |130-140|)

MAE = (1/5) * (20 + 10 + 5 + 10 + 10)

MAE = (1/5) * 55

MAE = 11

In other words, the actual sales were 11 units less than predicted.

What MAE Can’t Do

MAE is a simple and easy-to-understand way to measure prediction error, but it’s essential to understand it in the context of the size of your data. For example, an MAE of 10 might not mean much for a product that sells 1000 units a year, but it might mean a lot for a product that sells 20 units a year.

It’s also important to know the difference between MAPE and MAE because they give different views on how accurate forecasts are. MAE shows the average absolute forecast error in the same units as the original data. MAPE, however, shows the error as a percentage of the actual values.

Because MAE depends on scale, it only works well when all the predicted values have similar ranges and differences. It can’t be used to compare sales data from different units or types, though.

RMSE stands for Root Mean Squared Error.

The RMSE is a strength. It is sensitive to big mistakes because it is squared. It is often used in machine-learning methods. This means that RMSE gives more significant mistakes weight, making it a great way to measure things when big mistakes are really bad.

Finding the RMSE

Root Mean Squared Error can be found by:

RMSE = sqrt[(1/N) * Σ (Actual Sales – Forecasted Sales)^2]

Where:

• Sqrt is the square root function.
• N is the total number of observations.
• Σ is the summation symbol, indicating that each squared difference between the actual and the forecasted sales needs to be added up.
• (Actual Sales minus Forecasted Sales)^2 is the squared difference between the actual and forecasted sales for each data point.

An example of RMSE

Let’s use the same sales numbers from the made-up business from the last five days again.

The root mean squared error (RMSE) would be calculated as:

RMSE = sqrt[(1/5) * ((120-100)^2 + (110-120)^2 + (115-110)^2 + (140-130)^2 + (130-140)^2)]

RMSE = sqrt[(1/5) * (400 + 100 + 25 + 100 + 100)]

RMSE = sqrt[(1/5) * 725]

RMSE = sqrt[145]

RMSE ≈ 12.04

This means that this data set’s root mean squared error is about 12.04 units.

How to Understand RMSE

If reducing significant errors is essential for your prediction, RMSE might be a better choice for an error measure. It’s essential to remember that since RMSE is given in squared units, it might not directly show the average error in the raw measurement units.

One crucial difference is that RMSE doesn’t give each error in the sales estimate the same amount of weight. The biggest mistakes are considered the most important, so it would only take one big mistake to give a bad RMSE, even if the total number of mistakes or business effects isn’t that big.

Things that affect how accurate forecasts are

The forecasting process is affected by many moving parts, such as the amount of sales, the level of aggregation, and the length of time in the recorded period.

Number of Sales

Most of the time, more sales mean better accuracy. Patterns and trends in data are often easier to spot when the sample is larger.

Level of Aggregation

Putting together goods or SKUs with similar features can help the accuracy of forecasts. Putting things together that behave similarly helps everyone see how the sale of one item might be connected to the sale of another.

How Long It Takes

Companies can get a better idea of how regular sales patterns or cycles might change their predictions when they zoom out. Companies can often spot more minor changes in sales trends when they look at them over a more extended period.

Possible Causes of Wrong Predictions

When running a business, it’s important to make accurate sales predictions so that supply and demand are met and processes run smoothly. But making predictions is a complicated process, and mistakes can happen everywhere. A lot of mistakes are made when predicting sales because of these things:

Based Only on Sales History

Even though past sales data is a great way to guess what will happen with future sales, it’s not perfect. The market is constantly changing, and the sales past doesn’t show everything that affects sales. For example, changes in the economy, the market, the competition, or customer tastes can affect sales. The prediction model can become out of date and wrong if these things aren’t taken into account.

Inventory-level agreements that have already been set

Agreements with sellers about the amount of inventory can often cause mistakes in forecasting. If the inventory levels are set too high, there may be too much stock, making it more expensive to keep. If it’s set too low, there might not be enough stock, and sales will be lost. To reduce these mistakes, inventory levels should be set by accurate predictions of demand instead of deals made at random.

Stocking up on things that are rarely used

Keeping things in stock that don’t sell very often can throw off the forecast. These things can make the general inventory look bigger, leading to wrong assumptions about the demand.

When making forecasting choices, it’s essential to look over the product line on a regular basis and think about how fast each item sells.

Silos of data

Many essential data is kept separate in many businesses, in systems or teams, like CRM, ERP, revenue intelligence, and subscription management. This can make it hard to see the whole business and cause mistakes in predictions.

For forecasting, it’s essential to ensure all the necessary data is combined and easy to get to. Not only will this make forecasts more accurate, but it will also help everyone in the business make better decisions.

Sales reps who don’t talk about changes in the market or with customers

Most of the time, sales reps are the first to notice when the market or customers’ habits change. If this information isn’t adequately shared with the predicting team, it could lead to wrong predictions.

To avoid this, it’s essential for the sales team and the people in charge of planning to stay in touch. Regular meetings and reports can help ensure that all the essential information is shared and added to the forecast.

How can you get better at predicting sales?

Different strategies are needed to make your sales forecasts more accurate. These strategies rely on your business’s size, the type of product or service you offer, your sales cycle, and your internal processes.

Here are some general things you can do to make your sales predictions more accurate:

• Use more than one way to make predictions. If you only use one method to make predictions, your results may be affected by its flaws and limits. A more complete and accurate forecast can be made using quantitative (like time-series analysis and regression analysis) and qualitative (like market research and the views of sales staff) forecasting methods together.
• Use information about the market. Knowing about market trends and changes in how people act, you can make more accurate sales predictions. Read industry studies and market research regularly, and ask your sales team what they think about what they’re seeing on the ground.
• Make your sales stack work together. Break down the walls between your company’s data silos and make sure your forecasting model can access all of your sales, marketing, customer service, and outside data, such as market trends and economic factors.
• Check the correctness of your forecasts often. Do not “set it and forget it” when making sales predictions. When new sales information comes in or the market changes, you should always go back and change your predictions.
• Use software to make predictions. There are many tools for making sales forecasts that can automate much of the work and use complicated algorithms to make more accurate predictions.