Forecast Accuracy

What is Forecast Accuracy? 

In a business context, forecast accuracy is the degree to which sales and revenue teams successfully predict future growth. Companies rely on accurate sales forecasts to refine their budgeting decisions, adjust sales strategies, and optimize resources for the short and long term.

To measure forecast accuracy, companies compare the actual financial performance of their products and services against historical projections.

The numerical representation of forecast accuracy is the forecast error — the discrepancy between forecasts and actual performance over the set period.

A lower average error indicates greater accuracy in sales forecasting and underscores sustainable growth within the company.

Conversely, inaccurate sales forecasts highlight potential issues in the sales process, such as inconsistent, inaccurate, or siloed data, haphazard revenue growth, or unprofitable sales channels.


  • Forecast error
  • Sales forecasting accuracy

Why Measure Sales Forecast Accuracy

Sales teams, company executives, and investors use forecasts to make decisions that drive their company’s future. If their forecasts are ill-informed or inaccurate, these decisions can be based on false assumptions and lead to losses in revenue or other resources.

The consequences of forecast errors are dire. A lack of accurate forecasting insights can cause companies to lose out on potential opportunities, overinvest in areas with low returns, or pursue strategies that are doomed to fail.

Aside from the risks associated with not measuring forecast accuracy, there are plenty of benefits to doing so.

Better Demand Predictions

Sales performance reflects demand — better sales metrics generally indicate higher customer interest.

In the same way, sales forecasts provide valuable insights into future customer demand. By understanding the ins and outs of product and service interest, executives can better equip sales teams to capitalize on opportunities and drive revenue growth.

If there are discrepancies between projected sales and actual sales, company leadership can adjust their operations to better meet demand and ensure long-term profitability.

Set Sales Performance Goals

Sales quota attainment and employee engagement have a bilateral relationship — performance goals need to be realistic to keep sellers engaged while high engagement yields statistically higher quota attainment percentages.

Less than 25% of sales reps exceeded quota in the last year according to a 2022 study from Sales Insights Lab, which underscores a significant gap in sales leaders’ ability to set realistic goals vs. expectations.

Accurate sales projections are the start of the goal-setting process — they tell leaders what the sales numbers should be at (assuming the selling process is sound).

With this information, they can set attainable quotas that support their reps while driving sustainable growth.

Communicate With Investors

When investors sink their money into a company, they expect returns (or, in the short term, transparent communication). Timely and accurate sales reporting is a major part of the process.

By tracking forecast accuracy over time, sales leaders can build trust with investors, maintain business relationships, and encourage future investment rounds.

Investors can use these reports to make informed decisions about their investments and determine the best areas to invest in for higher returns.

Improve Financial Planning

FinOps teams rely on forecast accuracy to make budgeting decisions. Unpredictable sales figures can lead to a misallocation of resources, throwing off the entire financial plan.

Accurate forecasts allow teams to adjust their budgets according to the expected performance of different products and services.

For example, if executives are planning an expansion project but anticipate a slowdown in sales, they can adjust the company budget to cover additional overhead expenses.

Monitor Market Trends

According to a CB Insights report, the top reason startups fail is a lack of product-market fit. In 42% of cases, founders failed to understand the needs of their target customers and misjudged market demand.

A huge part of successfully growing a company (B2B or B2C) is understanding when and how to release a product or service.

Sales forecasts give executives a clearer picture of customer interest and preferences in the marketplace, and they show reps who the best targets for their sales efforts might be. And improving their accuracy means more reliable insights for business growth.

Ensure Reasonable Inventory Levels

For retailers, wholesalers, distributors, and B2B manufacturers, maintaining appropriate inventory levels is essential to a successful business. It’s more than just an upfront cost — inventory becomes increasingly expensive over time.

Actual demand will always vary, but accurate sales forecasts can get businesses closer to the right levels.

Optimize Resource Allocation

Allocation can be a big challenge for companies with limited resources, especially when those resources are spread between multiple departments or business units.

Accurate sales forecasts enable leaders to effectively calculate how many people and capital need to go into each business area — from marketing to R&D to customer success — and shift resources to the most likely source of ROI.

Develop Strategic Product Roadmaps

Sales forecasts are a great way to plan for the future. They visually represent current market trends and help executives anticipate what might come down the line.

Once sales leaders understand their markets, product developers can develop strategic product roadmaps that capitalize on opportunities and minimize risks.

Even small changes in the sales process can make a big difference in forecasting accuracy and the success of planned initiatives.

Maximize Efficiency of Sales Strategies

Improving sales efficiency means reducing the time and effort spent on activities that don’t generate value.

Measuring forecast accuracy helps managers better understand which sales methodologies work, which customer segments generate the most revenue, who the most qualified leads are, and how much time sales reps spend on deals before closing.

Forecast Accuracy Formulas

There are three calculations businesses use to measure the accuracy of their forecasting models: mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE).

Mean Absolute Percentage Error (MAPE)

MAPE provides a clear representation of the average error as a percentage, which is particularly useful in comparing forecast accuracy calculations across different products, times, or units of measure.

MAPE Calculation

The formula for Mean Absolute Percentage Error is:

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


  • 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 the actual sales and the forecasted sales for each data point.

It’s important to note that since we’re dealing with percentages, the results are multiplied by 100%.

Example of MAPE

Let’s take a small fictional company that wants to measure forecast accuracy and has sales data for the last five days as follows:

  • 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

MAPE would be calculated as:

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%

In this case, on average, the sales forecasts were off by about 9.19% from the actual sales.

MAPE gives stakeholders a relative measure of error percentages, making comparing the forecasting errors between different scales or units easier.

Limitations of MAPE

While MAPE is a widely used metric in sales management, it has a few limitations when making a demand forecast.

MAPE calculates the error as a percentage of actual values, so it can be heavily influenced by periods of low demand. This is because, in the MAPE formula, each error is divided individually by the actual demand, so large errors during periods of low demand will inflate it.

Let’s consider two forecasts with errors of 10 units each. If one of these errors occurred when the actual demand was 20 units, and the other when the actual demand was 200 units, the former would contribute much more to the MAPE (a 50% error as opposed to a 5% error) despite the absolute error being the same.

Mean Absolute Error (MAE)

Mean absolute error (MAE) — also called mean absolute deviation (MAD) — is especially handy in business scenarios where it’s essential to assess the magnitude of error without considering their direction.

MAE Calculation

The formula for Mean Absolute Error is:

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


  • 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 sales and the forecasted sales for each data point.

The reason for taking the absolute value of the difference is to ensure that the deviations are treated equally, regardless of whether they are above or below the mean. Otherwise, calculating a percent error would be impossible

Example of MAE

Let’s continue with the same sales data from the fictional company for the last five days.

The Mean Absolute Error (MAE) would be calculated as:

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

So, on average, the forecasted sales deviated by 11 units from the actual sales.

Limitations of MAE

Although MAE provides a simple and intuitive measure of forecast error, it’s essential to interpret it relative to the scale of your data. For instance, an MAE of 10 may be insignificant for a product with an average demand of 1000 units, but substantial for a product with an average demand of 20 units.

The distinction between MAPE and MAE is also crucial, as the two metrics offer different perspectives on forecast accuracy. While MAE presents the average absolute forecast error in the same units as the original data, MAPE expresses the error as a percentage of actual values.

Since MAE is scale-dependent, it works well when all the forecasted values have similar ranges and variations but cannot be used for comparisons across different units or types of sales data.

Root Mean Squared Error (RMSE)

Commonly used in machine learning algorithms, the strength of RMSE lies in its sensitivity to large errors due to the squaring operation. This means that RMSE gives more weight to significant errors, making it an ideal measure when large errors are particularly undesirable.

RMSE Calculation

The formula for Root Mean Squared Error is:

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


  • 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 – Forecasted Sales)^2 is the squared difference between the actual sales and the forecasted sales for each data point.

Example of RMSE

Again, let’s use the same sales data from the fictional company for the last five days.

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

Therefore, the Root Mean Squared Error for the given data set is approximately 12.04 units.

Interpretation of RMSE

If minimizing large errors is crucial in your forecast, RMSE can be a more suitable choice of error metric. However, it’s also important to note that because RMSE is in squared units, it may not directly represent the average error in the original units of measurement.

A critical distinction with RMSE is that it doesn’t give each sales forecast error the same weight. The most significant errors are regarded as most important, so one big error would be enough to return a bad RMSE, even if the overall error frequency or business implications aren’t significant.

Factors Impacting Forecast Accuracy

Several moving parts impact the forecasting process, including sales volume, aggregation level, and the length of time in the period being measured.

Sales Volume

Higher sales volumes generally lead to greater accuracy. With larger datasets, it’s often easier to identify patterns and trends in the data.

Aggregation Level

Aggregating products or SKUs based on similar characteristics can be beneficial for forecast accuracy. Grouping together items that have similar behavior helps stakeholders understand how the sale of one product might be related to another.

Length of Time 

When companies have the ability to zoom out, they can better understand the seasonal sales patterns or cycles that may affect their forecasts. By considering a longer time frame, companies are often able to identify more subtle changes in sales trends.

Potential Sources of Forecasting Errors

When running a business, accurate sales forecasting is crucial to align supply with demand and maintain efficient operations. However, forecasting is a complex process, and errors can easily creep in from various sources. Here are some of the most common sources of sales forecasting errors:

1. Relying on Sales History Alone

While historical sales data is an invaluable resource in forecasting future sales, it isn’t infallible. Market conditions change constantly, and many factors that affect sales aren’t reflected in the sales history. For instance, changes in competition, market trends, consumer preferences, or economic conditions can all influence sales. If these factors aren’t taken into account, the forecasting model can become outdated and inaccurate.

2. Pre-Determined Inventory-Level Agreements

Inventory-level agreements with suppliers can often lead to forecasting errors. If the inventory levels are set too high, there may be excess stock, leading to increased holding costs. If set too low, it could result in stock-outs and lost sales. To minimize these errors, inventory levels should be determined based on accurate demand forecasts rather than arbitrary agreements.

3. Keeping Rarely Used Items in Stock

Holding stock of items that are rarely sold can distort the forecast. These items can cause the overall inventory to appear larger than necessary, leading to overestimation of demand.

It’s important to regularly review the product range and consider the sales velocity of each item when making forecasting decisions.

4. Data Silos

In many organizations, important data is often kept in silos within different departments or systems, such as CRM, ERP, revenue intelligence, and subscription management. This can lead to a fragmented view of the business and result in forecast errors.

It’s important to ensure that all relevant data is integrated and accessible for forecasting. Doing so will not only improve forecast accuracy but also enable better decision-making across the organization.

5. Sales Reps Keeping Quiet About Market or Customer Changes

Sales reps are usually the first to notice changes in the market or customer behavior. If this information is not communicated effectively to the forecasting team, it can result in inaccurate forecasts.

To prevent this, it’s important to maintain open lines of communication between the sales team and those responsible for forecasting. Regular updates and meetings can help ensure that all relevant information is shared and incorporated into the forecast.

How Do You Improve Sales Forecasting Accuracy?

Improving sales forecasting accuracy involves a combination of strategies that depend on various aspects of your business, including the size of your company, the nature of your product or service, your sales cycle, and internal processes.

Here are some general strategies that can enhance the accuracy of your sales forecasts:

  • Use multiple forecasting methods. Relying on a single forecasting method exposes your predictions to its limitations and biases. Using a combination of quantitative (e.g., time-series analysis, regression analysis) and qualitative (e.g., market research, sales force opinions) forecasting methods can provide a more comprehensive and accurate forecast.
  • Incorporate market intelligence. Understanding market trends and shifts in consumer behavior can significantly improve the accuracy of your sales forecasts. Regularly review industry reports, conduct market research, and solicit input from your sales team about what they’re seeing on the front lines.
  • Integrate your sales stack. Break down data silos within your organization and ensure your forecasting model has access to all sales, marketing, customer service, and external data like market trends and economic indicators.
  • Regularly measure forecast accuracy. Sales forecasts aren’t a “set it and forget it” endeavor. Regularly review and adjust your forecasts as new sales data comes in and as market conditions change.
  • Use forecasting software. There are numerous sales forecasting tools available that can automate much of the forecasting process and apply complex algorithms to generate more accurate forecasts.

People Also Ask

What is a good level of forecast accuracy?

According to most sources, an accuracy within 10% to 20% of your day-1 forecast is considered a good level of forecast accuracy. The exact desired accuracy rate will vary depending on the industry and specific sales forecasting needs of the business.

Is a higher or lower MAPE better?

A smaller MAPE value signifies a greater degree of accuracy in the forecast – a 0% MAPE implies that the prediction perfectly matches the actual outcome, whereas a larger MAPE value suggests a less precise prediction.