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Forecast Accuracy

Forecast accuracy is critical to forecasting and decision-making in various industries like entrepreneurs, economics, supply chain management, and finance. Accurate forecasts allow organizations to make firm decisions and use allocated resources wisely.

What is forecast accuracy?

Forecast accuracy measures how effectively a forecasting model predicts future values compared to the actual observed values. It is a critical evaluation metric used to evaluate the reliability and validity of forecasts. The aligned goal of forecast accuracy is to minimize the discrepancy between forecasted values and the actual outcomes.

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Why is forecast accuracy important?

Forecast accuracy is essential in various ways:

  1. Proper decision-making
  2. Proper planning and budgeting
  3. Supply chain optimization
  4. Risk management
  5. Customer satisfaction
  6. Competitive advantage
  7. Assessment of models and techniques
  8. Financial performance

  1. Proper decision-making: Accurate forecasting allows appropriate and validated insights into future events and various trends. Decision-makers can use specific knowledge to develop well-informed and forecasted choices, which will lead to better outcomes and reduced risks.
  2. Proper planning and budgeting: Governments and businesses rely on forecasts to establish a plan for the future, as accurate forecasts allow for better long-term planning, like investment decisions and setting realistic financial goals.
  3. Supply chain optimization: In supply chain management, accurate forecasting is essential for coordinating activities and meeting customer demands. Supply chain partners can collaborate more effectively as they have reliable predictions of further demands.
  4. Risk management: Forecast accuracy plays an essential role in risk evaluation and minimization. ,An organization can develop a backup plan and form adverse events by predicting accurate potential risks.
  5. Customer satisfaction: Accurate forecasts allow businesses to meet customer demands on time, enhancing customer satisfaction and loyalty; satisfied customers are more engaged and like to make repurchase decisions.
  6. Competitive advantage: Organizations that can accurately forecast future trends and market changes help to gain a competitive edge. They can respond to market shifts and capitalize on better opportunities.
  7. Assessment of models and techniques: Forecast accuracy serves as a metric to evaluate the performance of different forecasting models and tools, as it helps to determine methods that are pretty effective.
  8. Financial performance: Trusted forecasts are directly related to better financial performance. For businesses, accurate predictions impact revenue generation and better profitability.

How to calculate forecast accuracy?

To calculate forecast accuracy, there are the following steps:

  1. Gather data
  2. Calculate forecast errors
  3. Choose forecast accuracy metrics
  4. Calculate forecast accuracy metric

  1. Gather data: Gather the actual values are the corresponding forecasted values for the observations that need to be evaluated. This ensures a sufficient number of data points for a meaningful evaluation.
  2. Calculate forecast errors: For every observation, calculate of error forecast by subtracting the forecasted value from the actual value. The forecast error represents the difference between the forecasted and the actually occurred.
  3. Choose forecast accuracy metric: Decide on the forecast accuracy metric based on the nature of your data and the specific goals of the analysis.

Forecast metrics include:

Mean absolute error (MAE)

Mean squared error (MSE)

Root mean squared error (RMSE)

Mean absolute percentage error (MAPE)

Symmetric mean fundamental percentage error (SMAPE)

     4. Calculate forecast accuracy metric: Use the formula corresponding to the close accurate metric to calculate the forecast accuracy; the formulas were provided in the previous responses. The formulas are as follows:

Mean Absolute Error (MAE): MAE = (1 / n) x Σ|Actual - Forecast|
Mean Squared Error (MSE): MSE = (1 / n) x Σ(Actual - Forecast)^2
Root Mean Squared Error (RMSE): RMSE = √[(1 / n) x Σ(Actual - Forecast)^2]
Mean Absolute Percentage Error (MAPE): MAPE = (100 / n) x Σ[|(Actual - Forecast) / Actual|]
Symmetric Mean Absolute Percentage Error (sMAPE): sMAPE = (100 / n) x Σ[|Actual - Forecast| / (|Actual| + |Forecast|)]

What are the common mistakes of forecasting?

The common mistakes in forecasting are as follows:

  1. Underestimate uncertainty
  2. Lack of expert input
  3. Misinterpretation of historical data
  4. Neglecting external factors
  5. Insufficient data
  6. Overfitting
  7. Groupthink

  1. Underestimate uncertainty: Forecasting involves dealing with uncertainty.Overconfidence in short forecasts without accounting for uncertainty can lead to flawed decision-making and inadequate preparations for unexpected events.
  2. Lack of expert input: Relying solely on automated forecasting algorithms without incorporating domain knowledge can lead to overlooking crucial factors that impact the future.
  3. Misinterpretation of historical data: Failing to consider relevant historical data or misinterpreting its patterns can follow a biased forecasting process. Historical data offers insights into past trends and valuable resources for making decisions.
  4. Neglecting external factors: Sometimes forecasts may be overly focused on historical data and fail to consider external factors like economic changes, market trends, or policy shifts that can influence further outcomes.
  5. Insufficient data: Forecasting models require sufficient and relevant data to make accurate predictions. insufficient data can compromise the accuracy of forecasts.
  6. Overfitting: Overfitting occurs when the forecasting model is overly complex and fits the data instead of actual data, but failing at generalizing new data can affect forecast accuracy poorly.
  7. Groupthink: Relying on consensus forecasts or failing to consider dissenting opinions can lead to biased forecasts after the identification of other scenarios.

How can we improve forecast accuracy?

Improving forecast accuracy requires a systematic approach that includes refining forecasting techniques, utilizing efficient data, and incorporating expert judgment. Some strategies to improve forecast accuracy are:

  1. Use multiple data sources
  2. Choose appropriate forecast method
  3. Consider external factors
  4. Ensemble forecasting
  5. Quick data collection
  6. Expert judgement
  7. Regular scenario analyis

  1. Use multiple data sources: Incorporate diverse data sources to get a comprehensive view of the features influencing the forecast. Combining internal data, external data, and opinions can lead to more accurate forecasts.
  2. Choose appropriate forecast method: Choose suitable forecasting techniques based on data characteristics, patterns, and the specific nature of the problems.
  3. Consider external factors: Consider external factors like economic indicators, changes, and market trends that can influence the forecasted outcomes.
  4. Ensemble forecasting: Consider using ensemble prediction, which combines multiple forecasting models to take advantage of their strengths and lower model biases.
  5. Quick data collection: Allows access to relevant, accurate, and high-quality data that covers a large historical period; data errors and inconsistencies can follow inaccurate forecasts.
  6. Expert judgment: Combine data forecasting with the insights of domain experts. The expert review allows for identifying qualitative features and potential events that may not be captured.
  7. Regular scenario analysis: Consider scenario analysis to explore various potential futures and potential impacts on the forecast.

Does forecast accuracy matter to security analysis?

Yes, forecast accuracy is essential in security analysis. Security analysis involves assessing different scenarios related to financial instruments, like stocks, bonds, and various other securities, to make decisions. Forecast accuracy is essential to guide investors and analysts in knowing the further performance of these securities and making investment choices.

Employee pulse surveys:

These are short surveys that can be sent frequently to check what your employees think about an issue quickly. The survey comprises fewer questions (not more than 10) to get the information quickly. These can be administered at regular intervals (monthly/weekly/quarterly).

One-on-one meetings:

Having periodic, hour-long meetings for an informal chat with every team member is an excellent way to get a true sense of what’s happening with them. Since it is a safe and private conversation, it helps you get better details about an issue.


eNPS (employee Net Promoter score) is one of the simplest yet effective ways to assess your employee's opinion of your company. It includes one intriguing question that gauges loyalty. An example of eNPS questions include: How likely are you to recommend our company to others? Employees respond to the eNPS survey on a scale of 1-10, where 10 denotes they are ‘highly likely’ to recommend the company and 1 signifies they are ‘highly unlikely’ to recommend it.

Based on the responses, employees can be placed in three different categories:

  • Promoters
    Employees who have responded positively or agreed.
  • Detractors
    Employees who have reacted negatively or disagreed.
  • Passives
    Employees who have stayed neutral with their responses.

Can forecast accuracy be negative?

No, forecast accuracy is negative. Forecast accuracy is to measure how well a forecasting model predicts further values compared to the actual observed values. It is a non-negative value that presents the degree of uncertainty between the forecasted values and actual outcomes.

Forecast accuracy is expressed as the non-negative value that indicates the level of error between the forecasted value and the actual values. If the forecast accuracy is less than 100%, that means there is some level of error between the forecasted value and the actual values.

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