Accurate sales forecasting benefits every part of your business, from human resource management, inventory, supply chain, and warehousing, to sales, marketing, and financial activities. That’s why you should focus on creating an accurate forecast right from the beginning.
In this article, let’s dive into sales forecasting accuracy, how to calculate it, what mistakes you might make when doing it, and tips you can follow to increase the accuracy of your sales forecasting
Forecasting Accuracy Definition
Sales forecasting accuracy refers to how accurate your sales forecast is. It tells how close you come to actually hit your projections.
When you get your sales forecasting right, you can reduce guesswork and ensure accurate budgeting. You can also make more informed decisions when setting goals, hiring, prospecting, and other revenue-impacting activities. The more accurate your forecasting, the more likely your sales reps will hit their quota.
Importance of Forecast Accuracy in Business
Forecast accuracy is crucial in business as it enables companies to make informed decisions about production, inventory, and resource allocation. Accurate forecasts help businesses to reduce uncertainty, improve product availability, minimize spoilage, optimize inventory turnover, and enhance overall supply chain efficiency. By measuring forecast accuracy, businesses can identify areas for improvement and make data-driven decisions to drive growth and profitability.
Why Measure Forecast Accuracy?
Measuring forecast accuracy is essential for businesses to evaluate the quality of their demand forecasts and identify areas for improvement. By tracking forecast accuracy, businesses can:
- Reduce uncertainty and improve decision-making
- Optimize inventory levels and minimize stockouts
- Improve product availability and reduce spoilage
- Enhance overall supply chain efficiency
- Drive growth and profitability
Forecast Accuracy Formula
Forecast accuracy formulas are a way to determine the accuracy of your forecasts. They’re especially useful for businesses that rely on economic forecasts or market predictions, as well as for any other type of organization that needs to predict future outcomes.
Here are three widely-used forecast accuracy formulas:
Mean Absolute Percentage Error
Mean Absolute Percentage Error (MAPE) is a common method for calculating sales forecast accuracy by comparing forecasted values to actual values. It’s calculated by taking the difference between your forecast and the actual value, and then dividing that difference by the actual value.
Mean Absolute Error
The Mean Absolute Error (MAE) is another way to measure the accuracy of an estimate by taking the absolute value of the difference between your forecast and the actual value. It’s calculated by taking the difference between your forecast and the actual value, and then dividing that difference by the square root of your sample size.
Root Mean Squared Error
Root Mean Squared Error (RMSE) is one of the key forecast accuracy calculations and is considered a better indicator than MAPE or MAE. RMSE is the square root of MAE and can be used to compare models with vastly different lengths—MAPE and MAE depend on actual value, but RMSE doesn’t.
Factors Impacting Forecast Accuracy
Several factors can impact forecast accuracy, including:
Sales Volume
Higher sales volumes generally lead to greater accuracy in forecasting. With larger datasets, it’s often easier to identify patterns and trends in the data, which can improve forecast accuracy.
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.
By understanding these factors, businesses can improve their forecast accuracy and make more informed decisions about production, inventory, and resource allocation.
Common Sales Forecasting Accuracy Mistakes
There are many reasons why your sales forecasts are inaccurate. Here are some:
Understanding these common forecast errors can help you avoid them and improve your forecasting accuracy.
1. Rely on Assumptions and Gut Feelings
It’s natural for decision-makers to predict sales performance based on their expertise and experiences.
That said, your feelings aren’t often the best indicators of what’s actually happening and what’s to come. When you depend too much on guesswork and hunches, your forecasts are far less likely to close as predicted, leading to consequences on your business’s operations, growth, and reputation.
2. Use Spreadsheets That Reside in Silos
Spreadsheets can be an efficient, low-cost tool for sales forecasting if you’re running a start-up or a small business.
But as your company grows, you shouldn’t use spreadsheets for sales forecasting for many reasons. Think about data integrity issues, poor collaboration, difficulty in incorporating historical data, and lack of a complete picture of your sales performance. That doesn’t mention that spreadsheets are static; meanwhile, you need real-time visibility.
3. Ignore Past Data
Historical data can tell a lot about your future sales performance. It shows you your top-performing sales reps, the number of opportunities and deals your team converted over a given period of time, what activities they did best, what areas they need support from, and more.
If you ignore the past sales performance of your sales reps, you may create forecasting that might not be possible for your team to achieve.
How to Improve Sales Forecast Accuracy
When calculating sales forecast accuracy, mistakes are inevitable. But there are some things you can do to improve your forecasting process. By using various metrics to calculate forecast accuracy, you can identify areas for improvement and make data-driven decisions.
1. Establish a Sales Process for Your Team
Accurate data is a solid foundation for accurate sales forecasting. That’s why you need to have a well-established sales process from the get-go. Break down the process into clear steps, determine who handles each step, build a set of metrics to measure performance, etc. Doing that will ensure everyone in your team has the same understanding of qualified leads, opportunities, and closed deals. Effective demand planning ensures that your forecasts align closely with actual demand, improving overall accuracy.
2. Choose a Sales Forecasting Method
Intuitive forecasting, historical forecasting, and length of sales cycle forecasting are common examples of forecasting models. You don’t need to use all of them. The key is to decide the method that best fits your business.
For example, if your business is new, the best forecasting model can be intuitive forecasting. But if you have a lot of historical sales data, you may consider using the historical forecasting model.
3. Invest in a Forecast Accuracy Automation Tool
A sales forecasting tool like Revenue Grid helps your sales team predict future sales growth more accurately because it provides real-time tracking and full visibility of your sales performance.
- Analyze trends, changes, and seasonality of your sales performance over time. Display insights on an intuitive, real-time dashboard, helping you understand the sales progress of each sales rep, the number of sales they’ve made, and their projection.
- Get alerts on risks, understand the reasons behind changes, and get recommendations to mitigate those risks.
- Compare forecasts with past periods and offer data visualizations (charts, graphs, and more) to present trends.
Maintaining sales forecasting accuracy gives your sales team a powerful weapon to achieve their sales target and drive sales growth. Understanding forecast error is crucial for making adjustments and improving the accuracy of your sales forecasts.
Improve your sales forecasting accuracy with Revenue Grid
How to achieve sales forecasting accuracy with Revenue Grid
A revenue intelligence platform is a unique tool that can help to make your forecasts play out. Let’s go through several simple steps on how to improve your sales forecasting accuracy with Revenue Grid revenue intelligence solution:
1. Submit accurate forecasts across your sales team and update them in time with sales forecasting cadences:
- Submit and adjust individual and team forecasts for future periods
- Review your quota, override, and track changes across all forecast categories (Commit, Best Case, Pipeline, Booked revenue)
- Slice and dice any forecast by vertical, product, or time period.
An accurate demand forecast is essential for making informed decisions and achieving sales targets.
2. See the pipeline growth during the selected period with pipeline evolution reports and adjust your revenue projections when needed:
- See the total pipeline value at the beginning and end of a selected period
- Analyze opportunities across Forecast Categories
- Understand the Forecast Category and Opportunity Stage relationship
3. See exactly where you’ll end your quarter with Salesforce-native forecast evolution reports:
- Create reports for forecast categories (Commit, Best Case, Pipeline, Booked revenue)
- Perform retrospective analysis per specific sales teams or reps
- Understand what has changed since the previous week’s forecast call with a single click
4. Guide your team at each stage of sales forecasting process with Revenue Signals:
- Set up Revenue Signals to remind your team to submit forecasts on time.
- Update forecast figures in one click as soon as the opportunity category changes
- Receive Revenue Signals about important changes in the forecast in real-time
Following these simple steps with a revenue intelligence solution will make your forecasts more accurate.
