Most sales forecast accuracy is under 90% because predictions from the sales team are usually wrong. Sales forecasts are often fraught with bias and rely too heavily on sales managers’ interpretations of the pipeline deals. Despite this, every quarter sales leaders make new forecasts that rely on the same old tricks. When the quarter ends, we should not be surprised when our forecast misses again (either ahead or behind).
79% of sales organizations miss their forecast by more than 10% – SiriusDecisions
Measure Sales Forecast Accuracy
If you’re asking yourself, “How accurate are my forecasts?” then you are taking the first step toward a more accurate prediction. If you are routinely within 10% with your Day 1 Forecast then you should feel pretty good. If not, it is time to find a way to improve your forecasts. Like most things in business, the fastest way to improve is to measure your current process.
Sales forecast accuracy is defined as the difference between the Day 1 Forecast and Actual Sales for the quarter (or any reporting period) as a percentage of Actual Sales.
(Day 1 Forecast – Actual Sales) / Actual Sales * 100%
Day 1 Forecast – The first forecast made for the quarter (or any reporting period). It is the most important forecast to measure because it sets the expectation for the quarter. That being said, as the quarter progresses and more information becomes available, you should revise your forecast as necessary to run your business.
Actual Sales – The cumulative sales closed in the quarter (or any reporting period). Comparing the total cumulative sales closed to overall forecast is straight forward, but it requires some attention if you break your forecast down into components (i.e. business unit). It is important to compare the forecast to the actual sales it was trying to predict.
$100 Dollar Example
Day 1 Forecast is $90
Actual Sales are $100
($90 – $100) / $100 *100% = -10%
Under forecasted actuals by 10%
Forecasting What You Have and What You Expect
On the first day of a quarter, you make a sales forecast based on what you currently have in your pipeline. This is called the Inter-Quarter Forecast. It represents only a portion of your total forecast. The rest of your forecast is comprised of deals that are not in the pipeline on Day 1, but will arrive and win within the quarter. This is called the Intra-Quarter Forecast. The proportion of Inter-Quarter to Intra-Quarter depends on your sales cycle. If you have a 45-day sales cycle, for example, 50% of your forecast might be Intra-Quarter.
Inter-Quarter Forecast – The forecast based on the value of your existing pipeline on the first day of the quarter.
Intra-Quarter Forecast – The forecast based on the expectation of deals that will arrive and close in the quarter.
Total Forecast – Inter-Quarter + Intra-Quarter + Won
Record Your Forecast
After you determine Total Forecast it is important to write it down. You should track how you are doing compared to your forecast and record any changes you make from week to week. If you have a predictive analytics platform you need make sure it is tracking your forecasts. Without recording how you do, there is no chance to improve.
The figure below is one way to record a series of forecasts. The blue area is the Inter-Quarter Forecast. It represents the remaining in-quarter expectation for all deals that have been in the pipeline since Day 1. The black area is the Intra-Quarter Forecast. This represents the sales expectation from deals that were not in the pipeline on Day 1. The teal area is Actual Sales. This is the total value of all won deals from the current quarter.
Recording a series of forecasts in this way helps us understand how accurate we forecast. The Day 1 Forecast accuracy, in this case, is -4.2%. This is based on the forecast made on April 3rd of $8.3M and Actual Sales of $8.66M. It is also good to note that the Inter-Quarter Forecast makes up about 67% of the total forecast. This is a good number to track in order to pick up changes in your business.
As time moves forward the Inter-Quarter and Intra-Quarter expectations decrease and Actual Sales replace them. If the Actual Sales do not increase at the same pace that your expectations decrease then you will miss your forecast. In this example, the average weekly absolute variance is 4.2% for the quarter. Now, if you are within 5% overall this means you are in the “best-in-class” in forecasting.
Now that you understand how to measure your Day 1 forecast accuracy you are ready to improve your sales forecasts. Start by documenting your current process and recording your forecasts. As you start this process you might find that you have room to improve your process. Do not let that sway you from your path toward improvement; your forecasts will get better. ORM Technologies specializes in the application of Optimized Analytics for sales and marketing. If you have questions or would like help with your sales forecast, do not hesitate to send us an email at email@example.com.
For more information on sales forecasting, our white paper on The Importance of Sales Forecasting goes in-depth on refining your forecasting method to improve its accuracy.