Measuring forecast accuracy is one of the most popular ways to measure the quality of your forecast. However, we’d like to suggest some other simple but very valuable analyses you can do to help build confidence in your forecast data. At a minimum, knowing when the forecast was last updated will give you an indication as to how good the data is. We’re not talking about the date the forecast was “due” to be updated by the sales team but rather the last time a particular opportunity changed. This is one way our customers determine how complete their forecast is.
On the Right90 Forecast screen, there are two key “viewable” fields that help you understand if your forecast is complete:
- “Last forecasted date” field tells you when the quantity or price for that particular line item was last forecasted
- “Last updated field” tells you when anything else associated with that line item was updated (i.e. the stage of the opportunity, market application, or other custom fields you have set up).
Before analyzing “last forecasted data” results, we have two more tips for streamlining your view and maximizing the functionality available in the Next Generation Forecaster. First, when you have your forecast data on one page in Right90, filter out zero revenue rows to reduce the amount of unnecessary data. Try filtering revenue greater than zero for just the current month. Then, sort by clicking on the “last forecasted date” column header to bring the older items to the top so that you can focus on items that may not be up to date.
In the example to the right, you can clearly see that quantity and price forecast updates were made in January 2010.
As both a forecaster and a manager, reviewing when the forecast was last changed helps you quickly spot areas that may need a second look. Catching outdated information along the way will help build confidence in your forecast data and improve your forecast accuracy. Have you used these techniques to evaluate the quality of your forecast, or do you have another favorite technique to share?
Tags: Best Practices, features, forecast, sales forecasting, spotlight