Revenue Forecasts asserts that a certain amount of revenue will be earned in a certain period of time with a certain probability that the actual revenue earned in the period will be within a certain tolerance of the forecast. For example, management may forecast that there is a 90% chance of actual revenue being not more than 10% less than a forecasted amount.

Generally speaking, the percent probability of revenue from a source is assigned by management based on their judgement in light of their collective past experience with similar revenue generating opportunities in similar circumstances.

Some managers set forecasts equal Expected Value; that is, their revenue forecast equals the sum of potential revenue generating opportunities each multiplied by an assigned probability of occurring. There are several potential problems with this approach that should be considered carefully before proceeding:

Summing expected values allows fractional results when there may actually be little to no chance of fractional results. For example, an opportunity to generate revenue of $100,000 with a 50% probability of occuring would contribute $50,000 to a forecast computed as a weighted sum even though the actual result is more likely to either be $0 or $100,000. Actual Results are more likely to binary; that is the the result either happens or it does not happen, so fractional results do not occur.

The percent probability assigned to a given opportunity may reflect the probability of the revenue ever occuring but not be specific to the period in which the revenue will occur. A good approach to forecasting needs to set the probability of revenue in a specific period

Probabilities assigned often reflect stage of progression through a sales process but do not represent a real assessment of probability of the event occurring. For example, one might assign a 75% probability to all prospective sales for which a proposal has been submitted because it is, at the point of submission, about 75% of the way through the sales process but it may be, in reality, that only 50% of all proposals are successful.

Failure to account for any or all of the three cautions can lead to actual revenue results that are consistently outside the forecast tolerance range.

2 thoughts on “How to increase the accuracy of revenue forecasts.”

Hello,

Great article. I do have a question. So if we have a probability of winning an order of 4 drives in third quarter of this year – and that probability is 50%. You can not forecast 2 drives for sales in third quarter. It is either 4 drives or zero. What is the best method of forecasting this probability?

You must consider each case on its merits and exercise your best judgement to either include or exclude the 4-drive deal in your Q3 projection based on what you know about the circumstances, players, prospects, and context. For example, if you have only deal of its kind then the odds are long. If you have three other similar prospective deals then maybe you will get one or two out of the four. Either way it makes no sense to forecast a fraction of the deal and it takes experience, careful review, and thoughtful, honest reflection to make a good projection.

Hello,

Great article. I do have a question. So if we have a probability of winning an order of 4 drives in third quarter of this year – and that probability is 50%. You can not forecast 2 drives for sales in third quarter. It is either 4 drives or zero. What is the best method of forecasting this probability?

You must consider each case on its merits and exercise your best judgement to either include or exclude the 4-drive deal in your Q3 projection based on what you know about the circumstances, players, prospects, and context. For example, if you have only deal of its kind then the odds are long. If you have three other similar prospective deals then maybe you will get one or two out of the four. Either way it makes no sense to forecast a fraction of the deal and it takes experience, careful review, and thoughtful, honest reflection to make a good projection.