Sales Forecasting is not the same as Revenue Forecasting

By Editor

So let’s start by defining what I mean by a “Sales” forecast. I came late to the Sales CRM world having spent many years running groups designing and developing financial forecast, planning and budgeting systems. In conversations with CRM customers, large and small, it is clear that there is a difference between a sales forecast and a revenue forecast, though these differences are often not understood.

Sales forecast – a contract with the sales organization.

This difference was best summed up by a sales leader customer of mine at a well known B2B software company who told me (and I am paraphrasing); “Yes, I know you can probably automate the Sales Forecast process, but if you do that, and the sales manager misses, they will turn around and say that it was the machine’s number, not mine. I want that contract with the sales organization, so we can hold their feet to the fire.”

This was a very interesting observation and helped me better understand why there is skepticism and general distrust of sales forecasts within organizations. In conversations with finance or supply chain leaders the general consensus is that sales forecasts are unreliable. Finance and supply chain are looking for a forecast that is an expected outcome so they can plan ahead:

  • for financial impact to cash positions and income statement projection
  • what goods and services need to be positioned when and where in the case of supply chain.

The sales organization is generally more interested in building a forecast that explains how to get to quota attainment.

Sales forecast process started by rep’s judgements.

So the difficulty here is that both groups are motivated by different uses of the sales forecast.

Let’s look at the typical process that many sales organizations use to create a forecast. For B2B enterprises that use a field sales organization or a combination of both inside and field sales, the forecast starts with the pipeline with opportunities or deals expected to close in the forecast period. The majority of companies I have encountered will forecast for the current quarter, whether for the months in the quarter or as a quarter as a whole. Irrespective of whether monthly or quarterly, the typical forecast cadence is weekly.  Reps will make a judgement call as to whether they are confident that the deal will close within that quarter, “committing” that deal to their manager. There may be some parallel categories such as “best case” for a more optimistic view or “worst case” for a more pessimistic. But the “commit” category will be the primary set of deals that the sales leadership team will consider as their forecasted deals.

Sales management view.

The front-line sales manager will discuss these sets of committed deals with the reps, trying to better understand whether they can be justified. This review will then go up the reporting chain – moving deals in and out of the forecast categories, and perhaps adjusting the overall amount. These adjustments can have several motivations and are one of the biggest areas of controversy.

  • Plug numbers to bridge a shortfall between aggregated committed deals and quota;
  • Adjustments based on a revision of the amount in the committed deals;
  • Estimations for “blue birds” i.e. deals that are not currently in pipeline but will open and close within the quarter;
  • Estimation for “cats and dogs”, smaller deals that are not managed at the deal level.

he controversial aspect to this is the size of the judgement amount or “plug” relative to the total. At the beginning of the quarter, this judgement amount can be large relative to forecasted deals. This has the potential for a large part of the overall forecasted number to be unsupported by known deals. Reps do not commit deals early in the sales cycle and managers will not want to show that they have little chance of achieving quota, so create a judgement amount that will bridge the quota gap. This explains why there is the suspicion, especially early in the quarterly cycle, of the sales forecast being more judgement-based than evidence-based.

Other factors that will cause a divergence between a sales and revenue forecast are:

  • The final amount that gets booked has little relationship to the amount included in an early stage opportunity.

For example, I analyzed 12 US based customers, these amounts can vary by as much as 70%, plus or minus 90 days from close.

  • Team selling and overlay organizations.

Team selling where the opportunity amount is split by percentage across several or more individuals and may add to greater than 100%.

Overlay organizations where product specialists also own quota for their products or services within a larger opportunity.

For example, for global sales accounts, there could be several sales persons working on the account say in North America and EMEA; the opportunity amount is shared between these reps with potentially the share or split being greater than 100%. For a sales forecast driven by quota attainment, the shared amount is applied up until there is a common management level, at which point the forecast amount needs to be adjusted down to reflect only 100% of the amount. In this instance the sales forecast serves a different purpose than would a revenue forecast.

AI assisted revenue forecasting.

Increasingly I am seeing companies wanting to have both a sales forecast and a revenue forecast. To get around the problem of reps and managers not committing early stage deals, machine learning and predictive analytics (collectively known as Artificial Intelligence or AI) are increasingly being promoted by vendors as part of their solutions. Going back to the different components of the forecast process, we can use that same list of component parts of the sales forecast, but this time show where AI can be used to replace judgement-driven adjustments with adjustments based on data and the historical record.

Using AI to “score” deals has been around for more than three years, so there has been time to assess impact on customers and to judge what are the more appropriate use cases. There are two major components to predictive scoring of deals.

  • Is this a good opportunity – meaning is this is a good fit based on product or solution, experience in this type of account or size of deal?
  • Will this close within the desired time range (more of a measure of deal management)?

So the revenue forecast now looks like the following:

  • Committed deals
  • Judgement changes to forecast categories such as “commit”, “best case” or “worst case”
  • Computation of amount adjustment based on historical variation depending on proximity to the projected close date
  • Computation for “blue birds” i.e. deals that are not currently in pipeline but will open and close within the quarter
  • Yield calculation used to compute “cats and dogs”, smaller deals that are not managed at the deal level.

These numbers should also be broken out by product, support and services if the company in question has non-recurring revenue such as implementation services included in the opportunity amount.


While there is certainly overlap between a sales and a revenue forecast, because of the different motivations behind the use and purpose of these forecasts, companies should employ both approaches – one for the sales organization and one for the rest of the company.

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