The Sales Forecasting Methods You Need to Know

*A lot has changed in sales forecasting since this post was first published in 2012. Check out our updated 2015 post: “The Best Sales Forecasting Methods for You.”

Here at InsightSquared, we’ve talked to a lot of sales managers about their analytics and reporting. Every single one of those conversations inevitably finds its way to the topic of forecasting. It is the one report virtually every sales leader thinks about every single day. Regardless of sales model or approach, even the most nascent sales organization spends time building and maintaining forecasts.

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Before we go too much further on various sales forecasting methods, we should set a common understanding of what a forecast is. A sales forecast is a projection of what your performance as a sales organization will be at the end of a measurement period (most often either monthly or quarterly). It is the expected value of your bookings at the end of the measurement period, i.e. how much business you think you will close.

A sales forecast is a projection of what your performance as a sales organization will be at the end of a measurement period.

If you’re just getting started with forecasting, here’s a run down on basic sales forecasting techniques and their strengths and weaknesses:

Opportunity Stages Forecasting

In CRM system such as Salesforce.com, your opportunities each have a status or Opportunity Stage. Each stage represents a milestone as you work towards taking a validated sales opportunity across the finish line and closing the deal. As an example, in the software world, these stages might be steps like “Completing an assessment call”, “Providing a demo”, “Enrolling in a trial”, “Submitting Quote” and “Closed – Won”.

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Each of these stages will have a “probability of closing” associated to them. So an opportunity in an early stage might have a win rate of 23%, whereas a later stage might have a 86% probability of closing.

Using this method, sales leaders create a basic forecast by multiplying the amount associated to each opportunity by each opportunity’s probability of closing, for those opportunities expected to close this period (i.e. those with a close date set in the CRM to this period).

Here’s a quick example:

This Month's Forecast

Opportunity Name Stage Probability    Amount    Forecasted Amount   
Opportunity A Demo 25% $100 $25
Opportunity B Trial 50% $200 $100
Opportunity C Negotiations 90% $300 $270
Total $395

There are some advantages and disadvantages to this (and any) approach. Lets run them down:

Pros:

  • -The math here is easily understood.
  • -The method is highly objective. The emotional element is largely taken out of the equation. If it is in an early stage, the probability of closing is low, no matter how good the sales rep feels about the opportunity.

Cons:

  • -The probabilities associated to different stages are rarely tested nor rigorously derived.
  • -Further analysis is needed. A conventional pipeline forecast will not account for opportunities that will close in that period but have not yet entered the pipeline. Furthermore, the shorter your sales cycle, the bigger this issue becomes.
  • -This approach is reliant upon accurate close dates in your CRM. Sales leaders are often hounding their reps to update their close dates with this method.
  • -Sales cycle length is not taken into account. An “ancient” opportunity that is 2 years old but set to have a close date of this month is counted the same as the “typical” opportunity that is only weeks old and also due to close soon.

Forecast Categories

To combat some of the flaws of the previous approach, other sales organizations use Forecast Categories to generate their forecast. Independent of the milestone hit by the opportunities, sales reps and managers are asked to make an assessment of their opportunity. Common forecast assessments might be “Commit” (i.e. I am committing to bring this in) and “Best Case” (i.e. if a few things go our way, this could come in).

Sales managers use these assessments to build out different scenarios for the consumers of their forecasts (CEOs, CFOs). The “worst case” scenario would be just closing the Committed opportunities. The “Best Case” scenario assumes more deals closing, which is more optimistic.

Advanced Predictive Forecast

More advanced methods can of course be used. One such advanced method would use the length of the sales cycle, employee’s personal win rates, and the probability an opportunity will close in the period. This method evaluates the age of each of your opportunities and compares it to the typical length of your sales cycle, then multiplies that by the owner’s win rate at that stage. 

This allows you to judge algorithmically the likelihood the opportunity will close in the period you’re forecasting as opposed to a later one. The math in an approach such as these can get complex, quickly. Which is why more advanced reporting and analytics solutions are useful. You, as a sales manager, can focus on your strengths and leave the fancy math to the MIT PhDs behind the scenes.

Let’s get creative

Sales leaders are a creative bunch. More than one has said to me that forecasting is “half-art, half-science”.

The methods I outline here are relatively basic. If you’ve built a forecast, you’ve probably modified, tweaked and improved your own methods.  Certain types of customers might have a track record of closing more frequently than others. Or certain geographies might haggle for lower prices with greater regularity.

Do you have favorite methods of sales forecasting? How has it worked for you?

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