Sales Forecasting is comprised of two critical elements:

  • Having the right data
  • Drawing the correct conclusions from the data

An accurate sales forecast allows you to prepare post-sales support (implementation, materials, support, infrastructure). If you overestimate sales, you start to spend money that won’t be coming in and underestimating sales leaves you ill-prepared.

While many will argue that sales forecasting is both an art and science, this ebook will outline concrete ways that any business can get started with sales forecasting.

At Base we’ve worked with thousands of businesses to develop sales processes, build forecasts and increase sales rep adoption. We’ve learned quite a bit along the way and today we’re sharing the introduction to sales forecasting.

What is sales forecasting

It’s no secret that sales forecasting, or the process of predicting future sales revenue over a given period of time, can be a major struggle for sales leaders. In fact, just 31% of businesses consider their forecasts to be effective in terms of accuracy and helping guide pipeline management.

Inaccurate sales forecasts can have serious business-wide repercussions, from product shortages to over hiring. On the flip side, the Sales Management Association found a correlation between businesses’ forecasting effectiveness and the achievement of their annual revenue objectives.

Specific areas that can be impacted include:

Preparing Post Sales Success

Whether you’re selling software or solar panels, there are post sales activities that need to take place. This can involve purchasing more materials, preparing customer support or developing an implementation timeline. Whatever the case may be, the more accurate you are and the earlier you know your expected sales number, the more prepared your business can be.

Course Correct Faster

Having an accurate view of which deals will close can sometimes paint a bleak picture of missed goals. The earlier you can identify that you will not hit your goal, the faster you can work with marketing to acquire more leads (pipeline) and course correct.

Sales Forecasting 101


In order to fairly evaluate all opportunities in your sales pipeline, you’ll need some type of basic criteria for forecasting. Think of this as a ‘scorecard’ that is used to evaluate opportunities. At the basic level, this ‘scorecard’ will consist of a single variable. Based on the ‘score’ you’d then predict that a deal will/will not close in the given time period.

Beginner: Sales Forecasting By Pipeline Stage

If you’re not already using a defined sales process, start immediately. Once you have a sales process (often called a sales pipeline), begin to assign a percentage to each stage of the sales process. This percentage represents the likelihood that the deal will be won if it reaches this stage of your sales process. The percentages should increase as the sales pipeline stages approach won/lost.

Here’s an example:

sales forecasting pipeline

Each stage of the pipeline has a defined win likelihood. Most CRMs will support this functionality, but you can also do this in Excel or Google Sheets. To produce a basic forecast of expected revenue, you’d simply multiply the deal value by the likelihood to be won. For example, a $10,000 deal that’s in a pipeline stage Qualified which carries a 10% win rate would produce a Forecast of $1k ($10,000 * .10 win likelihood).

Keep in mind this is forecasting at the most basic level. In most cases, success will be binary, you wouldn’t close the deal for 1k, it will be sold for either $10,000 or lost, however, this is a quick back of the napkin way to estimate incoming revenue.

Beginner: Sales Forecasting By Rep Classification

Another easy way to add forecasting to your sales process is to incorporate a forecasting field into your CRM. The expectation would be that any deal in your sales process has this field completed by the sales rep. This field would contain multiple options which signal what is going to happen to this opportunity in the given time frame (month/quarter). Common values for a Forecasting Dropdown Field include:

  • Best Case: Deals which you think are likely to close but are not certain. In order for this to close everything needs to go right.
    Commit: Deals which you believe will close in the given forecasting period.
    Pipeline: All deals that are in in your pipeline that are not classified as Won/Lost/Unqualified
    Closed/Won: Deals that have been moved to ‘Won’ and are contributing to the generated forecast.


Forecasting is done by salespeople who are closest to the opportunity/deal


Sales reps are often pessimistic or optimistic
Sales reps want to focus time on customer development not sales forecasting

Intermediate: Sales Forecasting With Frameworks

Forecasting by deal stage or rep classification is a quick and easy solution, but it can often be inaccurate due to emotion and opinion. One strategy for eliminating the impact of emotion on the sales forecast is to use a sales framework/methodology which generates a score for each deal. This can then be used for forecasting.

One sales methodology which we’re fans of here at Base is MEDDIC. Created by Dick Dunkel and Jack Napoli in the mid 90s while they were at the legendary sales organization PTC, MEDDIC outlines six core areas to consider for deal qualification. The team at Lucid Chart has a nice breakdown on MEDDIC here. At a high level, MEDDIC accounts for the following:

    • • Metrics
    • • Economic Buyer
    • • Decision Criteria
    • • Decision Process
    • • Identify Pain
    • • Champion

If you’re using a sales methodology like MEDDIC for forecasting, you can assign a point value for simply identifying each criteria. Below I’ve created a simple scorecard and two sample deals.

meddic forecasting

As an example, deal #2 is missing a Decision Criteria, Pain and Champion. As a result, the deal would have a score of 15pts. In terms of forecasting, you may say that you’re only forecasting revenue for deals which have a score of 20pts or above (for example). This ensures that there’s a standardized scorecard for each deal that removes emotion from the process and quantitatively helps you evaluate deals.

Intermediate: Sales Forecasting By Score

Expanding on forecasting with frameworks is the concept of building out a scoring formula which can be used to evaluate the likelihood of a deal closing. A scoring formula would be created based on past sales data and then apply the learnings to predicting the likelihood of a won deal. For example, deals with a marketing source, Referral might be scored at 7.9 while deals that come from Adwords would be scored at 5.1 because referral deals have historically closed at a higher rate than Adwords deals. Take this logic and expand it across multiple data points and you have a very advanced way to detect which deals are likely to close. Once each deal is scored you have a strong indicator of which deals are going to be won and which will be lost for your forecast.

Here’s an example of how a single variable (Marketing Source) would look once it’s scored.

scoring forecast


Removes emotion and bias from the forecasting process


Low CRM adoption (fields not filled out) will result in limited pipeline visibility for scoring

Advanced: Sales Forecasting With Regression Analysis

At the most advanced level of forecasting, it’s possible to create a statistical model which accounts for covariance and correlation between variables. In order for this to be effective, you need to have a large quantity of clean and organized data. Regression analysis for sales forecasting is an incredible goal to strive for, but it’s typically only a good idea for advanced companies which are looking to fine tune their forecasting strategies.


Mathematically determine which variables have an impact on won revenue


Large quantity of clean and accurate data required for meaningful results


Sales Forecasting is a skill which takes several quarters to get the hang of, but once you truly understand your sales pipeline, you’re enabled to make faster business decisions. As you build out your forecasting strategy, be sure to be conscious about the data you’re collecting and then trust the data to build a forecast for your business. Eliminate emotion and bias whenever possible by using scores, and update your forecast on a routine basis.

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