Introduction to sales forecasting in 2019

Accurate sales forecasting is comprised of two critical elements: Having the right data and drawing the correct conclusions from it. Neither is easy, and the stakes are high. If you overestimate sales, you could spend money that you won’t make, and if you underestimate sales, you’re left ill-prepared for the coming quarter.

An accurate sales forecast allows you as a sales manager to prepare post-sales support (implementation, materials, support, infrastructure). At Sell, 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, which we’ve compiled in this article.

Note: For a quick overview of the contents of this article, check out the video below. We cover the definition of sales forecasting, why it’s useful, and forecasting strategies from beginner to advanced.

Keep reading to learn how your business can get started with forecasting sales.

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.
  • Faster course correct. 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.

What are different sales forecasting strategies?

In order to fairly evaluate all opportunities in your sales pipeline, you’ll need some type of basic criteria for forecasting. Think of these criteria 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.

Once your criteria are in place, you can choose a sales forecasting strategy to use. We list strategies below based on their difficulty—from beginner pipeline stages to advanced regression analysis. Each one takes practice, as well as an objective mindset, to provide your company with accurate forecasts.

1. Pipeline stages

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 of stages and win likelihood:

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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 simply multiply the deal value by the likelihood to be won.

For example, a $10,000 deal that’s in the Qualified pipeline stage, which carries a 10% win rate, would produce a Forecast of $1k ($10,000 * .10 win likelihood).

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

  • Advantage: Estimate incoming revenue and better understand future opportunities based on past information.
  • Disadvantage: It does not take into account individual characteristics of a given deal.

This quantitative approach is best combined with your sales reps’ opinion on certain deals, so you cover both subjective and objective elements. Your forecast will be more accurate as a result.

2. Rep classification

Sales forecasting by rep classification uses your sales reps’ input to identify if a deal is going to close or not.

To get started with rep classification, include a forecasting field in your CRM. The expectation would be that any deal in your sales process would have this field to be completed by the sales rep. The forecasting field would contain multiple options to signal what is going to happen to this opportunity in the given time frame (month/quarter).

Common rep classification categories include:

  • Best Case
  • Commit
  • Pipeline
  • Closed/Won

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This is a qualitative approach to sales forecasting that only looks at the sales rep’s opinion of deals in the pipeline.

  • Advantage: Forecasting is done by salespeople who are closest to the opportunity/deal.
  • Disadvantage: Sales reps are often pessimistic or optimistic. The data can be subjective to the sales rep’s personal viewpoint.

While this approach does require that your sales reps give an honest assessment of their skills and potential clients, it can be an effective way to determine if additional steps need to be taken by your reps to close deals, as well as check rep performance.

3. Qualification 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.

A sales methodology that we’re fans of here at Sell is MEDDIC. Created by Dick Dunkel and Jack Napoli in the mid-1990s 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 criterion. Below, we’ve created a simple scorecard and two sample deals.

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

  • Advantage: You have a scientific way to complete qualification.
  • Disadvantage: It requires more input and adoption from your sales team.

Choosing this criteria method does mean more work up front from you and your team in order for it to be effective. However, it also is more objective than forecasting by rep classification.

4. Scoring win likelihood

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 information to predicting the likelihood of a won deal.

For example, in deals with a marketing source, the 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.