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6 Sales Forecasting Methods that Will Help You Predict the Future

Sales forecasting isn’t about guessing or fortune telling. It’s about using market research and historical data to project future sales for a given period.

Done well, the ability to forecast sales can help your business make better financial decisions, manage inventory, and plan ahead for future growth.

The tricky part is putting together a sales forecast that is realistic and accurate enough to actually be useful.

Let’s take a look at the type of forecasting you can do and various factors that will impact your results.

Short-Term vs. Long-Term Sales Forecasting

A short-term sales forecast can be calculated monthly, quarterly, bi-annually, or annually. A short-term forecast is helpful for setting challenging-yet-attainable sales quotas, ensuring production is on schedule to meet demand, and making smart hiring decisions. For businesses that face rapid changes in market or demand, short-term forecasting is ideal for projecting upcoming sales and revenue.

Long-term sales forecasting, on the other hand, look at a business’ sales projections for periods of 5 or 10 years into the future, or even longer in some cases. However, this type of forecasting is typically only relevant in industries that require higher upfront costs and investments in equipment.

Both short-term and long-term forecasts are impacted by internal and external factors.

Short-Term vs. Long-Term Sales Forecasting

What Factors Impact Your Sales Forecast?

Your sales forecast is influenced by a number of different factors, some of which are within your control (internal) and some of which are not (external). Depending on the forecasting method you’re using, these factors may or may not be built into your calculation.

Sales Forecasting: Internal Factors

  • Changes to your sales territory. If you shuffle territory assignments, redefine boundaries, or introduce a new sales territory management plan (whether your territories are  geographical in nature or not), you can expect to see a temporary drop in sales. However, sales should bounce back to an even higher point once your reps adjust to their new, optimized territories.
  • Changes to your compensation plan. If you make changes to your sales compensation plan or commission structure, you’ll likely see it reflected in your sales numbers. For instance, if you switch to a structure that rewards reps for increasing revenue more rather than closing more deals, your forecast should anticipate fewer new accounts with a focus on landing higher-value customers.
  • Changes in the size of your team. When you hire more sales reps, you should expect an increase in sales (once they’re onboarded and trained), since you’ll have more people on your team working to close deals. Likewise, when you have to let someone go or when a rep quits or retires, your revenue might dip until you replace those team members.
  • Changes to your products or services. Whenever your business introduces new revenue streams, releases highly-anticipated features, or restructures how offerings bundled or priced, the changes will impact your forecast. If a new offering enables sales reps to speed up the sales cycle or increase their win-rate, for instance, your forecast should reflect that positive gains.

Sales Forecasting: External Factors

  • Changes made by competitors. The decisions and actions of your competitors can affect the outcome of your sales forecasts. For example, if a major competitor in your space suddenly discounts their prices, that will impact your ability to sell at your current prices. Or if new competitors emerge, you might struggle to maintain your market share unless you can adapt.
  • Market changes in supply and demand. Paying attention to what’s happening within your industry plays a huge role in your ability to forecast sales accurately. If there’s a growing need for your product or service, that’s a sign you can be more optimistic in your sales forecast and projected growth (especially if there’s a gap in market supply).
  • Seasonality. Depending on what you sell, your sales might naturally rise and fall during certain times of the year. This is different than market changes, as seasonal highs and lows occur on a cyclical basis. Since seasonality is reasonably easy to predict, you should factor it into any sales forecasts based off MRR or historical data to avoid skewing your results.
  • The rate of inflation. For long-term forecasting, in particular, you need to account for potential inflation and how it will affect your costs and pricing strategies.

Sales Forecasting Methodology: 6 Popular Models for Forecasting Future Sales

When it comes to forecasting future sales and revenue, there are several approaches you can choose from. Let’s look at six of the most commonly-used sales forecasting methods.

1. Forecasting by Unit Sales (Or Service Units)

This is one of the most simple, straightforward methods for forecasting sales. Start by figuring out the average price per unit (or service unit, billable hours) and the number of units you’re likely to sell over a given period.

Your sales forecast, in this case, can be calculated by multiplying the number of expected sales by the average price. If you’re planning to increase prices next year, for instance, you can use this method to estimate how that decision will impact revenue.

The tricky part, of course, is accurately predicting how many units your team can move in a given week, month, or year. That’s where historical data can come be brought in to paint a more realistic picture.

2. Historical Forecasting

Unless you’re first starting out, your business should have at least some existing sales data that you can refer to as a starting point. You should look at all sources of revenue gained and lost during a particular period, including new customers, monthly charges, and customer churn.

Historical forecasting is a fast and easy way to get a ballpark estimate for future sales. However, the main concerns with this forecasting model are that it assumes demand and growth are both constant, without accounting for seasonality or long-term fluctuations in market demand.

To improve accuracy, you should look at the same time period from a past year and compare it to the present year. For instance, rather than forecasting your 2019 sales based on the average monthly sales for all of 2018, you could break it down by month or quarter. So, you could use sales from April 2018 to forecast for April 2019 – but those numbers might look completely different from your sales in January or October.

Furthermore, you can fine-tune even further by factoring in your historical month-over-month growth rate.

3. Intuitive Forecasting

If you don’t have any historical data to reference, then sales forecasting becomes more of an art than a science. Intuitive sales forecasting is a method that asks reps to estimate how likely they are to close a given deal, when they expect to close, and the anticipated value of that deal.

Since this method relies on making an educated guess, its accuracy hinges on a rep’s experience level, knowledge of their prospects, and their ability to make an honest assessment of each situation (rather than an overly optimistic projection).

For instance, a rep might predict that three of their open opportunities will close by the end of the month. In this case, they would add up the approximate value of each of these deals to come up with a monthly forecast.

Though this can be effective if sales reps are realistic about their abilities, the state of their pipeline, and their average win-rate, intuitive forecasting is not considered the most reliable method. That said, sometimes you have no choice but to trust your gut – especially if your business is brand new and you don’t have any sales data to use as a benchmark.

4. Opportunity Forecast

This model uses a weighted sales pipeline to forecast your upcoming wins and revenue. Calculations are based on what stage each deal is at (and how likely it is to close based on that stage) and the potential value of each opportunity.

For example, your weighted pipeline might breakdown sales stages like this:

  • Step 1: Prospecting – 10% (likelihood of closing)
  • Step 2: Qualification – 25%
  • Step 3: Proposal – 50%
  • Step 4: Demo – 65%
  • Step 5: Negotiation – 80%
  • Step 6: Close the Deal – 100%
  • Lost or Dead Lead – 0%

So, let’s say you have three prospects at different stages in your pipeline, each with a potential value of $10,000.

Prospect A is someone you’ve emailed back and forth with a few times. This places them in the prospecting or outreach stage, with an estimated 10% likelihood of closing.

Prospect B has been qualified as a viable customer, but you haven’t set up a demo or shipped over a proposal yet. So, this opportunity is currently at the qualification stage, giving it a 25% likelihood of closing.

Prospect C is negotiating terms and finalizing the contract, but they’re pretty much ready to seal the deal. As it’s reached the negotiation stage, this deal has an 80% chance of closing.

Using opportunity forecast methodology, you would multiply the likelihood of closing by the potential deal value. In this example, your calculation would look like this:

Prospect A → $10,000 * .10 = $1000

Prospect B → $10,000 * .25 = $2500

Prospect C → $10,000 * .80 = $8000

So, your total sales forecast for the period would be $11,500. As deals move further along or drop out of your pipeline altogether, simply adjust the calculation to reflect those changes.

5. Forecasting by Sales Cycle Length

Forecasting based on age of the deal is similar to a weighted pipeline, but it focuses on sales cycle length rather than deal stage.

Hypothetically, the longer a rep has been working to close a specific deal, the more likely it is to go through. So, if you know the average length of your sales cycle, you should be able to predict how likely a particular deal is to close based on how long it’s been in your pipeline.

For example, let’s say your average sales cycle is 60 days. If a sales rep has been in contact with a prospect for 30 days, you could say they have a 50% likelihood of closing. In your revenue forecast, you would divide the total potential value of the deal by .50 to reflect the odds of it closing.

Of course, this method is far from perfect, especially since deals can stall or get stuck if your pipeline has roadblocks or leaks. So, if you’re using this approach, consider switching to a weighted pipeline for more accurate results.

6. Multivariate Analysis

This is both the most complex and accurate model for sales forecasting. It uses predictive analytics to consider several factors that influence sales revenue, including probability of closing, sales cycle length, and the rep’s performance history.

For example, you could look at each rep’s average win rate, deal age, and potential deal size to forecast how much revenue you can lock in by the end of the quarter. The main drawback to this method is that it requires detailed analysis and careful data selection.

Bonus Tips for Better Sales Forecasting

  • Start with Historical Data: Always refer to your historical sales data (if available) to set a benchmark, even if you’re using a different forecasting method.
  • Get Team-Wide Buy-In: Communicate with your entire sales team about the importance of collecting data and keeping it clean. If everyone makes an effort to ensure the accuracy and completeness of their data, your sales forecasts will be right on the money.
  • Look at Multiple Factors: Include enough input to account for 2-3 relevant variables. Whether you’re looking at rep performance, pipeline stage, deal age, or some other metric, be consistent in what and how you track your sales data.
  • Build an Effective Sales Stack: Ensure your sales team has the right technology to help them capture, manage, and analyze sales data. For example, a CRM platform that provides you with a visual pipeline and deal stage tracking can simplify sales forecasting immensely.
  • Use a Powerful CRM: Simplify data tracking, collection, and analysis with a robust CRM. A platform like Propeller will make it easy to manage your customer information, generate sales reports, and plug your data into the forecast model of your choice.

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