Sales forecasts: A beginner’s guide (+ key sales forecasting methods)
Understand the basics of sales forecasting and master key techniques to improve your business decisions.
Published December 18, 2018
Last updated December 2, 2021
Imagine life if we had no meteorologists or technology to help us predict the weather. We’d find it more difficult to plan our daily outdoor activities and our outfits. We may even end up in dangerous situations when severe weather arises unexpectedly.
Similarly, sales teams struggle without an accurate sales forecast or sales dashboard. They can’t effectively plan to onboard new customers, adjust workload capacity, set achievable goals, or spot critical issues in advance.
Predict future revenue and get ahead of potential blockers by using a mix of sales forecasting methods that prioritize accuracy.
What is a sales forecast?
A sales forecast is a data-backed prediction of how much your company expects to earn during a specified period of time. This is based on numerous factors—historical data, industry and economic trends, and your current sales pipeline.
Overall, sales forecasting focuses on answering two main questions:
- How much revenue?
- When will that revenue come in?
Just like meteorologists aren’t always spot on with their weather forecasts, sales forecasts aren’t 100 percent certain. So, take these forecasts as predictions—not hard facts. Factors such as marketing, fluctuations in the economy, and hiring or firing employees can all cause deviations.
Why sales forecasting is important
Sales forecasts have a far-reaching impact, even beyond the sales team. Companies with accurate sales projections can make smarter business decisions regarding their sales process, business operations, marketing, and budget allocations. They can also establish more realistic goals for their teams.
Make informed decisions
You need a grasp on revenue to make important business decisions—such as hiring, investing, and budgeting for ongoing business expenses. Trying to make these decisions without sales forecasting is like planning an outdoor activity without knowing the weather. Do you bring sunglasses and sunscreen or an umbrella and a jacket?
In the same way, forecasting provides valuable insights that help you make intelligent decisions. For example, if you’re forecasting significantly higher sales in the next year, you may need to hire more people across the company or in a specific department to avoid getting behind.
Accuracy is key here. If you overestimate sales, you’ll end up spending money that won’t be coming in. If you underestimate sales, you may be scrambling when you get an influx of orders and don’t have sufficient staff and materials.
Set goals and track performance
Throughout the forecasting process, you’ll examine sales from every different angle and in various stages. By doing so, it’ll be easy to see where your sales team may be struggling.
Once you’ve identified areas of improvement, you can provide additional training opportunities to help agents refine their sales techniques at those stages. For example, say there’s a predicted decrease at the “Opportunities” stage; that may indicate it’s a good time to offer prospecting training.
By analyzing your past sales revenue and data, you’ll also be able to set realistic goals and benchmarks for your team.
4 essential sales forecasting methods (with examples)
Common sales forecasting methods use qualitative and quantitative methods to help you predict total sales, revenue, and new business.
1. Opportunity stages forecasting
Most businesses can break their pipeline down into a general set of stages:
- Won or lost
Opportunity stages forecasting allows you to calculate the chance of closing a future deal at each stage in the sales pipeline. For example, if you typically end up winning about half of your deals that reach the “Proposal” stage, then you know you’ve got a 50/50 shot for all the deals in that stage during a given quarter.
To use this sales forecasting technique, multiply a deal’s potential by the win likelihood. These numbers can be determined through most customer relationship management (CRM) tools. Next, repeat this process for each deal in your pipeline and add them together.
Let’s say you have a $1,500 deal opportunity at the incoming stage, a $2,000 deal at the qualified stage, and a $1,000 deal at the negotiation stage. Based on the chart above, forecasting would look something like this:
Deal 1: 10% x $1,500 = $150
Deal 2: 25% x $2,000 = $500
Deal 3: 75% x $1,000 = $750
The overall forecast amount for these three deals is $1,400.
2. Length of sales cycle
Forecasting based on the length of your recent sales cycle helps you predict exactly when a deal is likely to close. Rather than analyzing success rates based on stage or your sales rep’s gut feeling, this approach makes assessments based on the age of the deal.
For this method, tally up the total number of days it took to close all recent deals. Then, divide that by the number of deals you closed.
Imagine you recently closed five deals. Calculate the period of time it took to close each one, then add up the numbers:
- Deal 1: 62 days
- Deal 2: 60 days
- Deal 3: 59 days
- Deal 4: 55 days
- Deal 5: 60 days
- Total: 296 days
Divide that total by the number of deals (five), and you get your average sales cycle: 59.2 days, or roughly two months.
Now that you know your average sales cycle, you can apply it to the individual opportunities currently in your pipeline. Perhaps a salesperson has reached the “Proposal” stage with a lead after one month—even if this seems like a sure thing, the forecast suggests otherwise. Based on your average sales cycle length of two months, you might predict that the rep has a 50 percent chance of closing the deal. It may take longer than a month for that proposal to actually turn into a win.
3. Regression analysis
Regression analysis provides an in-depth, quantitative assessment of factors that might be affecting your sales.
Success with this method requires a good grasp of statistics and the factors impacting your company’s sales performance. It also involves calculating the relationships between variables that influence sales.
The simple regression model equation is Y = a + bX. But let’s break that down. Here’s how you’d go about completing a regression analysis:
- Determine the reasons for forecasting (what you want to learn and why).
- Determine the factor that is being affected, such as sales (Y, your dependent variable).
- Determine factors that might be affecting your sales (X, your independent variables).
- Determine the time period you want to review.
- Collect the data for both dependent and independent variables.
- Choose a regression model and run it.
- Look for correlation between variables.
Say you want to forecast sales for the next year to plan for budget allocations and determine if more sales reps should be hired. Sales will be your constant, dependent variable (Y)—the factor you’re trying to understand. Now, imagine you want to evaluate how sales calls are affecting your sales. This is your independent variable.
- Dependent variable (Y): Sales (SALES)
- Independent variable (X): Sales calls (SALES CALLS)
You collect data for both your dependent and independent variables over an eight-year period—your annual sales from 2012 to 2020 and the number of sales calls during that time.
Your equation could be SALES = a + b (SALES CALLS), with a representing the intercept and b representing the slope, respectively. Next, use regression software to run the analysis—Excel has this capability. Note that you will not have to compute a or b yourself; the regression software will generate that, too.
You’re looking for the “line of best fit” to approximate the relationship between the variables. For example, your plot might look something like this:
The slope (b) is 0.907, and the intercept (a) is -313.
Based on this model, sales calls look closely correlated to sales and may be leading to more revenue. But remember: just because a variable is correlated doesn’t mean it is the cause. You have to consider a variety of factors too in-depth for this exercise. This is also a simple linear example. You will normally have a multiple linear regression with several independent variables, such as number of emails sent, number of demos given, number of meetings held, etc.
4. Scenario writing
Scenario writing is a qualitative approach used for long-term planning and to account for possible extremes. It is dependent on a subjective understanding of business and sales.
In this approach, you project the likely outcomes based on a specific set of assumptions. You draft several different scenes that could unfold based on the assumptions, say best- and worst-case scenarios for the deals in progress.
Here is an eight-step process for strategically thinking about the planning process for scenario writing:
Let’s say your focal issue is yearly sales. You then move on to key internal factors influencing your sales, such as sales calls, inquiries received, or demo meetings held. External forces that might have an impact are competitors or government restrictions. For critical uncertainties, consider what difficulties might arise over the next year: Will the customer start leaning toward new technology? Will possible government policies affect the nature of your business?
Based on this information, you can begin to develop scenarios. For scenario writing to be effective, plan your potential outcomes around uncertainties with your business, and then create a clear action plan for each one.
Use a variety of sales forecast methods for best results
Remember that you aren’t limited to just one technique. People use their smartphone weather apps, watch weather reports on TV, and rely on almanacs and other resources to help determine what to expect from Mother Nature. In a similarly simple way, you can use multiple forecasting techniques and sales reporting tools to get an accurate picture of incoming sales and revenue and evaluate your current sales approach.
Decide which methods will be most effective for your company, and begin applying them. Don't get caught up in “analysis paralysis,” either. Although accurate data is important, the aim is for valuable—not perfect—information.
While using any forecasting technique appropriately takes practice, it will assist you in optimizing your sales forecast process and looking to the future.