Imagine life without meteorologists or technology to help us predict the weather. We’d find it more difficult to plan our daily outdoor activities (not to mention our outfits). And we’d potentially end up in dangerous situations when extreme weather events showed up unexpectedly.
Similarly, sales teams struggle to create and execute forward-thinking strategies without an accurate sales forecast or sales dashboard. If teams are working blind, they can’t effectively onboard new customers, adjust workload capacity, set achievable goals, or spot critical issues in advance.
At Zendesk, 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 in this article, we’ll share the most essential sales forecasting techniques—plus examples.
What is sales forecasting?
Sales forecasting is the data-informed process of predicting how much your company expects to earn over a given time frame. This is based on several factors, including historical data, industry and economic trends, and your current sales pipeline.
Essentially, sales forecasting attempts to answer the most urgent questions any company can ask: How much revenue can we expect to generate? and When will that revenue come in?
Accurate sales forecasting relies on two critical elements: having the right data and drawing the correct conclusions from it. Neither is easy. If you overestimate sales, you could spend money that you won’t make. If you underestimate sales, you may be understocked for the coming quarter.
But just like meteorologists aren’t always spot on with their weather forecasts, sales forecasts aren’t 100 percent correct. Take these forecasts as predictions, not hard facts. Various factors—such as marketing, fluctuations in the economy, and hiring or firing employees—can all cause deviations.
Why sales forecasts are important
Sales forecasts matter when planning for the future. Internally, forecasts are necessary when determining:
- Production cycles
- Supply chain purchasing and production capacity
- Sales territories, quotas, and strategies
The benefits of sales forecasting
Accurate sales forecasting improves decision-making, helps identify problems in advance, and enables you to predict future revenue and growth. Some other benefits of precise forecasting include:
Higher growth rates
With an eye on exactly where you’re headed, it’s easier to hit and surpass goals. That’s probably why the Aberdeen Group found that companies with accurate sales forecasts were 10 percent more likely to grow their revenue year-over-year and 7 percent more likely to hit quota.
Better post-sales preparation
Post-sales activities can include refreshing inventory, prepping customer support, or developing an implementation timeline. The earlier you know your expected sales numbers, the more prepared your business can be.
Faster strategic planning
With sales forecasting, there’s no need to wait until the end of the quarter to adjust company tactics. If forecasting is predicting that you won’t meet your sales goals, for example, you can pivot and restrategize before those numbers take you by surprise. Forecasting also helps you determine when you should hire, where you should distribute resources, and when you need to get defensive with your risk management.
Who’s responsible for sales forecasting?
Big-picture thinking is the sales manager’s responsibility. Of course, managers rely on their reps for correct data. That’s why having an easy-to-use customer relationship management (CRM) platform is so critical to gathering objective, usable data for sales forecasting.
From that data, managers can start creating their forecasting picture based on historical win rates and pipeline velocity.
How to forecast sales
Common sales forecasting methods use qualitative and quantitative methods to help you predict total sales, revenue, and new business. Each one takes practice—as well as an objective mindset—to provide your company with accurate forecasts.
Sales forecasting methods
1. Opportunity stages forecasting
Opportunity stages forecasting allows you to calculate the chance of closing a future deal at each stage in the sales pipeline.
Most businesses break down their pipeline into a general set of stages:
- Won or lost
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 with common 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:
The overall forecast amount for these three deals is $1,400.
One disadvantage to this approach is that it doesn’t account for the unique characteristics of a given deal. This quantitative method is best combined with your sales reps’ opinions on certain deals, so you cover both subjective and objective elements. Your forecast will be more accurate as a result.
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 the pipeline stage or the sales rep’s gut feeling, this approach makes assessments based on the age of the deal.
For this method, simply 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 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 reaches 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 turn into a win.
3. Qualification frameworks
Forecasting by deal age is quick and easy, but it can sometimes be inaccurate due to emotion and bias. One strategy for eliminating the impact of emotion on sales forecasting is to use a qualification framework, which generates a score for each deal.
For example, in deals with a marketing source, Referral deals may be scored at 7.9, while deals that come from Adwords might be scored at 5.1 because Referral deals have historically closed at a higher rate than Adwords deals.
MEDDIC is another qualification framework that we like. Created by Dick Dunkel and Jack Napoli in the mid-1990s, MEDDIC outlines six core areas to consider for deal qualification. The team at Lucidchart has a helpful breakdown of MEDDIC, but at a high level, MEDDIC accounts for:
- Economic buyer
- Decision criteria
- Decision process
- Identify pain
If you’re using a sales methodology like MEDDIC for forecasting, you can assign a point value for every criterion. Take this logic and expand it across multiple data points. After scoring each deal, you’ll have a stronger indicator of which deals are likely to be won or lost.
4. Regression analysis
Regression analysis provides an in-depth, quantitative assessment of factors that might be affecting sales. Success with this method requires a good grasp of statistics and the elements impacting your company’s sales performance. It also involves calculating the relationships between variables that influence sales.
Regression analysis is the most advanced level of forecasting, so it may be more difficult to run and comprehend. But for advanced companies looking to fine-tune their forecasting strategies, this technique can offer valuable information to help with business growth.
The 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 so you can plan for budget allocations and decide if more sales reps should be hired. Sales (Y) is the dependent variable you’re trying to understand. Now, imagine you want to evaluate how sales calls (X) are affecting your sales; this is your independent variable.
- Dependent variable (Y): Sales
- Independent variable (X): Sales calls
You collect data for both your dependent and independent variables over eight years—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 because 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: correlation doesn’t necessarily imply causation. You have to consider various 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 the number of emails sent, number of demos given, number of meetings held, etc.
5. 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’s 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 challenges might arise over the next year: Will customers 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.
Sales forecasting model best practices
It’s crucial to use a data-guided process when constructing a sales forecasting model. Follow the steps below to build an objective foundation from which to work.
Establish a clear sales process
Ensure you have a standardized sales process in place. Sales stages and the steps for each stage should be repeatable and clearly defined so your reps know how to guide a customer through the pipeline. This allows you to have consistent and uniform data to refer to when forecasting sales.
Focus on accurate data
According to Forbes, a surprising 84 percent of CEOs have concerns about the quality of data they’re using to make decisions. Use a single, unified CRM platform to minimize lost data and increase accuracy. Data should also be entered in real-time so critical information doesn’t slip through the cracks.
Leverage historical data
Use historical data as a guideline to review what’s worked in the past so you can predict the future. While this data shouldn’t be the only factor in your forecasting decisions, it does provide a look at previous scenarios where sales increased or decreased. Replicate activities that have been effective in the past, and identify areas that still need improvement.
Assuming you have the above foundation in place, use the following scientific strategies to craft a sales forecasting model:
- Adjust your scoring strategy to ensure you understand which deals will close.
- Analyze stage duration to determine which sales stages are taking the longest and why.
- Measure lead and opportunity performance at every stage in the sales pipeline.
Sales forecasting techniques and tips
Include sales rep classification in your forecast
Rep classification is a subjective, qualitative forecasting approach that solicits your sales reps’ opinions to identify if a deal is going to close or not.
To get started, include a forecasting field in your CRM for the sales rep to complete. This forecasting field should contain multiple options to signal what they believe is going to happen to this opportunity in the given time frame (month/quarter).
Common rep classification categories include:
- Best case
- Forecast stages
While this approach requires that your sales reps give an honest assessment of their skills and potential clients, it can be an effective way to check rep performance and determine if your reps need to take additional steps to close deals.
Use a time-series analysis as a benchmark
Time-series analysis is used to identify trends over time. It is most accurate when years of a product’s historical sales data are available for reference. By looking at historical sales trends, forecasters can ballpark future performance rates and anticipate any fluctuations during the year.
A time series is a set of chronologically ordered points of raw data. An example is the set of data points recorded every month over six years of a product’s sales. This type of sales forecasting helps showcase:
- Cyclical patterns in sales every few years
- Growth rates or trends for different data sets
- Any seasonal variations in the data
One issue with this sales forecasting technique is that it assumes buyer demand is constant. If anything unexpected happens, your time-series-based model won’t hold up.
Generally, it’s a good idea to use historical demand as a benchmark rather than the foundation of your sales forecast.
Refine your scoring strategy
To help prioritize prospective buyers and gauge their likelihood of conversion, companies score leads based on criteria that signify purchase intent, such as content downloads and website visits.
That’s one way to do lead scoring, but an alternative method is for businesses to actively seek out and identify high-quality leads. This method:
- Enables your sales reps to find high-value leads based on profile similarities with previously closed deals
- Gives your reps confidence that the leads they qualify will eventually turn into paying customers
- Provides data-driven evidence indicating which deals you can expect to close and include in your forecast
For example, after analyzing your recent data, perhaps you discover that the CTO was the decision-maker in nearly 65 percent of won deals. In that case, score leads higher when CTOs are the main point of contact, and increase their anticipated win rate once they enter your pipeline.
You can follow the same methodology for lost or unqualified deals. If you lost 80 percent of deals where the CMO was the decision-maker, then score leads lower when the CMO is the point of contact. The same goes for similarities in industry, company size, location, etc.
Once each deal is scored, you have a stronger prediction of which deals will be won and which will be lost.
This smart lead scoring approach increases the likelihood that the leads you qualify will eventually turn into paying customers. It also gives you real, data-driven evidence for which deals you should expect to close and include in your forecast.
Incorporate stage duration into your sales forecasting model
As any sales leader who’s fallen short of their forecast knows, it’s not just about whether a deal closes—it’s also about when it closes. Timing matters with sales forecasting. While deals can sometimes stall because of unforeseen roadblocks, having a deep understanding of your stage duration can greatly improve your forecasting accuracy.
Stage duration is the amount of time each of your deals spends in a given stage of your sales pipeline (such as qualified, quote, or close). Conducting a stage duration analysis allows sales leaders to see more than just the average time deals spend in every stage. It also:
- Helps sales leaders identify the ideal length of time a deal should spend in a sales stage
- Highlights bottlenecks within each stage
- Calculates the likelihood of closing a deal based on the amount of time spent in a particular stage (compared to deals that have been won)
This information will help you answer the following questions to create an optimal sales cycle:
- How long are deals staying in the prospecting/incoming stage?
- How many days do they languish in the quote/proposal stage?
- Where are bottlenecks most common? (And what adjustments can you make to remove them?)
- How long does it take your sales reps to close deals?
- How can lower-performing reps adopt the winning habits of reps who move through the process more quickly?
Understanding stage duration enables you to identify the types of deals that take longer to close. It also pinpoints where deals are most likely to get stuck. This information is invaluable for building a forecasting model.
Measure across conversion points for accurate forecasting
More often than not, very few metrics other than revenue are factored into the sales forecast. And when other metrics are considered, their measurements are isolated. What good does it do your business to know how many marketing leads are accepted by sales if you can’t measure the impact this has on your bottom line?
Think of it this way: Sales revenue is the cumulative result of each conversion point along your sales pipeline. So to accurately predict sales revenue, your forecasting model must include data about leads accepted, opportunities qualified, etc.
To understand how leads and opportunities flow through your sales process and pipeline, use sales metrics called process measures. They break down conversion rates stage by stage. This allows you to pinpoint bottlenecks and inefficiencies, and it reveals actionable insights around how to increase revenue.
Ultimately, process measures give you a clearer, more precise picture of expected revenue over time. One example is sales cycle length. This metric looks at how long it takes for deals to go through your pipeline. It shows the effectiveness of varying sales processes, so you can make adjustments if needed.
Say it takes a total of 98 days to close 10 deals. When you plug those numbers into the formula above, you find that the average sales cycle is 9.8 days. If you can predict how long it’ll take incoming deals to close, you’ll have a better idea of what your sales numbers will be at any given time.
With the ability to measure performance across each of these conversion points and see exactly how it affects sales revenue, businesses can forecast more accurately.
Sales forecasting examples
Here are a couple of examples showing how sales leaders can use forecasting to set their teams up for success.
Making informed hiring decisions
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 falling 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. A sales forecast will give you valuable insights into revenue so you can make intelligent hiring decisions.
Identifying areas of improvement and setting goals
Forecasting makes it easy to see where your sales team may be struggling. Once you identify areas of improvement, you can provide additional training opportunities to help reps refine their sales techniques at those stages. Say there’s a predicted decrease in the opportunity stage of forecasting. 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.
Common sales forecasting mistakes
There are a few common pitfalls you need to watch out for when forecasting.
If you aren’t using the right tools to create your forecast, you won’t end up with reliable results. Instead of old-fashioned spreadsheets requiring manual entry, adopt a CRM across departments. Organized, unified data management lets you automate your analyses and ensures all teams are using the same methods and metrics.
The forecasting process must be standardized so there’s no confusion about responsibilities, timing, or inputs. The last thing you want is to revise your forecast because of faulty data, uncertainty, or tardiness. This not only reduces trust in the forecast, but it can also be dangerous if extreme inaccuracies pop up.
Sales reps’ gut feelings about an opportunity are valuable—but not infallible. Sellers are often too optimistic about which deals are likely to close. It’s worth investing in sales coaching so reps can learn how to increase objectivity. Your team should also be equipped with the right sales productivity tools so they’re always working from consistent data.
The future of sales forecasts
Looking to the future, sales forecasting will be increasingly data-driven, automated, and integrated. To stay competitive, the vast majority of top-performing companies are investing in smart CRM software with forecasting capabilities. Key areas of advancement include:
Predictive analytics are statistical methods that analyze current and historical data. They include predictive models and machine learning, which are increasingly utilized in sales forecasting. By analyzing historical pipeline data, your team can leverage machine learning to extrapolate from previous trends and fill in any gaps in current data. Not only can this provide an accurate forecast, but it can also deliver specific recommendations for your sales reps to act on.
Sales forecasting software
Sales forecasting software is becoming more accurate and simple to integrate. Investing in this type of software is a surefire way to create precise forecasts for your company. Here’s a short checklist of what modern forecasting software can now do:
- Analyze trends and changes over time
- Model simulations and outcomes
- Calculate using standard and advanced formulas
- Compare multiple techniques
- Create visualizations (maps, charts, graphs)
A CRM is an all-in-one tool with pipeline software, lead management capabilities, and even prospecting tools for lead generation. It can also be invaluable for sales forecasting. By tracking all your customer data and potential deals, a CRM can generate sales forecast reports to predict future sales. Monitoring your team’s past and current efforts also helps you fine-tune your sales strategy.
CRMs come in different shapes and sizes, so you can choose the right one for your business. A CRM for startups won’t offer as many user capabilities as one for an enterprise-level company, which may require a customizable CRM.
Whatever your business size and needs, choose a variety of sales forecast methods to achieve the best results. Use multiple forecasting techniques in your sales reporting software 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 correct 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.