5 essential sales forecasting techniques
Better forecasts lead to better business decisions. Here are a few approaches to forecasting sales.
Published December 18, 2018
Last updated September 16, 2020
Sales forecasting is a key component of any business. It helps companies make better business decisions and affects many areas such as the sales process, operations, marketing planning, and budget allocations.
Unfortunately, many sales leaders struggle with implementing effective sales forecasting techniques. 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. If you overestimate sales, you start to spend money that won’t be coming in. Underestimating sales leaves you ill prepared for an influx of orders.
It's crucial to get your sales forecasting methods right early on. Correct sales forecasting has numerous benefits including:
- Spotting problematic issues in advance
- Evaluating sales opportunities
- Tracking sales rep progress
- Preparing post-sales support such as implementation, materials, support, and infrastructure
With the value that forecasting adds to a business, why do so many sales leaders struggle with correct sales forecasting techniques? The simple answer? It's a difficult, quantitative topic — one with many factors to consider. Becoming a sales forecasting expert takes time and practice. But there are steps you can take to gain skills in the area and choose the right techniques.
Before diving into methods for sales forecasting, take a look at the video below. We provide an overview of forecasting strategies to give you a better understanding of the topic.
Now let's get specific. Choosing the right forecasting methods will ultimately depend on your company, but here are five possible techniques, three quantitative and two qualitative, that will help you and your company begin making better business decisions.
1. Opportunity stages forecasting
Opportunity stages forecasting allows you to calculate the chances of a deal closing in the pipeline.
It's best to use this when you want an objective understanding of your pipeline stages (your sales reps' opinion on deals, while possibly accurate, are subjective). Also use when you want to assess your sales team performance and check where they need improvement moving a prospect down the pipeline.
Most businesses can break their pipeline down into a general set of stages:
- Won or lost
The farther along a deal gets through this chain of stages, the better chance it has of making it all the way to “Won.”
To adopt this forecasting technique, you’ll need to analyze and understand your sales team’s past performance. It requires extrapolating, so a solid understanding on the rates of success from each stage is necessary to get a good estimate on future results.
If about half of your deals that reach the quote stage end up as won, then you know you’ve got a 50/50 shot for all the deals in that stage during a given quarter.
Example: Multiply a deal's potential by the win likelihood (this can be determined in most CRMs). Say that you have a $1,500 deal opportunity with a 10% likelihood to be won. Your opportunity forecast would, therefore, be $150. Complete this exercise for each deal in your pipeline and add for the overall forecast amount.
Based on the image above, let's review three deals in the pipeline. Deal 1 is in the incoming stage. Deal 2 is in the qualified stage and Deal 3 is in the negotiation stage. Multiple Win Likelihood by each deal amount:
- 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.
This basic calculation allows you to quickly estimate incoming revenue. You also have a better understanding of future opportunities based on past information.
Although it does make a numbers-based prediction, forecasting based on opportunity stages is an imperfect calculation:
- It can't account for individual characteristics of a given deal, such as a repeat client versus a new one.
- The deal value and close date have to be accurate and up-to-date in your CRM.
- In most cases, success will be binary.
Opportunity stage forecasting is a good technique for assessing deals in your pipeline and understanding incoming revenue and sales rep performance. Just remember to take your sales reps' opinions into consideration for each deal to effectively combine objective and subjective elements and get a more accurate forecast.
2. Length of sales cycle
Forecasting by the length of your sales cycle is a quantitative method that helps you predict when a deal is likely to close. Rather than analyzing success rates based on stage, this approach makes assessments based on the age of the deal. It requires your team to crunch how long your average sales cycle is. Use this technique to objectively learn about different types of deals in your pipeline.
The basic formula for average sales cycle is Total # of Days to Close Deals / # of Closed Deals:
Example: To provide a more in-depth illustration, let's say you have five deals you recently closed. Calculate the amount of days it took to close each one:
- Deal 1: 62 days
- Deal 2: 60 days
- Deal 3: 59 days
- Deal 4: 55 days
- Deal 5: 60 days
- Total: 296 days
Divide this number by the number of deals (which is five) and you get your average sales cycle of 59.2 days or roughly two months.
Now that you know your average sales cycle, you can apply to individual opportunities in your pipeline. Maybe one of your sales reps has reached the proposal stage with a lead after one month. Based on your average sales cycle length of two months, you might forecast that the rep has a 50% chance of closing the deal.
Since it’s not tied to strictly defined categories, using the length of sales cycle approach can open up the option for creating algorithms based on different types of deals. So you could have a separate set of numbers for the average repeat customer, or the average lead who comes from a website query.
Like the opportunity stages approach, this method still requires that accurate data finds its way into your CRM. Especially if you have multiple equations in the works, you’ll need to make sure that deals are being tagged and categorized correctly so that the math gives you a reliable prediction.
This technique allows you to objectively answer questions about when a deal starts, when it will end, where your sales team is in the process, and what skills they need to be applying to close the deal.
3. Regression analysis
One of the most mathematically focused choices for forecasting is regression analysis. Use this technique if you want an in-depth quantitative review of factors that might be affecting your sales and to make changes, if needed, to your sales process.
Success with this method requires a good grasp on statistics as well as on the factors influencing your company’s sales performance. It involves calculating the relationships between variables that impact sales. Traditional steps with a regression analysis include:
- Determine the reasons for forecasting (what you want to learn and why).
- Determine the factor that is being affected such as sales (your dependent variable).
- Determine factors that might be affecting your sales (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.
- Look for correlation between variables.
Example: You want to forecast sales for the next year to help you plan for budget allocations and to determine if more sales reps need to be hired. Sales will be your constant, dependent variable — the factor that you are trying to understand.
To complete a single variable analysis, let's say you determine that the variable impacting sales include sales calls. This is your independent variable.
- Dependent Variable (y): Sales (SALES)
- Independent Variables (x): Sales Calls (SALESCALLS)
You collect data for both your dependent and independent variables over an eight-year time frame: your annual sales for 2010-2018 and the number of sales calls during that time period.
The simple regression model equation is Y = a + bX. Your equation could therefore be: SALES = a + b * (SALESCALLS) with a representing the intercept and b representing the slope respectively. Use a regression software (Excel has this capability) to run the analysis. Note that you will not have to compute a or b yourself — this will be generated by the regression software.
You are looking for the “line of best fit” to approximate the relationship between the variables. Your plot might look something like this:
b, the slope, is 0.907 and a, the intercept is -313.
Based on this model, sales calls do look closely correlated to sales and may be causing better sales. However, just because a variable is correlated does not 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 multiple independent variables such as number of emails sent, number of demos given, number of meetings held, etc.
Regression analysis helps you determine which variables actually have an impact on your sales.
While this approach can yield very accurate forecasts, it's one of the most advanced levels of forecasting. For some companies, being able to account for many variables that go into a successful sale may require a PhD in mathematics. In addition, a large quantity of clean and accurate data is required for meaningful results.
Regression analysis takes skill and practice to execute and understand results properly. However, running regressions correctly can reveal valuable information about your business that will help with future growth.
4. Forecast stages
This qualitative approach is best used if you want an individual sales rep assessment technique and/or if you want to determine the expected value of deals. It relies on the insights and intuition of the sales reps rather than on a deal moving through pre-determined stages.
With forecast stages, reps make a personal projection about the outcome of any given sales opportunity. For instance, they may be certain that a customer is ready and willing to make a purchase, or the opportunity may need several things to come together for success.
The exact terminology may vary from one business to another, but the key here is that the reps are making a judgment call on how likely each of their deals is to close. When this information comes at the beginning of a deal’s lifespan, it can help managers and execs to get a long-range view of results. The sooner they have that intel, the better their financial predictions will be.
Your sales reps make predictions on opportunities in the sales pipeline and sort into categories. These categories typically include:
- Best Case
Example: Your sales rep might record something like the following for two deals:
- Deal 1 - Best Case: “I'm not sure about this opportunity, the lead seems hesitant and has mentioned a competitor several times. I think the only way he's going to buy is if Rob contacts him with a customized proposal on Monday.”
- Deal 2 - Commit: “I just started on this profile, but given certain indications in our phone calls and emails, I believe that this customer is going to buy. I'm on it with the demo and dedicated to closing this one!”
Since this technique is not subject to waiting until an opportunity makes it to a later stage, you can forecast in advance. Your sales reps also know the opportunities/deals best and should be able to make closer predictions.
The downside to this approach is that it’s not a hard science. It's subjective. It requires that your whole team of reps is able to make honest assessments of their potential clients and their own skills. If you don’t have confidence in your reps, then this approach will lead to lots of disappointment for your business.
This technique gives you an inside look at your sales team's opinions on deals and helps you determine if additional steps need to be taken to close them.
5. Scenario writing
Our final technique is another qualitative approach, which is excellent to use for long-term planning and for possible extremes that data may not always be able to account for. Just like forecast stages, it also 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 pictures that could unfold based on the different sets of 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:
Example: Scenario writing is based on storytelling. Say that your focal issue is your yearly sales. You then move to key internal factors that you believe are affecting your sales such as sales calls, or inquiries received, or demo meetings held. Some of the external factors that might have an impact are competitors or government restrictions.
For critical uncertainties, examine what difficulties might arise over the next year. Will the customer start leaning more towards new technology? Will possible government policies affect the nature of your business? Based on this information, you can begin to develop specific scenarios and understand how you would go about handling each one.
This approach helps you think strategically about what could happen with your sales and helps you make plans accordingly. Think of it as a type of contingency plan.
As with forecast stages, this strategy involves at least one person having a keen eye for both business activity and psychology. Both of these subjective strategies are more an art than a strict science, so they’re best used as a complement to a more numbers-driven method. Combining the strengths of both approaches will be more likely to create a full picture of what the future holds for your company.
For scenario writing to be effective, plan your scenarios around uncertainties with your business and have a clear action plan if one of the scenarios were to occur.
Next steps for your forecasting techniques
Whichever sales forecasting techniques you choose, remember that you aren't limited to just one. Use multiple forecasts to get a full picture of your sales approach. Decide which ones will be most effective for your company and begin applying. Don't get caught up in “paralysis by analysis” either. Although accurate data is important, the aim is for valuable, not perfect, information.
Using any of these forecasting techniques appropriately takes practice, but will assist you in being more objective with your sales process and look to the future.