It’s the beginning of the year and all B2B organizations are busy sketching out plans for the new year. Out of all the plans that you are making in your company, its future depends on sales forecasting. It’s the reason why the average sales manager spends 37% of their time forecasting sales—and not just during the beginning of the year.
But sales forecasting can be complicated—you need to factor in all stages of the sales process, from creating an opportunity to closing it to get to a realistic prediction. And the more confusing part is—you have so many options! While there are a myriad of forecasting techniques that can help you achieve what you want, this ‘paradox of choice’ can also cripple your forecasting speed.
To make matters easier for you, we have compiled a list of the eight most commonly used types of sales forecasting techniques. Some of these techniques will help you make short-term forecasting while others are more practical for long-term planning purposes.
8 forecasting techniques that you can evaluate and apply
If your goal is near-perfect sales forecasting, you have plenty of methods to iterate your sales projections so that you can benchmark the forecasting outcome of one method against another. Over time, you can run different forecasting methods to figure out which one works the best for you.
On a side note, we’ve also put together a detailed post on how to do your sales forecasting, the challenges on the way, and key KPIs to ensure better forecast accuracy.
But let’s get back to the eight types of sales forecasting techniques!
A lot of them are bottom-up, meaning they require you to start by projecting the expected number of seats or licenses you want to sell and multiplying that number by the average cost per unit.
A few of them are top-down sales forecasts, which look at the big picture view of the industry you operate in (i.e., your total addressable market or TAM) and break down your forecasting goal by calculating a fraction of that TAM that you can realistically win.
1. Intuitive forecasting
Intuitive sales forecasting uses intuitive data, i.e., the opinions of your sales reps on how they feel about the deals in their pipeline. This sales forecasting method believes that sales reps are the closest to the deals and therefore have the best data about the likelihood of the prospects converting—or not.
Intuitive forecasting also makes it fairly easy for sales teams to capture the forecasting data: you just ask the reps how confident they are about a deal they are handling and enter their opinions as the required data point.
When a sales manager asks their rep about the health of a deal during their pipeline review meeting and the rep answers back, "I'm pretty confident that I will close the sale within the next 7 days for 10 seats worth $1860," they are essentially exercising the intuitive sales forecasting method.
As you can imagine, there are glaring issues with this forecasting method. Opinions are often flawed, subjective, and exaggerated. From the team perspective, the sales manager entering the forecast data has no way to test the veracity of the reps’ opinions. Some reps are naturally optimistic about all the prospects they interact with while others tend to be generally neutral.
Intuitive forecasting works great when you use it in conjunction with other, more scientific methods, of forecasting.
2. Historical forecasting
Like the name implies, this method of sales forecasting makes use of historical sales data to generate forecasts. Historical forecasting is just like weather forecasting: to figure out how much you will sell in a month, quarter, or year—you need to look back at the matching timeline from the past and predict that the results will be equal to or greater than the historical data.
The good thing about historical forecasting is that it’s easy to cross-match historical sales data to predict future sales targets. On the downside, this forecasting falsely assumes that the market is always constant. For instance, it doesn’t take into account seasonal factors (e.g. holidays, fiscal year closing) or uncertain market environments (e.g., recession, wars, layoffs).
More on that, later. For now, make a note about using historical sales forecasting as a reference point rather than the basis of your forecasting process.
3. Statistical forecasting
In an intuitive sales forecasting method, you use qualitative data (e.g. opinions, confidence, optimism) as the foundation for forecasting. In statistical forecasting, it’s just the opposite. Any forecasting method that uses quantitative data or statistics is an example of a statistical forecasting method.
The quantitative data points could range from the stock market collapse, rising GDP, housing sales, inflation, or even historical sales numbers.
The biggest problem with the statistical forecasting method is that there are just too many variables that you can go after—and figuring out which one is most suitable for you is an uphill task.
But if you can identify the right statistics to bank on, statistical forecasting can give you pretty accurate results.
4. Opportunity stage forecasting
Opportunity stage forecasting works around the interesting premise that the longer a prospect progresses along the sales pipeline, the better their chances of converting.
For example, let’s assume that a prospect who just signed up for your product’s free trial is 20% likely (probability percentage) to convert into a paying customer. Another prospect who attended the product demo is 80% likely to sign a contract.
To make opportunity stage forecasting more effective, you need to assign a reporting period to it: monthly, quarterly, or yearly. This timeline is usually the average length of your sales cycle or the quota that you have set for the sales team.
The accuracy of your opportunity stage forecasting is dependent on the efficiency of your pipeline management. Here’s an article that might help you dig deeper into it.
A different version of opportunity stage forecasting is “weighted sales pipeline”—it assigns a weighted score (from 0% to 100%) to calculate the win probability of a deal based on what stage of the sales it is in. Eventually, it lets you measure the expected revenue in your sales pipeline.
One good thing about opportunity stage forecasting is that it lets you calculate the expected value of a deal. Once you choose the timeline to close, you just need to multiply a deal’s potential value by the probability to close. That means if the prospect has attended a demo (i.e. 80% probability) and their deal is worth $2000, your forecasted deal value is $1600.
There’s just one downside to this method. It assigns the same probability to close value to prospects who are in a certain stage in your pipeline—regardless of how long they are there. It doesn’t differentiate the prospect who is at the demo stage of the pipeline for the last two days versus another prospect who is there for the last two months—who, let’s be honest, might be a lost cause.
5. Lead-driven sales forecasting
If you run a lot of sales and marketing campaigns to generate leads, this method can help make good predictions. Lead-driven forecasting method uses predictive analysis to assess all of your lead gen channels and assign a value to each lead based on past customer behavior.
Assigning a value to each lead helps you get an idea about the probability of their conversion and the potential deal amount they represent.
To get accurate results from lead-driven forecasts, you should know three important data points:
- the average sale price by lead gen channel
- # of qualified leads per each sales rep per month
- the percentage of leads that converted into paying customers
Once you start sourcing this information, you will end up having a forecasting spreadsheet entry that looks like this:
6. Length of sales forecasting cycle
If you aren’t happy with the results you are getting out of the intuitive forecasting techniques, the length of sales cycle forecasting can help. Unlike the intuitive method, the length of sales forecasting relies heavily on hard data—instead of the reps’ opinions—and avoids chances of error.
Here’s how it works: let’s say one of your reps is engaging with a prospect who signed up for your product six weeks back and who you just qualified as a potential lead. Based on their interaction with the prospect, the rep might say that the prospect is very likely to buy your product in the next few weeks.
But if your sales team’s average length of the sales cycle is about three months, you know that there is only a 50% chance that the prospect will convert.
Keep in mind that you can have different sales cycles for different leads by channel. Outbound leads might take two months to close, referrals might need just one month, and leads from events might take significantly longer. You will have to apply different averages to different prospects based on the prospecting source.
7. Multivariable analysis forecasting method
With multivariable analysis forecasting, you have the advantage of using multiple business data points to make sales forecasting. To make effective sales forecasting, this method looks at the length of your sales cycle, the win rate of each opportunity type, and your reps' win rates.
For example, let’s say you have two sales reps collaborating to close the same deal. The first rep is really good at closing discovery stage deals with an average win rate of 50% while the second rep is a pro at demo-to-close deals with a win rate of 40%.
The estimated deal value at the discovery stage was $1400 while it’s forecasted to be $2500 during the demo stage. Combined, the cumulative deal value amounts to $3900 with a win rate of 90%.
The upside of using this method is that experts endorse it as one of the most accurate forecasting techniques. However, multivariable analysis demands that you need a lot of clean data, an advanced level of analytics, and a hefty sales budget. If you aren’t fully committed to one of these variables, the method is likely to generate inaccurate forecasts.
8. Test-Market analysis forecasting
The test-market analysis is a great tool to forecast sales when you are launching a new product or service and aren’t sure how the market will respond to it. This method works great if you already have access to an audience.
For instance, if you are an enterprise software company ready to launch a new product, you can apply this method to your existing customer base to test the market response. If you are a second-time startup founder, you can use the test-market analysis to do a soft launch of your product to identify your ideal customer profile.
Once you run the analysis on a limited set of audience, you can study the results to make full-fledged predictions before the full release.
The only caveat: don’t assume that all markets behave the same way to each new product or service. You have to be careful about extrapolating the forecast data from one market to another because of the differences in their demographic, psychographic, and technographic variables.
What’s best for you?
The answer obviously depends on many variables like what industry you serve in, what you sell, who you sell to, how big is your salesforce, and what are your business goals. From what we have learned based on our interactions with most scaleups who are Avoma customers, most fast-growing businesses commonly use a combination of intuitive and opportunity stage forecasting.
The sales leaders in these companies use their sales reps’ intuition to gauge their confidence in the deals that they are managing—but they hardly stop with that. It helps them reorder the deals based on priority and give corresponding attention to others. They then use a more scientific, data-led forecasting technique to validate their findings and calculate a forecasting goal.
At the end of the day, what technique you use for forecasting your sales is less important than how well you execute it. Our suggestion: go with the one that feels most feasible for you and limit your forecasting window to a month or a quarter to start with. Change tack if you feel that your predictions are off and stick to the one that gives you the most accurate outcome.