A recent Gartner survey revealed that only 45% of sales leaders and sellers have high confidence in their organization’s sales forecasting accuracy.

This begs the question: Are businesses truly harnessing the power of sales forecasting, or are they still relying on forecasting methods that are little better than crystal balls?

If you're nodding along, thinking, "Yes, that sounds about right," then it's time to think about a more accurate way to anticipate future sales.

Let’s explore seven sales forecasting methods that can transform your approach to predicting and driving revenue.

What is Sales Forecasting?

Ever had your fortune read at a county fair? It's a fun gimmick. But when it comes to a sales forecast, you need something a bit more sturdy than a crystal ball in a tent.

Forecaster Paul Saffo said as much when writing for the Harvard Business Review in 2007.

What he calls the “mythical seers” are concerned with predictions.

Yet, sales forecasting isn't about predicting the future with pinpoint accuracy. It's about analyzing sales data and market trends to track your growth potential and make informed decisions.

A sales forecasting model is a good practice that provides "essential context that informs your intuition," so you can:

• Get a comprehensive view of potential scenarios based on market conditions and;
• Position your sales team to seize opportunities.

Translating this wisdom into practice is a challenge for many companies. Luckily, there are tried and true ways to make predictions based on market analysis.

7 Sales Forecasting Methods: Pros, Cons, And Best Scenarios

Each sales forecasting methodology has its merits and is suited for different scenarios.

Depending on your company’s unique set of circumstances, combining forecasting models might even be the most appropriate approach.

Let’s look more closely at seven sales forecasting methods for making data-driven decisions.

1. Historical Forecasting

Historical sales forecasting relies on past sales data to predict future performance.

Since historical data is based on actual events, it’s a solid foundation for your predictions, especially in stable markets and economic conditions.

Saffo finds that companies’ past consumption patterns are powerful forecasting tools, as long as you look far back enough.

He advises that when you use a historical forecasting model, “always look back at least twice as far as you are looking forward. Search for similar patterns, keeping in mind that history—especially recent history—rarely repeats itself directly.”

Sale forecasting challenge

Saffo hints at the main issue with historical forecasting: it assumes that past sales data is a reliable indicator of the future, which isn’t always the case (particularly in dynamic markets or economic variables).

If there’s a big shakeup in market conditions, like a new competitor or a sudden market shift, historical forecasting might not see the fluctuation coming.

If you're working with a new product with little relevant data on past consumption patterns, you'll likely struggle to forecast sales.

Best Scenario

Historical sales forecasting shines in stable, predictable markets where past market trends are likely to continue and there are limited external factors.

The forecasting model works particularly well for products or services with consistent sales cycles and minimal market fluctuation, such as:

• Banking;
• Consumer products or services;
• Maintenance services.

For example, a company selling household cleaning products can rely on historical data to predict future sales, as consumer demand in this sector remains relatively stable over time.

2. Length of Sales Cycle Forecasting

Length of sales cycle forecasting is all about timing.

This sales forecasting model hinges on one simple question: how long does it take for a prospect to convert into a paying customer?

If you know this, you’re able to look at your sales pipeline and predict future sales based on how likely that customer segment is to close.

Let’s assume your typical sales cycle is 12 months, and an opportunity has been in progress for 3 months. Then you know there’s a rough 25% chance that the deal will close.

Sale forecasting challenge

While quite straightforward, this sales forecasting method presumes that the length of the sales cycle is consistent, which may not always be the case.

If your sales cycle varies significantly from deal to deal, length of sales cycle forecasting might not be as reliable.

Best Scenario

Length of sales cycle forecasting is a good practice for businesses with well-defined, consistent sales processes where the sales cycle length is relatively stable—such as B2B services or manufacturing.

3. Lead-Driven Forecasting

Lead quality is the linchpin of a lead-driven sales forecast.

With this sales forecasting model, you assign scores to each lead based on factors such as:

• Lead source;
• Prospect engagement level;
• ICP fit.

Looking at the score, you can determine which leads are most likely to convert into a future sale.

For example, let’s say your scoring criteria looks like this:

Lead Source: Webinar (10 points), Free Trial (8 points), Content Download (5 points)

Engagement Level: High (10 points), Medium (5 points), Low (2 points)

ICP Fit: Excellent (10 points), Good (5 points), Poor (0 points)

Let's break down different customer segments:

1. A lead from a webinar who has interacted with your website frequently and fits your ICP perfectly might score 30 points (10 for the source, 10 for engagement, 10 for ICP fit).
2. A lead from a content download who also interacts with your website regularly but is a poor fit for your ICP would score 15 points. You know the first one has a high likelihood of conversion, whereas the latter isn’t quite there.

Sale forecasting challenge

The tricky aspect of this sales forecast is the scoring. To set up an effective system, you need:

• Comprehensive data on your leads and revenue trends;
• Clear and well-defined scoring criteria;
• Team training;
• The capacity to maintain an updated list of leads and respective scores.

If done manually, working on this sales forecast can be a veritable time-suck.

Plenty of lead-scoring sales forecasting software can get the job done, but that’s a financial burden not every organization can carry.

Best Scenario

Lead-driven forecasting works well in environments with a high volume of leads and detailed tracking of lead quality and conversion rates.

Businesses with robust sales operations and lead generation processes, such as digital marketing agencies or SaaS companies, might find this to be a good sales forecast good method to rely on.

4. Opportunity Stage Forecasting

Opportunity stage forecasting predicts sales outcomes based on an opportunity's location in the sales pipeline.

As a new lead source progresses through each stage, you use historical revenue data to estimate the odds of a successful closure.

So, if past data shows that deals in the demo stage typically have a 45% chance of closing, you apply this probability to current deals in the same stage and plan accordingly.

Sale forecasting challenge

One significant drawback to this type of sales forecast is that opportunity stage forecasting doesn’t consider how long a prospect has been in a given stage against the average sales cycle.

A deal that has stalled for several months is given the same probability as a new lead source in the same stage, despite the former being less likely to close.

Best Scenario

Opportunity stage forecasting is a good option for businesses with a high volume of sales opportunities and detailed customer relationship management (CRM) data.

Think tech or SaaS companies, where deals tend to progress through clearly defined stages.

5. Regression Analysis

This statistical method examines the relationship between different variables to forecast sales. Essentially, it helps answer the question of how does X impact Y?

The regression model equation looks like Y = a + bX, where Y is the dependent variable (the outcome of what you want to predict, such as sales performance), and X is the independent variable that affects Y (like the number of sales reps on your team or the number of calls each sales rep makes).

Implementing a regression analysis involves:

1. Identifying your dependent and independent variables

2. Deciding the time frame for which you will collect and analyze data

3. Collecting data on both variables over the chosen time period

4. Choosing a regression model (like linear regression) and running the analysis on statistical software like Excel

5. Interpreting the results (how changes in independent variables impact your dependent variable)

Sale forecasting challenge

As you can tell, regression analysis requires a good grasp of statistics and high-quality, comprehensive data for an accurate sales forecast.

If the data quality is poor, the predictions will be unreliable. This method is not something to dive into without some analytical skills or support from an operation research department.

Best Scenario

Regression analysis is the ideal sales forecast method in industries with diverse and fluctuating sales drivers, such as:

• A local product or service;
• Retail;
• Real estate;
• Financial services.

For example, a retail company can use linear regression analysis to understand how variables like advertising spend, seasonal trends, and economic indicators affect their sales.

6. Multivariable Analysis

Multivariable analysis is regression analysis dialed up to 11.

It’s right there in the name — it looks at multiple factors simultaneously (seasonality, rep performance, historical sales data, and more) to offer a comprehensive view of what affects your sales.

Sale forecasting challenge

Unsurprisingly, multivariable analysis is quite complex.

Analyzing multiple variables requires not just high-quality data but also significant expertise and sophisticated tools, both of which might not be attainable for smaller enterprises.

Best Scenario

Multivariable analysis is the way to go for complex sales environments with numerous influencing factors — industries like technology, pharmaceuticals, and automotive, where multiple variables such as R&D investment, regulatory changes, and market competition significantly impact sales growth potential.

7. Intuitive Forecasting

Intuitive forecasting is the art of trusting your gut.

This type of sales forecast relies on your sales team's insights and instincts to predict future sales, stepping away from the quantitative realm and into the qualitative (closer to Saffo’s mythical seers than any other method on this list).

Sales forecasting challenge

While this revenue forecasting is quick to implement and can be highly flexible, intuitive forecasting is still subjective and, consequently, prone to bias — forecasts can vary significantly depending on who’s making them, leading to inconsistencies. All in all, it’s not the most reliable method out there.

Best Scenario

Intuitive forecasting is useful in new or rapidly changing markets where historical data is lacking.

It’s particularly beneficial when you have a highly experienced sales team with a deep understanding of your market dynamics to increase the likelihood of an accurate sales forecast.

How to Choose the Right Sales Forecasting Method

How do sales teams land on the ideal sales forecasting method (or combination of methods) and close more deals?

You experiment. Here’s a sales forecasting guide  to walk you through the process.

Consider Your Resources and Environment

While all sales forecasting methods are designed with the same goal in mind, each demands a different set of resources and circumstances.

So first, ask yourself:

How much sales data do you have, and how good is it?

Data is your number one consideration.

Chambers, Mullick, and Smith’s advice from 1971 still rings true today: choose a method that makes the best use of available data.

If you can readily apply one method of acceptable accuracy, do that instead of adopting a more advanced method that offers potentially greater accuracy but that requires information you don’t have or is of poor quality (because accuracy then becomes a moot point). This will help sales managers make informed decisions.

Methods like regression and multivariable analysis thrive on robust datasets.

If your data pantry is well-stocked, then you’re in a good position to use them.

But if you have little information to work with, you need methods that don’t rely on so many distinctive datasets.

How long or short is your sales cycle?

Length of sales cycle forecasting is ideal for businesses with well-defined, stable sales processes.

If your sales cycles are predictable and consistent, this method can provide reliable, accurate sales forecasts.

On the other hand, if your sales environment is more dynamic and deals vary significantly in length, methods like opportunity stage forecasting—which accounts for the progress of each deal through the sales pipeline—might be more convenient.

How big is your team, and what expertise do they bring to the table?

Complex methods like multivariable analysis require statistical expertise and are best suited for larger teams with specialized skills to help you make data-driven decisions.

If that doesn’t apply to your situation, simpler methods(such as historical sales data forecasting) are more straightforward and can be easily implemented by smaller teams with less technical expertise.

What market conditions are you operating in?

Historical sales data forecasting might be sufficient in stable markets, as past trends are likely to continue.

However, a more adaptive approach is necessary in volatile or rapidly changing markets. Conduct a market analysis to understand the state of your competitive landscape.

Combining multiple methods is a viable option, as is using dynamic techniques like lead-driven forecasting. They both help you navigate uncertainties and adjust your strategies accordingly.

Run a Pilot Test and Analyze the Results

After considering the above factors, you’ll be able to shortlist candidates for testing.

Let’s break this down into a specific example:

1. Imagine you’re the sales leader of a mid-sized B2B software company.
2. You have a well-defined sales cycle that typically takes six months and a reliable dataset from the past three years.
3. Your market and economic conditions are relatively stable, but:
4. You're planning to enter a new segment that could bring some unpredictability.

Given these conditions, you might start with historical forecasting to leverage your data.

However, to prepare for the new market segment, you could also incorporate lead-driven forecasting to adjust for current market dynamics.

Once you have your candidates, test them on a small scale to see how they perform. This could involve running an experiment over a specific time series or using a subset of your sales data.

After the pilot testing comes the critique session. Compare the outcomes of your experiments against actual sales results to determine which method provides the most reliable and actionable forecast.

In addition to accuracy, it's a good practice to consider ease of use and how well each method integrates with your existing processes.

Adjust and Refine

Based on your analysis, adjust your chosen methods as necessary.

Perhaps you find that a combination of methods works best for your needs or that you need to run a new experiment to compare other methods you didn’t consider initially.

Your market, team, and overall business aren’t stagnant. As new data becomes available and both internal and external conditions evolve, keep refining your approach and evaluate new potential methods.

From Accurate Forecasts to Performance Excellence

If your sales strategy is a high-performance engine, effective sales forecasting is the dashboard that tells you how fast you're going, how much fuel you have left, and when you need to make a pit stop.

Revenue forecasting doesn’t just tell you what lies ahead; rather, it helps you make smart decisions to optimize your journey.

But forecasting is just one piece of the sales performance puzzle.

For a deeper dive into optimizing your sales strategy and ensuring your sales team exceeds their targets, check out our guide to sales performance management.