December 13, 2024
With 2025 just around the corner, it's time to discuss our targets and projections for another ‘new’ year ahead. Among all the planning B2B organizations should be making, sales forecasting is the critical one—your future depends on it.
Creating an accurate forecast can be challenging. After all, it’s a skill that demands an artistic touch to visualize future trends, a scientific approach to analyze data accurately, and mathematical prowess to quantify expectations. Adding to this challenge is the overwhelming ‘array of choices’, which can slow down the process and make it feel daunting.
Let’s help you with the most effective forecasting methods so that you can avoid information overload and focus on achieving your revenue targets.
In this blog, we have curated a list of the 11 best forecasting methods in sales to help you forecast with more sophistication and engagement.
Sales forecasting involves estimating future sales revenue based on historical data, market conditions, and other influencing factors.
In case your organization is looking to anticipate how much revenue it can expect to generate and when that revenue will come in, sales forecasting can help them unlock the answers.
Essentially, it aids in:
On the other hand, inaccurate and ineffective forecasting can instigate several challenges, such as poor resource allocation, financial instability, operational inefficiencies, inventory management issues, strategic decision-making flaws, and many more.
(While on the subject, we have created an in-depth post on the best sales forecasting tools that offer superior accuracy, which might be helpful for you. For now, let’s focus on the techniques.)
Before diving into the best sales forecasting methods, let’s address the elephant in the room for a better understanding–demand forecasting vs sales forecasting.
Although both help businesses make informed decisions, demand forecasting and sales forecasting are different strategies. The crucial difference is that demand forecasting is market-focused, assessing overall consumer demand, while sales forecasting is company-focused, estimating what a particular business will achieve in sales.
Just like there are multiple ways to close a deal, there are different methods of forecasting sales. Primarily, there are two methods: Qualitative and Quantitative.
Of the below-enlisted methods, many take a bottom-up approach (projecting the expected number of seats or licenses you plan to sell and multiplying that by the average cost per unit).
On the other hand, some methods use a top-down approach (focusing on the overall industry picture—looking at your total addressable market (TAM) and then estimating your potential share of that market).
Qualitative forecasting relies on judgment and opinion rather than numerical data, which is useful in cases where historical data is insufficient, such as for new products or industries with rapidly changing trends.
These forecasting methods are applicable when:
Qualitative methods often rely on human judgment and rating systems to convert qualitative information into quantitative estimates.
Below are some commonly used qualitative sales forecasting methods:
As the name implies, intuitive forecasting relies on the informed opinions of sales reps about the deals in their pipelines. This approach is built on the idea that sales reps, due to their close interactions with prospects, have the clearest insights into the likelihood of conversion.
Intuitive forecasting can easily integrate into your workflow if your sales teams conduct regular check-ins and collaborative pipeline reviews. However, it requires a thorough understanding of each prospect and an honest assessment of opportunities—making it potentially exhausting and sometimes susceptible to bias or subjective judgment.
The survey method involves gathering data directly from potential customers or target demographics through surveys or interviews. This approach is particularly effective for gauging interest in new products, understanding customer preferences, or exploring a new market.
For example, a company launching a new product can survey target customers about their interests and preferences, helping to anticipate demand before investing heavily in production.
Surveys can be conducted via email, phone, online questionnaires, or in person. The data collected provides valuable qualitative insights that, while subjective, help businesses make more informed decisions about product offerings and market expansion.
With the test-market method, companies introduce a product to a limited market area to evaluate its performance before a full-scale launch. This approach is popular for innovative or unique products that may need consumer validation before mass production.
Test-market analysis offers more than just revenue projections—it gives you a deeper understanding of customer needs and preferences. You can combine this approach with customer feedback or surveys to gain valuable insights for refining your product.
The Delphi technique solicits opinions from a panel of experts on a specific product, service, or market trend. Experts contribute their knowledge of emerging industry trends, customer preferences, and competitive pressures. This method is particularly useful when launching a new product in a competitive market or where little quantitative data exists.
The Delphi technique is highly structured: experts provide their opinions in rounds, each time refining their responses based on the feedback received. Over time, this iterative process leads to a consensus forecast, supported by seasoned expertise.
In the executive opinion method, senior executives use their experience and insights to predict future sales. Executives often have a macro perspective on the industry and company strategy, allowing them to make informed judgments about future trends.
This approach can be effective for high-level planning, particularly in industries where market dynamics are influenced by regulatory, political, or economic factors. Although subjective, executive opinion can also bring a highly valuable organizational perspective during times when quantitative data is scarce or unreliable.
Quantitative forecasting methods rely on statistical and mathematical models, using historical data to identify patterns and trends. These techniques are highly accurate when there is ample historical data, making them suitable for industries where products and demand patterns are stable over time.
Below are some commonly used quantitative sales forecasting methods:
Opportunity stage forecasting allows you to predict the likelihood of a deal closing based on where the prospect is in the sales pipeline. It is easy to implement and offers a better understanding of the sales process by giving essential information about how likely they are to make a sale at different steps along the way. This helps them make smart choices to improve their sales.
For example: Imagine a company has three active sales opportunities in different pipeline stages:
Opportunity 1: In the Qualification stage with a potential value of $1,000.
Opportunity 2: In the Proposal stage with a potential value of $2,000.
Opportunity 3: In the Negotiation stage with a potential value of $1,500.
And the probabilities of closing each stage are Qualification (30%), Proposal (50%), and Negotiation (70%).
Here’s how the forecast can be calculated:
The simple moving average method calculates the average sales over a specific period (e.g., three months or a year) to predict future demand. Each time period is treated equally, providing a straightforward way to estimate average demand without emphasizing any particular season.
For example, a business may calculate the SMA for the past twelve months to forecast sales for the next month. SMA is ideal for stable industries where demand remains relatively constant and is less affected by seasonal fluctuations.
This forecasting method is straightforward; it helps you predict exactly when a deal is likely to close based on your sales cycle’s length. You don’t need to rely on the rep’s feedback or gut feeling to predict revenue outcomes. You must carefully track how and when leads enter your sales pipelines to get the most accurate forecasts.
To calculate, 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.
The weighted moving average method assigns different weights to time periods, giving more importance to certain periods based on expected seasonal demand. This method is beneficial for industries with clear seasonal patterns, like retail or food and beverage, where demand may peak during certain months.
For example, a company may assign higher weights to months leading up to the holiday season, ensuring that inventory levels match the expected surge in sales. This method helps avoid overproduction or underproduction during key selling periods.
Exponential smoothing is a sophisticated approach that applies decreasing weights to past data as it moves further from the present.
This forecasting method adjusts the forecast based on recent observations, making it highly responsive to sudden changes in demand trends, and is effective in industries where demand patterns are prone to short-term fluctuations.
Linear regression is commonly used when companies want to analyze how specific factors influence sales, like marketing spend or product pricing.
For example, a company may use linear regression to determine if there is a direct relationship between advertising expenses and sales growth. Linear regression models can provide actionable insights for budgeting and strategic planning by quantifying how much specific factors impact demand.
To conduct this analysis, sales teams must have a clear understanding of the internal and external factors affecting their sales and determine the reasons for forecasting, factors affecting sales (dependent variable), areas that are being affected (independent variable), and review time period.
Once you have collected the information, collect the data for both dependent and independent variables, choose a regression model and run it, and look for correlation between variables.
Choosing the best forecasting technique involves balancing complexity, accuracy, and the business environment. Here’s a guide to help you select the suitable method:
Analyze data trends and consistency. Quantitative techniques, such as regression or time series analysis, excel when historical data is extensive. In contrast, qualitative methods, like expert panels, work well for new products lacking past data.
The timeframe of your forecast influences the choice of method. Short-term needs, like inventory management, benefit from simpler approaches like moving averages, while long-term planning may require complex models to capture broader trends and market shifts.
Forecasting should match your team’s capacity. Smaller teams may find simpler models or software solutions more manageable, while sophisticated techniques may demand more resources and advanced analytical skills.
Stable industries can often rely on quantitative methods like exponential smoothing, which incorporates recent trends. However, qualitative approaches in volatile industries can offer flexibility to adjust to rapid changes.
Align your method with business priorities. For seasonal patterns, weighted moving averages capture fluctuations effectively, while for highly variable demand, a combination of methods might provide a more comprehensive view.
The need today is not for better forecasting methods but for better application of the techniques at hand.
Understanding the basic features and limitations of the above-listed sales forecasting methods can help you properly frame the forecasting problem and use them more effectively. Over time, you can experiment with different approaches to determine which method works best.
Intrinsically, the lack of a clear, organized, and transparent process can hinder accurate forecasting, letting crucial data slip through the cracks. It is, hence, crucial that you automate as much data as possible to let your team focus on important tasks, ensuring leaders have a clear view of the sales pipeline for confident forecasting.
Forecast with confidence by configuring Sybill with your CRM for instant answers and real-time updates on deal progress, risks, and opportunities after every meeting. Click here to catch Sybill in action.
With 2025 just around the corner, it's time to discuss our targets and projections for another ‘new’ year ahead. Among all the planning B2B organizations should be making, sales forecasting is the critical one—your future depends on it.
Creating an accurate forecast can be challenging. After all, it’s a skill that demands an artistic touch to visualize future trends, a scientific approach to analyze data accurately, and mathematical prowess to quantify expectations. Adding to this challenge is the overwhelming ‘array of choices’, which can slow down the process and make it feel daunting.
Let’s help you with the most effective forecasting methods so that you can avoid information overload and focus on achieving your revenue targets.
In this blog, we have curated a list of the 11 best forecasting methods in sales to help you forecast with more sophistication and engagement.
Sales forecasting involves estimating future sales revenue based on historical data, market conditions, and other influencing factors.
In case your organization is looking to anticipate how much revenue it can expect to generate and when that revenue will come in, sales forecasting can help them unlock the answers.
Essentially, it aids in:
On the other hand, inaccurate and ineffective forecasting can instigate several challenges, such as poor resource allocation, financial instability, operational inefficiencies, inventory management issues, strategic decision-making flaws, and many more.
(While on the subject, we have created an in-depth post on the best sales forecasting tools that offer superior accuracy, which might be helpful for you. For now, let’s focus on the techniques.)
Before diving into the best sales forecasting methods, let’s address the elephant in the room for a better understanding–demand forecasting vs sales forecasting.
Although both help businesses make informed decisions, demand forecasting and sales forecasting are different strategies. The crucial difference is that demand forecasting is market-focused, assessing overall consumer demand, while sales forecasting is company-focused, estimating what a particular business will achieve in sales.
Just like there are multiple ways to close a deal, there are different methods of forecasting sales. Primarily, there are two methods: Qualitative and Quantitative.
Of the below-enlisted methods, many take a bottom-up approach (projecting the expected number of seats or licenses you plan to sell and multiplying that by the average cost per unit).
On the other hand, some methods use a top-down approach (focusing on the overall industry picture—looking at your total addressable market (TAM) and then estimating your potential share of that market).
Qualitative forecasting relies on judgment and opinion rather than numerical data, which is useful in cases where historical data is insufficient, such as for new products or industries with rapidly changing trends.
These forecasting methods are applicable when:
Qualitative methods often rely on human judgment and rating systems to convert qualitative information into quantitative estimates.
Below are some commonly used qualitative sales forecasting methods:
As the name implies, intuitive forecasting relies on the informed opinions of sales reps about the deals in their pipelines. This approach is built on the idea that sales reps, due to their close interactions with prospects, have the clearest insights into the likelihood of conversion.
Intuitive forecasting can easily integrate into your workflow if your sales teams conduct regular check-ins and collaborative pipeline reviews. However, it requires a thorough understanding of each prospect and an honest assessment of opportunities—making it potentially exhausting and sometimes susceptible to bias or subjective judgment.
The survey method involves gathering data directly from potential customers or target demographics through surveys or interviews. This approach is particularly effective for gauging interest in new products, understanding customer preferences, or exploring a new market.
For example, a company launching a new product can survey target customers about their interests and preferences, helping to anticipate demand before investing heavily in production.
Surveys can be conducted via email, phone, online questionnaires, or in person. The data collected provides valuable qualitative insights that, while subjective, help businesses make more informed decisions about product offerings and market expansion.
With the test-market method, companies introduce a product to a limited market area to evaluate its performance before a full-scale launch. This approach is popular for innovative or unique products that may need consumer validation before mass production.
Test-market analysis offers more than just revenue projections—it gives you a deeper understanding of customer needs and preferences. You can combine this approach with customer feedback or surveys to gain valuable insights for refining your product.
The Delphi technique solicits opinions from a panel of experts on a specific product, service, or market trend. Experts contribute their knowledge of emerging industry trends, customer preferences, and competitive pressures. This method is particularly useful when launching a new product in a competitive market or where little quantitative data exists.
The Delphi technique is highly structured: experts provide their opinions in rounds, each time refining their responses based on the feedback received. Over time, this iterative process leads to a consensus forecast, supported by seasoned expertise.
In the executive opinion method, senior executives use their experience and insights to predict future sales. Executives often have a macro perspective on the industry and company strategy, allowing them to make informed judgments about future trends.
This approach can be effective for high-level planning, particularly in industries where market dynamics are influenced by regulatory, political, or economic factors. Although subjective, executive opinion can also bring a highly valuable organizational perspective during times when quantitative data is scarce or unreliable.
Quantitative forecasting methods rely on statistical and mathematical models, using historical data to identify patterns and trends. These techniques are highly accurate when there is ample historical data, making them suitable for industries where products and demand patterns are stable over time.
Below are some commonly used quantitative sales forecasting methods:
Opportunity stage forecasting allows you to predict the likelihood of a deal closing based on where the prospect is in the sales pipeline. It is easy to implement and offers a better understanding of the sales process by giving essential information about how likely they are to make a sale at different steps along the way. This helps them make smart choices to improve their sales.
For example: Imagine a company has three active sales opportunities in different pipeline stages:
Opportunity 1: In the Qualification stage with a potential value of $1,000.
Opportunity 2: In the Proposal stage with a potential value of $2,000.
Opportunity 3: In the Negotiation stage with a potential value of $1,500.
And the probabilities of closing each stage are Qualification (30%), Proposal (50%), and Negotiation (70%).
Here’s how the forecast can be calculated:
The simple moving average method calculates the average sales over a specific period (e.g., three months or a year) to predict future demand. Each time period is treated equally, providing a straightforward way to estimate average demand without emphasizing any particular season.
For example, a business may calculate the SMA for the past twelve months to forecast sales for the next month. SMA is ideal for stable industries where demand remains relatively constant and is less affected by seasonal fluctuations.
This forecasting method is straightforward; it helps you predict exactly when a deal is likely to close based on your sales cycle’s length. You don’t need to rely on the rep’s feedback or gut feeling to predict revenue outcomes. You must carefully track how and when leads enter your sales pipelines to get the most accurate forecasts.
To calculate, 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.
The weighted moving average method assigns different weights to time periods, giving more importance to certain periods based on expected seasonal demand. This method is beneficial for industries with clear seasonal patterns, like retail or food and beverage, where demand may peak during certain months.
For example, a company may assign higher weights to months leading up to the holiday season, ensuring that inventory levels match the expected surge in sales. This method helps avoid overproduction or underproduction during key selling periods.
Exponential smoothing is a sophisticated approach that applies decreasing weights to past data as it moves further from the present.
This forecasting method adjusts the forecast based on recent observations, making it highly responsive to sudden changes in demand trends, and is effective in industries where demand patterns are prone to short-term fluctuations.
Linear regression is commonly used when companies want to analyze how specific factors influence sales, like marketing spend or product pricing.
For example, a company may use linear regression to determine if there is a direct relationship between advertising expenses and sales growth. Linear regression models can provide actionable insights for budgeting and strategic planning by quantifying how much specific factors impact demand.
To conduct this analysis, sales teams must have a clear understanding of the internal and external factors affecting their sales and determine the reasons for forecasting, factors affecting sales (dependent variable), areas that are being affected (independent variable), and review time period.
Once you have collected the information, collect the data for both dependent and independent variables, choose a regression model and run it, and look for correlation between variables.
Choosing the best forecasting technique involves balancing complexity, accuracy, and the business environment. Here’s a guide to help you select the suitable method:
Analyze data trends and consistency. Quantitative techniques, such as regression or time series analysis, excel when historical data is extensive. In contrast, qualitative methods, like expert panels, work well for new products lacking past data.
The timeframe of your forecast influences the choice of method. Short-term needs, like inventory management, benefit from simpler approaches like moving averages, while long-term planning may require complex models to capture broader trends and market shifts.
Forecasting should match your team’s capacity. Smaller teams may find simpler models or software solutions more manageable, while sophisticated techniques may demand more resources and advanced analytical skills.
Stable industries can often rely on quantitative methods like exponential smoothing, which incorporates recent trends. However, qualitative approaches in volatile industries can offer flexibility to adjust to rapid changes.
Align your method with business priorities. For seasonal patterns, weighted moving averages capture fluctuations effectively, while for highly variable demand, a combination of methods might provide a more comprehensive view.
The need today is not for better forecasting methods but for better application of the techniques at hand.
Understanding the basic features and limitations of the above-listed sales forecasting methods can help you properly frame the forecasting problem and use them more effectively. Over time, you can experiment with different approaches to determine which method works best.
Intrinsically, the lack of a clear, organized, and transparent process can hinder accurate forecasting, letting crucial data slip through the cracks. It is, hence, crucial that you automate as much data as possible to let your team focus on important tasks, ensuring leaders have a clear view of the sales pipeline for confident forecasting.
Forecast with confidence by configuring Sybill with your CRM for instant answers and real-time updates on deal progress, risks, and opportunities after every meeting. Click here to catch Sybill in action.
