Businesses generate revenue that fuels future operations, but how do we plan for future operations without knowing future revenue?
That is one of the simplest reasons sales forecasting is so important. It helps get an idea of future sales performance. Diving deeper into the reasons for its existence, we find that predicting the future is extremely important to know what actions to take today so that the future is one you desire.
Paul Saffo, a technology forecasting, says-
“Forecasting is valuable to businesses because it not only predicts the future, but it gives you the ability to make informed business decisions and develop data-driven strategies that can impact your business today”
This statement accurately captures the reason why sales forecasting is done, albeit there are many.
This guide will explore sales forecasting, the factors that affect forecasts, and the steps to create a solid forecast for your business.
Let’s dive right in!
The Meaning of Sales Forecasting
Sales forecasting is the process of estimating future revenue for a period (next month, next quarter, next year, etc.) by using historical data and current business conditions (represented through key metrics). It also accounts for external changes like demand patterns or industry trends.
A reliable sales forecast acts as a guide for the entire company to make financial and other decisions that lead to a favorable future. Whether it be budgeting, setting future sales targets, or resource allocation needs, forecasting is the beacon that paves the path forward.
According to research by the Aberdeen Group, companies with accurate sales forecasts enjoy a 7.3% jump in quota achievement and a 10% revenue boost compared to companies with inaccurate forecasts.
Here is the data that goes into and affects the creation of a sales forecast:
Internal Factors
Historical Sales Data: This is perhaps the biggest signal in periods of sustained normal growth. In the absence of large external or internal changes, historical sales performance can give you a clear idea of what sales figures can be expected in the future. However, even while modeling a forecast with other factors, historical sales figures remain important as lagging indicators. This data can be a sum total or individual for each product, geography, etc.
Current Pipeline Value: Current sales pipeline value refers to the deals already being worked upon, including their potential value and likelihood of closure. If the pipeline is larger than usual, then future sales can also be expected to be higher than usual, given steady conversion rates.
Marketing Efforts: Your past marketing efforts are impacting current sales, and current marketing efforts will affect future sales. Understanding the relation between the size of a marketing campaign and its impact on sales clearly lets you adjust expectations for similarly sized campaigns in the future.
External Factors
Economic Indicators: Economic trends, such as high inflation or economic crises, can affect consumer behavior a lot, leading to a drop in sales during economic contractions and recessions while boosting sales in economic expansions and updrafts.
Seasonal Trends: Seasonal changes are a big deal if you want accurate forecasts. For example, a company selling candies can expect huge sales around Christmas. A more traditional and regular example would be companies selling financial software (Accounting, Invoice, etc.) and having high sales near the end of the year (or financial year), as that is the time when systems are installed or replaced.
Industry Trends: New governmental changes for your industry, competitor activity, technological jump, etc., can all affect future sales prospects, affecting forecasts. While some of these may be unexpected, some can be countered with adequate contingency plans and preparation.
While these are some of the major data points used to make sales forecasts, there are many other data sets that can be used to form such forecasts, depending on the techniques used.
Why is Sales Forecasting Done?
Knowing how much revenue you're going to make in the future is extremely important for correcting business strategy in the present and planning your strategy for the future.
Here are the main reasons why sales forecasts are needed:
Resource Allocation: Estimating future revenue is quintessential in budgeting for the future, after all, your marketing budget, production budget, etc. all depend on how much you’ll be able to make. Hence, sales forecasting is an important financial planning tool. Plus, knowing future demand helps you handle surges or drops in demand by being ready for it.
Future Strategy: Framing your future business strategy requires you to know what kind of sales figures you are looking at. Consequently, you can frame the actions you need to take, such as marketing spend, prospecting, etc., to arrive at the expected sales figure. Thus, accurate sales forecasting helps business leadership to make informed decisions regarding the future.
Investor Relations: Investors are always interested in the future growth prospects of a business. Hence, whenever you plan to raise funds from investors, you have to present a solid forecast of predicted future growth.
Goal Setting: Sales forecasts can be boiled down to effective, challenging, and achievable sales targets for the sales team. It helps give shape to the future sales strategy required to hit your forecasts.
Best Practices for Sales Forecasting
Before jumping into creating forecasts, companies should ensure some forecast hygiene- steps they should take to ensure maximum forecast accuracy. Here are some of the best practices to keep in mind for sales forecasting:
Organized CRM: An organized CRM (Customer Relationship Management) tool is the first priority a sales team should have to function efficiently, even outside of forecasting needs. Organized CRM data helps build forecasts easily and ensure data accuracy, with some CRMs having built-in forecasting and data analysis tools too. Complete visibility into the pipeline is important for sales forecasting, and a CRM is necessary for achieving that.
Structured Sales Process: For reliable forecasting, your organization should have a clear and well-established sales process with repeatable sales structures and steps. If your sales process is clearly structured and common steps are followed for each customer, then there is a higher probability of making sense of future deals and when they will close by analyzing past data.
Clear Sales Management and Targets: Realistic sales quota setting and efficient sales management are important to realize the sales goals you have in your forecast. Sales compensation management, quota management, etc. should be clear and effective to achieve desired growth. Pipeline management should be clean.
Cross-Functional Collaboration: Cross-functional collaboration and bringing in diverse perspectives when creating a sales forecast ensures correct interpretation of market trends, reduction in biases, understanding of organization frameworks, and an appropriate understanding of consumer behavior from all perspectives. Hence, marketing, finance, and other non-sales departments also have a role to play in sales forecasts.
Improving Over Time: Forecasting is not a one-and-done process. Sales forecasts have to be optimized and refreshed according to changing market conditions, internal variables, etc. The best results can be expected when forecasting is treated as a ‘live’ process that keeps incorporating changing variables to project a true and fair view of the future.
7 Sales Forecasting Techniques
The best sales forecasting technique for your business depends on the kind of data you have and the resources you have available to you. Methods such as regression analysis and multivariable forecasting require well-maintained accurate and robust datasets, along with dedicated resources with statistical proficiency. On the other hand, historical analysis is better for companies with limited data, but a stable industrial environment.
Here are the 7 major sales forecasting methods:
Historical Forecasting: Uses past sales data to predict future performance. By analyzing trends, seasonality, and growth patterns from previous periods, businesses can project future sales. This method works best in stable markets where past performance reliably indicates future results. Limitations include its inability to account for new market entrants, economic shifts, or product innovations.
Opportunity Stage Forecasting: Bases predictions on the current stage of each sales opportunity in the pipeline. By assigning probability percentages to each stage (e.g., qualified lead: 20%, proposal: 60%), companies can calculate potential revenue. This method requires accurate stage definitions and regular pipeline updates to maintain reliability.
Length of Sales Cycle Forecasting: Predicts when deals will close based on the average time deals spend in each sales cycle stage. By analyzing historical cycle lengths, companies can forecast when current opportunities might convert. Particularly useful for businesses with consistent, predictable sales cycles but less accurate for complex B2B sales with variable timelines.
Lead-Driven Forecasting: Projects future sales based on lead quantity and quality metrics. By tracking conversion rates through the funnel (leads to MQLs to SQLs to customers), companies can predict future revenues. Requires robust lead scoring and consistent lead generation to be effective.
Multivariable Analysis Forecasting: Incorporates multiple variables (market conditions, competitive landscape, pricing changes, marketing spending) to create comprehensive predictions. This sophisticated approach considers how different factors interact and influence outcomes. Requires significant data analysis capabilities but offers more nuanced forecasts than single-variable methods.
Intuitive Forecasting: Relies on sales reps' and managers' judgment based on their market knowledge and customer relationships. While subjective, experienced professionals often have valuable insights about the likelihood that data alone may miss. Best used to complement data-driven methods rather than as a standalone approach.
Regression Analysis: Uses statistical modeling to identify relationships between sales outcomes and various independent variables. By determining which factors most strongly correlate with sales performance, companies can build predictive models. This method requires statistical expertise but can reveal non-obvious influencers of sales success.
How to Do a Sales Forecast for Your Business
Below steps outline how businesses can start their forecasting function. A good tip in forecasting is to start simple and keep adding complexity as you move forward and confidence. That said, here are the steps you can take to build a robust sales forecast for your business:
Step 1:Determine Your Forecast Period and Goals
Define the timeframe (monthly, quarterly, annual) and how far you want to forecast the future. The farther you go, the less reliable forecasts become since a lot more variables become unknown.
Consider your business cycle and industry seasonality- Factor in seasonal drops and booms, etc.
Step 2: Gather Historical Data
Collect past sales data (at least 2-3 years if available).
Include revenue figures, unit sales, customer counts, sales cycle length, deals in pipeline, etc.
Note any anomalies or special circumstances that affected past performance
Step 3: Analyze Market Conditions and Trends
Research industry growth projections, competitor growth, etc.
Identify market shifts affecting your business, marketing efforts that could affect sales, consumer behavior patterns, etc.
Consider economic factors (inflation, interest rates, etc.
Step 4: Select Appropriate Forecasting Method(s)
Choose based on your business needs:
Historical sales analysis for stable markets
Opportunity stage forecasting for pipeline visibility
Lead-driven for marketing-heavy businesses
Regression analysis for complex scenarios
To know which methods work for you, consider creating a forecast for the current period using historical data, and match current sales figures with the figures in the forecast. The method that gives the closest results might be the one you need.
Step 5: Create Your Baseline Forecast
Apply your chosen method to existing data to create a rudimentary forecast.
Generate initial projections based on historical patterns, pipeline value, etc.
Start segmenting by product line, region, or customer type if relevant
Step 6: Adjust for Known Variables
This is where you start adding complexities and layers of data to adjust your forecasted figure.
Account for planned marketing campaigns
Factor in new product launches
Consider sales team changes or territory expansions
Incorporate pricing adjustments
Step 7: Test Multiple Scenarios
Create best-case, worst-case, and most-likely scenarios
Adjust variables for each scenario
Calculate confidence intervals for your predictions
Step 8: Validate with Stakeholders
Share forecasts with the sales team for inputs, considering their opinions on their quotas, market conditions, consumer behavior, pipeline value, etc.
Consult with marketing, operations, and finance departments to get comprehensive cross-functional inputs.
Get leadership approval or feedback
Step 9: Implement, Review, and Refine Regularly
Implement and track vs. actual performance.
Document variance and identify causes.
Update projections based on current performance
Refine your methodology based on what you learn
Common Pitfalls in Sales Forecasting and How to Avoid Them
Predicting the future, no matter how much data you have, can always be a hit or miss. Here are some common pitfalls faced by sales forecasting teams, and what steps to take to maintain diligence in forecasting:
Over-Reliance on Historical Data
Pitfall: Assuming past performance perfectly predicts future results. Solution: Combine historical analysis with forward-looking indicators like market trends, competitive intelligence, and macroeconomic factors. Regularly review and adjust assumptions based on changing conditions.
Pipeline Optimism
Pitfall: Sales teams often overestimate close probabilities and timelines. Solution: Implement objective qualification criteria, track historical win rates by stage, and apply probability adjustments based on actual performance. Consider having managers review forecasts for reality checks.
Ignoring Seasonality
Pitfall: Missing predictable fluctuations in buying patterns. Solution: Analyze multi-year data to identify seasonal patterns. Create normalized forecasts that account for typical high and low periods. Develop separate models for different seasons if necessary.
Neglecting the Sales Cycle Length
Pitfall: Failing to factor in how long deals actually take to close. Solution: Track and measure your average sales cycle by product, segment, and customer type. Use this data to create more realistic timelines in your forecast, especially for complex B2B sales.
Inconsistent Data Quality
Pitfall: Working with incomplete, outdated, or inaccurate CRM data. Solution: Establish clear data entry standards, implement regular data cleaning processes, and make CRM updates part of sales team evaluations. Consider automation tools to improve data quality.
Single Method Dependence
Pitfall: Relying on just one forecasting approach. Solution: Use multiple forecasting methodologies and compare results. When several methods point to similar outcomes, your confidence can increase. When they diverge, investigate why.
The Role of AI in Sales Forecasting in 2025
AI algorithms are great at building predictive models by analyzing large amounts of data and other key metrics of any business. They are far more meticulous than the human eye, and are automated, meaning that sales forecasting models using AI can keep producing and refining forecasts without any human input needed.
AI is able to extrapolate complicated details, identify correlations, patterns, etc. to arrive at meaningful conclusions about the future. Integration of an AI model with the CRM (Customer Relationship Management) platform feeds it with large amounts of data, much more than can be used manually in a forecast, making these forecasts powered by AI more accurate, holistic, and dependable.
Now that AI has evolved to consistently meet business expectations, including autonomous agents and conversational AI bots, predictive analytics is also taking a leap forward with tools capable of high-level forecasting, not only for sales but also for other areas such as revenue intelligence, financial operations, etc.
Conclusion
Sales forecasting remains a difficult endeavor for most businesses. According to Intangent, 80% of sales organizations have forecast accuracy of less than 75%. In the face of countless decisions to be made effectively, this abysmal forecast accuracy fails to fulfill the fundamental goals of sales forecasting, which is empowering present decision-making
To get better at forecasting, companies must first learn to manage their sales pipeline effectively and oversee deal stages. A structured sales process gives way to more efficient sales forecasts. Obviously, with the introduction of AI, forecasting is becoming more and more accurate.
FAQs
What is sales forecasting?
Sales forecasting is the process of predicting future sales revenue by estimating how much of a product or service a company expects to sell in upcoming periods. It combines historical data, market trends, economic indicators, and business intelligence to make educated predictions that help with budgeting, resource allocation, and strategic planning.
What are the four major sales forecasting techniques?
The four major sales forecasting techniques are qualitative forecasting (based on expert opinions and market research), time series analysis (using historical data patterns), causal forecasting (analyzing relationships between variables), and AI/ML forecasting (utilizing advanced algorithms to process multiple data points and identify complex patterns).
What is an example of a sales forecast?
A retail store might forecast that based on last year's 4th quarter sales of $100,000, plus a 15% observed growth rate and planned marketing campaigns, they expect $115,000 in sales for the upcoming holiday season. This includes weekly projections broken down by product categories and channels.
How do you calculate sales forecast?
Calculate sales forecast by multiplying the number of expected customers by the average purchase value, adjusting for seasonality and trends. Factor in historical performance, pipeline data, conversion rates, and market conditions. Common formula: Previous Period Sales × (1 + Growth Rate) × Seasonal Factors.
Why do some businesses fail to forecast sales?
Businesses often fail at forecasting due to poor data quality, inconsistent tracking methods, overreliance on emotions instead of hard data, failure to account for market changes, and lack of proper tools or expertise. Many also struggle with siloed information and insufficient historical data analysis.
Which model is best for sales forecasting?
The best forecasting model depends on your business type, data availability, and needs. For established businesses with stable patterns, time series models work well. For newer businesses or volatile markets, qualitative methods combined with simple quantitative models often prove the most effective.
Who is responsible for sales forecasting?
Sales forecasting typically involves multiple stakeholders: sales managers create bottom-up forecasts from their teams' pipelines, financial analysts provide top-down projections, and executives validate and adjust final numbers. Sales operations teams often coordinate the process and maintain forecasting systems.
What is the difference between a sales goal and a sales forecast?
A sales forecast is a realistic prediction of expected sales based on data, market conditions, and historical performance. A sales goal is a target that represents what the company wants to achieve, often set higher than forecasts to motivate teams and drive growth.
What is the golden rule of forecasting?
The golden rule of forecasting is to always be conservative and expect the lower end of possible values as true. This is done so that you can prepare yourself for the worst outcomes.
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