Top 8 Sales Forecasting Methods + How to Pick One 2025

At its core, historical forecasting involves analyzing past sales performance to estimate future outcomes. However, it may not be ideal for businesses experiencing rapid changes in their sales process, market dynamics, or customer behavior, as it assumes past trends will continue into the future. But what it also means is that well-organized and accurate data is an absolute must in this case and acquiring and organizing that data comes with a cost.

What is a sales forecast’s purpose?

Now, methods to forecast sales vary depending on factors such as the data being used, the time frame of the forecast, and the specific business objectives. While some techniques focus on historical sales, others take into account market trends and sales pipeline stages to calculate forecasts. In this guide, we’ll walk you through 10 proven sales forecasting methods, breaking down how each one works, its advantages and limitations, and real-world examples with numbers. By the end, you’ll know which sales forecasting method best fits your sales model and how to use it to build reliable revenue forecasts.

Lead-driven Forecasting

• A repeatable process ensures that sales forecasts are built using the same criteria and forecasting methods every time.
• This approach can provide a high level of precision but also requires significant expertise in statistical analysis and data handling.
• The pros in your sales organization will know from experience more or less what they can expect and apply it to the sales forecasting process.
• To get the most from a weighted model, revisit probabilities every quarter at a minimum, slice them by deal size, and check for outliers.
• This approach is essentially a timing-based forecast, helping sales leaders anticipate future revenue by monitoring deal “age” relative to benchmarks from historical sales data.

The best sales teams blend multiple techniques to get a clear, realistic picture of what’s coming down the pipeline. Whether you’re building out your first forecast or refining a mature revenue process, choosing the right method makes all the difference. Adjusting your forecasting assumptions based on new information, such as shifts in the market or changes within your business, is helpful. For example, businesses with long or complex sales cycles are more likely to revisit their model on a quarterly basis, while businesses with a short or more transactional sales cycle on a weekly basis. The technique required for a forecast that will be used to make decisions around production and inventory, for example, will need to be quite sophisticated to reap reliable, highly accurate results.

Product Teams

Missed revenue targets can highlight deficiencies in forecasting processes, historical sales data, tools, or team alignment. This not only casts doubt on leadership effectiveness but also negatively impacts team morale and sales performance. In summary, sales forecasting is a dynamic function that evolves with a company’s growth.

Furthermore, research by the Aberdeen Group found that companies with accurate sales forecasts saw a 13.4% increase in their year-over-year growth compared to companies with inaccurate estimates. Utilizing historical data is a helpful recommendation for building many parts of the sales process. It takes past performance data and then estimates sales and growth for the next revenue period. By choosing the right forecasting method, businesses can gain a clearer picture of their future sales performance. Now, before we go ahead and discuss the methods of sales forecasting, why exactly is it essential to create sales forecasts?

• Ensuring data accuracy should be a top priority for any company aiming to improve forecast accuracy.
• In this blog post, we’ll guide you through various sales forecasting methods and explain how to create reliable forecasts.
• Let’s explore the most common reasons behind failures in sales forecasting and how to overcome them using appropriate sales forecasting methods and tools.
• However, without these support tools, verifying intuitive forecasts is impractical on a large scale.
• You also won’t be able to use it in new businesses, since there’s no past data to use yet.
• You can also use forecasts to show whether your business needs to expand its sales staff or temporarily bring in cover for busy seasonal periods.

When the economy is in a slump, people/businesses lose money and are less likely to buy, whereas people are more likely to invest and buy Liability Accounts when the economy is booming. Assume you had $300,000 in revenue last month and that your sales revenue has risen at a rate of 12% per month over the previous year. Demo may close at a 40% possibility while Offer has a 70% likelihood of closing.

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Smaller sales organizations and teams that have closed fewer deals in more traditional industries may use this model. As you can imagine, it’s the least accurate forecasting model because it relies heavily on human opinion. It’s how is sales tax calculated strongly recommended that you take the results of a gut-feel forecast with a grain of salt because trying to predict swing deals and slippage using this model can end up being inaccurate. On the flip side, if your sales team looks like it will miss its targets, you can take proactive action. When business is expected to be slow, you can use your forecasts to justify your lead generation budget increase. You can also focus more of your attention on existing accounts to cover the lack of new business.

Sales forecasting focuses on predicting future sales revenue by organizations usually use only one method for forecasting sales. analyzing historical sales data, market trends, and the sales pipeline. This process is best suited for short-term predictions, such as monthly or quarterly sales projections. Accurate forecasts help businesses allocate resources, improve sales forecasting accuracy, and adapt quickly to real-time changes in the market. There is no single “best” model for sales forecasting – the right choice depends on your sales cycle, data maturity, and business model.

• Regular pipeline reviews combined with this forecasting technique can highlight gaps early and help keep your revenue goals realistic.
• But today, too many organizations rely on imperfect data, opinion, and gut feel to generate forecasts.
• By examining historical consumption data, businesses can make an informed sales projection and optimize inventory management to meet customer needs efficiently.
• Account-based forecasting takes a broader view by forecasting revenue at the account level rather than individual deals.
• The key is to find a sales forecasting method that works for your team while maintaining the highest level of accuracy possible within your sales process.
• By monitoring sales pipeline health, sales team performance, and market trends, you can identify risks before they impact sales revenue or lead to missed sales quotas.

This forecast becomes a smoothed version of a time series, eliminating fluctuations, noise, outliers and random variations from the data. Modern machine-learning algorithms are trained to objectively analyze historical data and assign values based on weights and biases. Both machine learning and AI pinpoint the trends in data that humans are likely to miss. You might also dig into the individual performances of your sales team to show who’s performing best and with what accounts they perform best with.