5 Top Forecasting Models to Improve Sales Accuracy

• The right forecasting model helps teams predict revenue more accurately and act with confidence.
• Different models serve different needs, from simple time-series forecasts to complex AI-powered predictions.
• AI is rapidly improving forecast accuracy by analyzing massive datasets and spotting trends humans might miss.

Sales forecasting is an art and a science. It combines hard data with subjective expert insight to help your business run smoothly. Forecast accuracy is important, and sales leaders are always looking to improve it. In fact, according to the State of Sales report, 98% of sales leaders say forecasting accuracy will be improved by AI.

That’s why businesses need to choose the right forecasting model for their sales operations. Not every model lends itself to accuracy every single time for every type of business. In this guide, we’ll go through different types of forecast models, how to apply the right one, and explore how sales AI is changing forecasting for the better.

A forecasting model is a statistical tool designed to help businesses predict future outcomes, like revenue, bookings, usage, and product demand, based on the likelihood of closing deals and when that money will be received. Sales forecasting starts with the end in mind: Businesses decide on a goal, then define and measure key metrics to gauge whether they’re on track.

Any forecast model has at least three common triangulation principles to consider:

1. Time. Your sales cycle horizon is represented by accurate close dates.

2. Customer buying readiness: This is the sales stage, and shows where you are in the process.

3. Probability: How you meet or miss the customer's need to be selected.

You can run the most basic forecast without historicals if you have reliable data with these principles.

Sales forecasts are data-driven estimates of revenue generated over a specific period, with models relying on historical data patterns to project what is likely to occur in upcoming periods. Key factors include past performance, seasonality, customer behavior, and market conditions. Organizations use forecasts to improve sales planning, anticipate revenue, manage resources, and guide decision-making.

Choosing the right forecasting model for your sales forecast will help improve accuracy, strengthen accountability, and increase predictability across your sales organization. For example, accurate sales forecasts highlight which deals are most likely to close, so your team can focus on opportunities with a higher likelihood to convert.

These are some of the top benefits of using a sales forecasting model:

• Easier to predict revenue: The right forecasting model can help businesses anticipate revenue and budget effectively.
• Be proactive, not reactive: Identify pipeline gaps earlier and adjust your approach based on the deal type.
• Better decision-making: Accurate forecasting improves resource allocation, hiring plans, and inventory or production decisions.
• Improve predictability: Build trust across teams by providing predictable, data-backed insights — which is highly important in times when the market is stagnant or volatile.
• Better performance insights: Consistent sales forecasting supports more accurate quota setting and performance measurement in the long term.

With methodical sales forecasting, sales teams can proactively allocate resources, win more deals, and help leadership make smarter decisions.

Many different forecasting models are used in sales, and each comes with its own strengths and trade-offs. Some models excel in data-rich environments and offer strong statistical accuracy while others are more flexible or better suited for teams with limited historical data.

Sales teams also choose different models (or different combinations of models) depending on their needs. For example, a business selling software subscriptions at $15/month per user will need different forecasts than a company with enterprise clients spending $300,000/year.

To choose the right forecasting methods for your business, consider your business model, what data you have available, and the size of your sales pipeline.

Here’s a look at the five primary forecasting models used in sales.

Quantitative models use historical numerical data to project future trends. They’re useful for predicting demand, sales, and resource allocation for the short-to-intermediate term.

An example of a common quantitative model used to forecast sales is time-series analysis, which uses past sales data to identify trends, patterns, and signals. Plotted out in a dashboard, time-series analysis can alert sales teams to things like spikes and drops in seasonality. Another example of a quantitative model is regression analysis, which models outcomes like sales and revenue with different variables, like the cost of ad spend.

Qualitative models are more subjective, relying on expert judgment where historical data is limited, like entering a new market or launching a new product. Data can be sourced from anonymous surveys and research, and predictions are usually better for the short term.

One example of a qualitative model used in sales forecasting is the Delphi method, in which selected experts undergo different rounds of surveys, with the goal of achieving a consensus. Proponents of the Delphi method believe it reduces bias and groupthink, thanks to the use of anonymized responses in the surveys. Incorporating multiple stakeholders and different expert insights, especially when historical data is scarce, can result in accurate predictions.

A bookings forecast estimates total contract values expected to close over a period, helping to predict future revenue. Bookings forecasting models can assign a weight to each opportunity in the pipeline based on funnel stage, time-series analysis through examining patterns in historical data, or using AI algorithms to identify patterns in large sales datasets.

For example, a company might need $5 million in bookings to meet its goals, but might only have $2 million in the pipeline. To hit the target, the company needs to line up another $3 million. Factoring in a win rate of 7%, leadership calculates the sales team needs $42.8 million in the pipeline. The company can adjust its strategies to raise bookings, work on improving the win rate, and focus on other strategies to gain revenue. AI can help by surfacing deals with stronger buyer engagement or by using historical patterns to predict deals more likely to convert.

This type of forecast aggregates sales projections by geographic or organizational segments, giving sales teams a snapshot of how expected sales compare between territories.

Not every region is the same — different areas have different customer types, seasonality, economies, and other factors. Each territory is examined with different factors in mind, and the forecasts roll back up to leadership to give a high-level picture of expected sales performance in different markets. Looking at a territory-based forecast helps leadership make decisions on how to allocate resources.

Also called usage-based forecasting, this mode is used to track the usage of a product over a set period of time, rather than just looking at revenue drawn from a single sale. This is especially common in the realm of tech and software, where consumption models are often preferred over flat rates or fixed subscriptions.

Customers might pay based on usage, volume, API calls, credits, or other measurements. Because revenue depends on how much customers consume, it's important to predict how much they might use in the future to accurately predict revenue. Consumption-based forecasts must take into account fluctuations in usage and seasonality, which can make it difficult to set accurate budgets and realistic revenue expectations. Despite that, some AI tools work well at usage and consumption-based forecasting because they can interpret irregularities in large swaths of data.

Every business is different, and no forecast model is one-size-fits-all. It’s important to consider different factors, like business size and age, when deciding on the right forecast model. Here are some examples.

• Small business, short-term sales forecast: Imagine a small business that sells small-batch, fresh-roasted beans to local coffee shops. A simple time-series model, which looks at historical data or bookings forecasts based on CRM data, can help leadership estimate near-term revenue with accuracy.
• Large corporations predicting macro trends: A larger corporation, like a sports equipment company with a national footprint, would need to apply more complex, long-term trend analysis or regression-based models with broad data inputs. To do this accurately, they would need to rely on clean, unified data to make more complex forecasts.
• E-commerce startup with highly seasonal demand: Seasonality is a huge factor in forecasting. Picture an e-commerce startup that sells viral designer swimsuits and flip-flops. They should use seasonality-adjusted historical data models to capture peaks and dips, like when people go on summer vacation or when business slows down in the wintertime.
• Usage-based business: A large SaaS company might draw revenue from usage rather than flat-rate sales. To accurately forecast, they would need to apply a consumption model to anticipate revenue based on how much customers actually use their product.

What’s important for businesses both small and large is choosing the right tools to help them accurately create sales forecasts. They should look for flexible forecasting tools, which allow businesses of any size to configure forecasts to their needs by defining forecast types, setting up role-based or territory hierarchies, mapping forecast categories, and customizing views for specific selling scenarios.

The right forecasting model for you is the one that aligns with your company size, business objectives, sales models, and the structure of your data — and delivers the right output for accurate decision-making. Here are some key considerations:

• Do you have strong data availability? If so, use quantitative models based on CRM and historical data. If not, use qualitative models based on expert insight.
• What is your time horizon? This is the timeframe of your sales forecast. Short-term forecasts, like up to a year, may use simple, high-frequency data. Long-term strategies may require advanced models and software. Sales cycle length also impacts how you adjust forecasts.
• How close is “close enough” with accuracy? Accurate forecasts are important for making the right decisions about budget and spending, but some businesses can afford a little bit of deviation. The more precision needed, the more robust and data-heavy the model should be.
• What resources are available? Consider whether your team has the tools, CRM visibility, and sales analytics capability to maintain the model.
• What's your organizational structure? Your organizational structure can influence your forecasting model by determining who owns the data, how teams collaborate, and how centralized or decentralized decision-making needs to be. Choose a forecasting model that aligns with your sales org hierarchy, product lines, or territories.

With the right forecast model in hand, your team will be able to accurately predict revenue and make changes when needed. Tools are increasingly using AI to improve forecast accuracy. Let's look at why and how sales AI makes better forecasting possible.

According to the State of Sales report, sales forecasting accuracy is a top 5 use case for how AI can improve sales. Before AI, sales forecasting could be accomplished by manually scouring datasets. With the advent of CRM software, this became easier, as accurately updated sales data could populate real-time dashboards.

Now, agentic AI further enhances sales forecasting by processing data, finding patterns, and pinpointing effective solutions even across complex datasets. Because AI can analyze massive datasets, it can identify complex, nonlinear patterns that traditional forecasting methods might miss. With machine learning, AI forecasting continuously improves as new data is added to the system. AI CRM systems and forecasting tools like AI sales agents can quickly create accurate forecasts based on multiple factors.

AI sales forecasting systems are becoming increasingly accurate with predictive analytics. These tools monitor customer data and deliver insights and recommendations to sellers about the hottest leads, the deals at risk, and what you need to do to lock down the sale. Generative AI can summarize insights from sales calls, create an action plan, and get you started with drafting prospect communications. Meanwhile, autonomous AI agents can help sales development representatives efficiently move a high volume of leads through the pipeline.

The tech doesn't replace humans. Instead, it greatly expands the information sales teams have to make well-informed decisions. It also gives them more time to spend on aspects of selling that will always be personal and intuitive, like relationship-building, understanding social and emotional context, and creative problem-solving.

No well-run operation makes decisions in a vacuum. That's why businesses rely on proven methods and tools — like sales forecasting — to help make better, data-informed choices. Forecasting helps businesses of all sizes accurately predict revenue, quota targets, and resource allocation. Choosing the right forecasting model for your sales team means considering what's important to your business.

An agentic AI solution like Agentforce can help. AI-driven forecasting will continue to expand capabilities, improve accuracy, and support more confident decision-making across the business.