A Beginner’s Guide to Sales Forecasting Methods

Sales leaders often need help accurately forecasting sales outcomes, especially as market conditions and product portfolios change. In fact, 40% of chief sales officers (CSOs) surveyed in Gartner’s 2022 CSO Priorities Survey ranked accurate and actionable forecasting among the top three internal challenges in 2022. [1]

If you’re a new sales leader or manager establishing a forecasting process for your team, this guide will help you prepare for upcoming quarters and create a more accurate forecast for stakeholders. Even better? Our recommendations are backed by insights from Gartner research, [1-3] so you can feel confident implementing them into your forecasting process.

Sales forecasting is a process that enables sales organizations to estimate how much sales revenue they can expect to close in future periods. There are several reasons to implement a sales forecasting strategy:

• It allows for more accurate budgeting. Sales forecasting aids in understanding how much revenue you’re likely to generate in the coming months, quarters, or years.

• It enables data-driven decisions. A sales forecast can help your marketing team decide whether or not to focus on a new target audience or launch a new product or service.

• It shows where to allocate resources. For example, a forecasted increase in demand can help determine how many seasonal hires to bring on before the holidays.

• It aids in goal setting. Being able to see your expected future revenue allows you to adjust your sales target as needed and set more realistic goals for your sales team. 

There are several different approaches to sales forecasting. The one you choose will depend on the data you have available and what you’re looking to measure. The table below lists five common sales forecasting methods:

Ensure your sales team doesn’t make these common mistakes when forecasting sales:

• Not maintaining accurate sales data: Even the most advanced machine learning-based forecasting will be ineffective if the sales data it relies on isn’t consistent and accurate. As a sales leader, you can hold your sellers accountable for forecast accuracy by having them track metrics such as conversion rate, initial pipeline value, and slippage rate. [2]

• Relying too much on qualitative data: Qualitative data, or data that relies on opinions as in intuitive forecasting, can lead to biased or overly optimistic projections. Make sure to pair qualitative forecasting techniques with quantitative data for the most accurate sales forecast.

• Overlooking qualitative data entirely: Numbers can’t explain everything, and qualitative data is useful for filling in the gaps. Customer surveys and focus groups, for example, can give insights into the popularity of particular products or services or explain unpredicted increases in sales.

• Not cleaning your data: Accurate sales forecasting requires clean data. Data cleaning, cleansing, or scrubbing is the process of checking your business data for inaccuracies, duplicates, and outdated or incomplete entries. It’s tedious, but that’s why there’s software for it.

Gartner recommends the following actions if your organization is looking to design or redesign a sales forecasting process: [3]

• Document what metrics are currently used as leading indicators for sales outcomes. Sales effectiveness, customer metrics, and product pricing indicators are good KPIs to track if you aren’t already doing so.

• Identify which roles will oversee your sales forecasting process. Sales managers and analysts are typically the ones responsible for sales forecasting.

• Discuss the benefits of sales forecasting with leadership, and demonstrate how a new or revamped strategy would boost your sales process. Get support from leadership to launch your sales forecasting strategy.

Having the right tech tools is key to building your sales forecasting strategy. Here are a few you should consider adopting if they’re not already part of your tech stack:

• Sales forecasting software: Sales forecasting software tools can help your company plan, budget, and forecast expenses and revenue with advanced modeling and analysis tools.

• Customer relationship management (CRM) software: Your CRM system’s sales forecasting feature can help make objective, informed decisions about which accounts to pursue by using historical and current numbers as well as patterns in sales activity.

• Sales force automation (SFA) software: These tools help manage and automate sales processes, including contact management, customer relationship management, and sales activity tracking. SFA systems may also provide insights for pipeline management, opportunity management, and forecasting.

Whichever tool you choose, ensure your sales team is diligent about entering sales data. Research shows that both managers and sellers document deal progress in CRM and SFA systems inconsistently, [1] which ultimately reduces the quality of sales pipeline data, increasing the likelihood of errors in sales performance forecasting.