What Is a Sales Forecast — and How Can a Sales Team Create One?

When preparing for your day, you probably check the weather forecast. Sun, rain, and snow all call for different gear, routes, and possibly even plans. The same is true for a sales forecast.

Sales forecasting predicts a company’s expected revenue over a given period based on all kinds of factors, like the competitive landscape, new product launches, and previous sales. Whether your forecast predicts “clear skies” ahead for the next fiscal year or a flood of new competition that threatens to shrink market share, it provides data that helps everyone in the organization prepare.

Here’s how sales forecasting works, why businesses need to forecast, and what tools can make it simpler.

What is sales forecasting?

Sales forecasting is a business practice that aims to predict all incoming revenue over a specific period, like a quarter or year. By forecasting likely sales, businesses can plan their activity — from how many people to hire to how many of its products to produce, and even whether it can afford big capital investments like opening a new distribution center.

Sales forecasting’s role in business strategy is to prepare for likely opportunities and inform resource planning. No important business decisions can be made without likely revenue to weigh them against — and that’s why a sales forecast is crucial for businesses of all sizes.

The benefits of sales forecasting for businesses

Sales forecasting takes time, energy, and investment, including considerable data collection. In some cases, it requires specialized tools and analysts. But the benefits are worth the effort:

• Better decision-making: Accurate forecasts let you know what next steps to take, and which to avoid. For example, if you think demand is going to be slow two quarters from now, you can direct your production facilities to make fewer products, preventing waste and overstocking.
• Optimized use of resources: If you forecast low sales in one target industry, you might move your sales team’s focus to another with better prospects. Sales forecasting helps you meet demand where it is.
• Accurate staffing: If your forecast predicts high demand, you may need to hire more salespeople. Dwindling demand might mean you need to reduce your workforce in certain areas. This could also apply to staff who provide after-sales support.
• Improved sales team morale: Sales forecasts help leaders set achievable quotas for sales reps. Quotas need to be high enough to motivate reps, but not so high that teams feel like management is asking for the impossible.
• Greater confidence and credibility: For private companies, meeting sales forecasts can give leaders confidence in the business’s ability to perform. In publicly traded companies, accurate sales forecasts drive investor confidence, which has a direct positive impact on the company’s share price.
• Maximized profits: At the end of the day, all these benefits net higher profits because opportunities get the resources they need and help you avoid issues like overstaffing and overproduction.

 

Common challenges in sales forecasting and how to overcome them

Korn Ferry found that fewer than 25% of sales organizations have a sales forecasting accuracy of 75% or greater. For something meant to provide guidance to an organization, that’s not great. Some of the following challenges might be to blame, but each can also be solved:

Data inaccuracy and reliability

Sales pipeline forecasting must be based on accurate data, but certain practices can hide the facts — such as pushing out a deal’s close date indefinitely or attributing a partner sale all to one rep. Businesses can overcome this with stronger data practices that use automated workflows to remove human error. For example, Slack’s Workflow Builder tool integrates with other third-party tools, like Salesforce, letting teams create automated workflows for everything from data collection to analysis and reporting.

Inaccessible or siloed data

Forecasts often rely on past sales performance data, but that data can be unaggregated and locked away in various channels. To avoid this, centralize all your sales data in a customer relationship management (CRM) platform. Then, integrate your CRM data with a work operating system like Slack. Here, you can use natural language searches to gather structured and unstructured data — such as from virtual meetings or conversation threads — and collaborate on forecasts with various stakeholders in channels.

Adapting to market volatility

When new legislation restrains your industry, new competition steals your customers, or a natural disaster destroys key parts of your supply chain, you must react right away. You can achieve this by applying a forecasting method that allows for real-time adaptability. By integrating your sales data with your work operating system, you can more easily access key data and collaborate in real time. Slack tools like huddles and canvases give teams ways to brainstorm new solutions on the fly, from wherever they are.

Accounting for varying sales performance

Anyone can have an off year — and if you’re making forecasts based on a stellar outlier performance, you may set yourself up for disappointment. Overcome this challenge by creating an even longer view that provides a range of targets your team can reasonably expect to hit. Stay on top of current performance to identify what’s working and what’s not. And regularly check in with managers, reps, and other stakeholders to make forecast adjustments. Slack makes it easy for sales teams to share information about forecasts, selling tactics, challenges, and opportunities — and to proactively collaborate and problem solve.

Inefficiency producing forecasts

Spreadsheets and manual analysis aren’t efficient anymore, and if forecasting is difficult, analysts are less likely to respond to changing conditions. It comes down to tools — and automated, multifactor forecasting software modernizes your efforts. Assistive AI agents can also help with things like drawing from unified customer data to surface relevant insights, provide action items, and help teams prepare for budget meetings.

7 sales forecasting methods explained

Forecasting methods largely fall into two types: quantitative and qualitative. Quantitative forecasting uses numbers and measurable data, while qualitative forecasting uses more subjective factors. In the world of sales forecasts, you might think that the numbers always win, but some factors — such as customer sentiment’s effect on sales — do not come across as clearly on paper.

Here’s a look at some of the most popular sales forecasting methodologies:

1. Historical sales forecasting: This is where you look at past data to predict future performance. A retail shop might compare holiday sales periods to predict this year’s holiday sales, while a SaaS business might look at the start of the fiscal year for its clients’ annual contract renewals. This type of forecast is strongest for its clear data-driven rationale, but it may not adapt well to major changes in and outside your company.
2. Intuitive sales forecasting: In this method, you interview your sales reps to collect their knowledge. For example, why are deals taking longer than usual? Why are cancellations up? Did leads spike after a recent campaign? This is best for most B2B industries, where salespeople have close, long-term relationships with customers. It can reveal insights raw numbers alone may not predict — but verifying information can take time and slow down the process.
3. Length of sales cycle forecasting: This data-driven method zeroes in on how long it takes for deals to close, compared over time. It assumes a flatline in deal size, so it’s best for industries with minimal customization, where volume is the key to success. A good example might be in commodities like materials suppliers. Outside of these, it might lack the nuance necessary to make good revenue predictions.
4. Multivariate sales forecasting: This method is considered the most complete, taking as many factors as possible into account and using predictive analytics to make sales forecasts. For example, an automotive company might use this to factor in weather conditions in countries where it procures parts, a decline in car sales across the industry, and the increase in its service business, which may indicate that customers are keeping their cars longer.
5. Opportunity stage forecasting: This method looks at the stages of the sales pipeline to see which potential deals are most likely to lead to sales and which are likely to stall out. This data can be compared to other indicators like the average length of a sales cycle to predict upcoming sales. For instance, a marketing agency may see deals sticking in the negotiating stage longer and predict a decline in sales over the next comparable period.
6. Pipeline forecasting: This method looks at the entire pipeline and compares it to other sales data. Its value and percentage likelihood of closing become the sales forecast for that next period. A nonprofit fundraising arm might use this to set a high benchmark for potential revenue and cap new hires at a certain level.
7. Revenue run-rate sales forecasting: This is the simplest sales forecasting method. It extends a current trend out for one year. For example, if your business made $250,000 in one quarter, the predicted run rate would be $1 million. This is a fast way to forecast and is technically based on data. But its weakness is faith that external conditions and internal sales performance will not drastically shift.

 

Step-by-step process for creating a sales forecast

Sales forecasts take time and effort to produce, but it’s mostly an ongoing, organization-wide practice. Leaders should review the steps below to set those practices in motion and make forecasting more straightforward for their organization.

Step 1: Collect historical sales data

Regardless of the method you use to analyze sales data, you need to start with as much of it as possible. Your CRM should automatically collect information on deals and customers, such as who buys what and how often, plus inventory data.

Teams that use Sales Cloud directly from Slack can access their accounts and data in one place. With automatic, real-time alerts, everyone can easily stay on top of deal movements, sales wins, and pipeline changes. This visibility helps sales leaders keep an eye on rep performance and identify opportunities for coaching and development to win more sales and increase forecast accuracy.

Step 2: Analyze sales trends and patterns

Dig into your data and ask important questions, such as:

• What patterns do you see?
• Do customers who buy one product tend to buy another at the same time?
• Has one subscription tier eaten into the customer base of another?
• Do harsh weather conditions tend to increase or decrease sales?

Noticing patterns and staying curious has the potential to put you ahead as a sales leader. Slack can elevate your sales leadership, helping you more easily dig into conversation insights and CRM data — and connect with individual reps and sales teams to ask questions and brainstorm solutions.

Step 3: Account for market and economic factors

It’s relatively simple to rely on your own sales data, but factoring in broader market effects can be touchy. Maybe you learn that your industry is contracting out work by 10%. That doesn’t necessarily mean you’ll experience a 10% cut in your sales, but it could. If you’re trying to boost partner sales, how will a shift to self-service affect your business? It can be difficult to quantify these things, but understanding where things could change can help your team prepare and adjust your sales forecast.

To prepare for shifts that could affect your business, focus on these areas:

• Changes in your industry, such as legislation or market size
• Changes in your customers’ industries, if you have a focus
• Events that could affect your supply chain, such as natural disasters
• Competitors closing, expanding, or making new offers

Step 4: Choose the right forecasting model

Dive deep into the forecasting methodologies above to decide which one — or combination of models — makes the most sense for:

• Your industry and company
• The resources and data you can access
• The level of accuracy you need
• When you plan to apply it, such as over a quarter or over a year

Step 5: Implement your forecast and monitor results

Using CRM data, automated workflows for data collection, and AI-powered analysis tools, teams can easily monitor, communicate, and act on their sales forecast from right within Slack. Once the specified period has ended, if sales fall within the expected range, that’s strong evidence for an effective model. If they don’t, you’ll need to investigate the cause. With the right automation and AI tools in place, this process becomes faster and easier, helping you act and adjust quickly for more accurate forecasts each time.

Develop a strong sales forecast process using the right tools

Accurate sales forecasting gives you the credibility and resources to run your business and execute plans. Slack and Salesforce provide teams with robust collaboration tools and greater visibility into key sales data and contextual conversation insights so they can make better forecasting decisions and adjust plans in real time. And with built-in automation and AI tools, forecasting becomes a much more accurate, manageable, and streamlined task.

Learn more about how Slack can help your sales team grow revenue and operate more efficiently.